Specified Life

Patient identifiers

2011-06-27T10:10:00.001-07:00

I have just posted an article on patient identifiers. Here is a short excerpt from the article:

Imagine this scenario. You show up for treatment in the hospital where you were born, and in which you have been seen for various ailments over the past three decades. One of the following events transpires:

1. The hospital has a medical record of someone with your name, but it's not you. After much effort, they find another medical record with your name. Once again, it's the wrong person. After much time and effort, you are told that the hospital has no record for you.

2. The hospital has your medical record. After a few minutes with your doctor, it becomes obvious to both of you that the record is missing a great deal of information, relating to tests and procedures done recently and in the distant past. Nobody can find these missing records. You ask your doctor whether your records may have been inserted into the electronic chart of another patient or of multiple patients. The doctor does not answer your question.

3. The hospital has your medical record, but after a few moments, it becomes obvious that the record includes a variety of tests done on patients other than yourself. Some of the other patients have your name. Others have a different name. Nobody seems to understand how these records got into your chart.

4. You are informed that the hospital has changed its hospital information system, and your old electronic records are no longer available. You are asked to answer a long list of questions concerning your medical history. Your answers will be added to your new medical chart. You can't answer any of the questions with much certainty.

5. You are told that your electronic record was transferred to the hospital information system of a large multi-hospital system. This occurred as a consequence of a complex acquisition and merger. The hospital in which you are seeking care has not yet been deployed within the information structure of the multi-hospital system and has no access to your record. You are assured that the record has not been lost and will be accessible within the decade.

6. You arrive at your hospital to find that it has been demolished and replaced by a shopping center. Your electronic records are gone forever.

These are the kinds of problems that arise when hospitals lack a proper patient identifier system (a common shortcoming). The purpose of the article is to list the features of a patient identifier system, emphasizing the essential role of identifiers in healthcare services and biomedical research.

The full-length article is available at:

http://www.julesberman.info/book/id_deid.htm

© 2011 Jules J. Berman

Post-Informatics Pathology

2011-03-31T04:07:00.000-07:00

For those who have been reading my blogs sequentially, I apologize for my lapse in the google ngram series. I've been preoccupied with other projects, but I hope to pick up where I left off, soon.

In the meantime, the Journal of Pathology Informatics has just published my article on "Post-Informatics Pathology." It is available at:

http://www.jpathinformatics.org/text.asp?2011/2/1/18/78499

- Jules Berman

Google ngram medical research 2

2011-01-03T03:38:00.000-08:00

In yesterday's blog, we began a series in which we'll discuss using Google's ngram data for medical research. We showed that with Google's ngram viewer, you can enter a word or phrase and find the frequency of occurrences of the phrase in books collected over the past half-millennium. The ngram viewer is intended to show us how particular words and phrases grow or wane in popularity.

There are now many websites that discuss the ngram viewer, but they all seem to be stuck in the realms of culture and literature; nobody seems to be using the ngram viewer for medical research [if this observation is incorrect, please send me a comment].

Words and phrases can tell us a lot about the patterns of disease. With the Google ngram collection, we can answer questions for which there is no other source of informative data [i.e., no historical data, and no existing collections of past observations or measurements]. We saw a few examples in yesterday's blog.

The drawback to Google's ngram user is that it produces one-off graphs from a single or small number of words and phrases, and performs a particular type of calculation (word/phrase occurrences as a percentage of total for a particular year).

When you're interested on analyzing a large dataset, you really want to do a global analysis over the data (i.e., analyzing the occurrences of every word or phrase, measured by all possible parameters, all at once). Then, when you start to mine the resulting data, you can look for any kind of trend, among any or all ways of grouping the data.

To understand the problem, let's look at two records in the Google dataset (provided in Google's ngram download page, .

circumvallate 1978 313 215 85
circumvallate 1979 183 147 77

The 1-gram "circumvallate" occurs 313 times in the 1978 literature, appearing on 215 pages, and in 85 books. In 1979, circumvallate occured 183 times, on 147 pages, in a total of 77 books. Depending on our question, we might be interested in the trends of word/phrases expressed as any of of these three parameters (total occurrences, page occurrences, book occurrences).

In the case of a medical term, we might be interested in combining the data for a word with all of its synonyms or plesionyms (near-synonyms). For example, we might want to sum the data for renal carcinoma, kidney cancer, renal ca, kidney ca, renal carcinoma, kidney carcinoma, carcinoma of the kidney, carcinoma of the kidneys, and so on.

Beyond the occurrence of near-synonymous terms, we might want to group classes of terms (e.g., all tumors, diseases spread by insects).

We might want to know the specific year that a term first came into use, or the specific year after which a term ceased to occur in the literature.

We might want to confine our attention to books that contain specific types of terms (e.g., names of diseases) and to produce a frequency calculation that excludes books that do not contain names of diseases.

We might want to look at the frequency order of terms or groups of terms in a particular publication year.

We might want to combine ngram data with relevant data included in other datasets.

All of these examples, and many more, cannot be accomplished by using Google's public ngram viewer.

The only way we can make any progress with these kinds of questions is to download the ngram data and write our own scripts to analyze the data.

In the next few blogs, I'll provide step-by-step instructions for acquiring, parsing, and analyzing the ngram data.

- © 2011 Jules Berman

key words: ngrams, Google ngram viewer, doublets, indexing, index, information retrieval, medical informatics, methods, translational research, data mining, datamining

Medical research with google ngrams

2011-01-02T07:14:00.000-08:00

This blog post marks the beginning of a series of articles on the general topic of indexing. Eventually, I'll get to standard back-of-book indexing, but I'm going to start with an advanced topic: ngram indexing.

Ngrams are the ordered word sequences in text.

If a text string is:

"Say hello to the cat"

The ngrams are:

say (1-gram or singlet or singleton)
hello (1-gram or singlet or singleton)
to (1-gram or singlet or singleton)
the (1-gram or singlet or singleton)
cat (1-gram or singlet or singleton)
say hello (2-gram or doublet)
hello to (2-gram or doublet)
to the (2-gram or doublet)
the cat (2-gram or doublet)
say hello to (3-gram or triplet)
hello to the (3-gram or triplet)
to the cat (3-gram or triplet)
say hello to the (4-gram or quadruplet)
hello to the cat (4-gram or quadruplet)
say hello to the cat (5-gram or quint or quintuplet)

Google has undertaken a massive effort to enumerate the ngrams collected from the scanned literature dating back to 1500. Moreover, Google has released the ngram files to the public.

The files are available for download at:

http://ngrams.googlelabs.com/datasets

We can use Google's own ngram viewer to do our own epidemiologic research.

When we look at the frequency of occurrence of the 2-gram "yellow fever" we get the following Google output.

Click on image for larger view

We see that the term "yellow fever" (a mosquito-transmitted hepatitis) appeared in the literature beginning about 1800 (the time of its largest peak), with several subsequent peaks (around 1915 and 1945). The dates of the three peaks correspond roughly to outbreaks of yellow fever in Philadelphia (1993, with thousands of deaths), the construction of the Panama canal (finished in 1914, after incurring over 5,000 deaths), and WWII Pacific outbreaks, countered by mass immunizations with a new, and unproven yellow fever vaccine. In this case, a simple review of n-gram "traffic" provides an accurate view of the yellow fever outbreaks.

Let's see the n-gram occurrence graph for "lung cancer".

Click on image for larger view

There is virtually no mention of lung cancer before the 20th century. Why? Because lung cancer was rare before the introduction of cigarettes. Here is what Wikipedia has to say about cigarette smoking through the twentieth century. "The widespread smoking of cigarettes in the Western world is largely a 20th century phenomenon – at the start of the century the per capita annual consumption in the USA was 54 cigarettes (with less than 0.5% of the population smoking more than 100 cigarettes per year)".

While lung cancer did not occur in great frequency until the twentieth century, gastric cancer has been around quite a while. In fact, the incidence of stomach cancer has been dropping in the last half of the twentieth century, [presumably due to refrigeration, other safe methods of food preservation, and the general availability of potable water in industrialized countries]. Here's the ngram graph for gastric cancer.

Click on image for larger view

Notice that the graph has about the same shape whether it's searching gastric cancer or stomach cancer or related synonyms. This tells us that the "traffic" for a medical term and its synonyms can provides similar trends (but with differing amplitudes allowing for usage).

Finally, let's look at my favorite subject in tumor biology, the precancers.

Click on image for larger view

Precancer terms have occurred with increasing frequency in the twentieth century (perhaps indicating the importance of this class of lesions).

Searching for medical ngrams, using Google's ngram viewer has some scientific merit. If we want to get the most out of the ngram files, we will need to do a global analysis of the ngram data related to medical terms. This means we will need to download the ngram data sets and write our own scripts that can analyze the occurrences of every term of interest, all at once, finding correlations of medical significance.

Jump to tomorrow's blog to continue this discussion.

- © 2011 Jules Berman

key words: ngrams, doublets, indexing, index, information retrieval, medical informatics, methods

Machiavelli's Laboratory: update available

2010-11-04T14:36:00.000-07:00

Machiavelli's Laboratory is a free ebook that I published on April 13, 2010. It is a satiric discourse on scientific ethics, from the perspective of an unethical scientist.

I just posted the latest version of the book at:

http://www.julesberman.info/integ/machfree.htm

Updated PDF and MobiPocket formats are also available at the site.

- Jules Berman

Extracting names from text file

2010-10-27T05:09:00.000-07:00

I'm beginning, for this blog, a series of short utility scripts and essays that relate, in one way or another, to the general subject of indexing and data retrieval.

The first entry is a short Perl script (just 18 command lines) that extracts the names (of people) wherever the names may occur within a provided text file. The output consists of an alphabetized list of non-repeating names. The script is so simple that it can be easily be translated into any language that supports regular expressions (regex).

The script is available at:

http://www.julesberman.info/factoids/namesget.htm

Blog readers who are uninterested in indexing and data retrieval may want to visit my two other blogs,

Machiavelli's Laboratory (scientific ethics taught from the perspective on an unethical scientist)

and

Neoplasms (essays on tumor biology)

- © 2010 Jules Berman

key words: indices, indexing, indexes, index, data retrieval, information retrieval, informatics

Germ cell tumor web page available

2010-10-14T05:12:00.001-07:00

The recent blog series on germ cell tumors has been packaged into a single web page available at:

http://www.julesberman.info/factoids/germcell.htm

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, tumor biology, tumour biology, carcinogenesis

New blog site on Neoplasms

2010-10-11T07:38:00.000-07:00

Readers who are primarily interested in medical informatics may have found the past week's blogs on germ cell tumors to be a bit off-track. I agree. I've created a new blog site, Neoplasms, to cover my interests in tumor biology.

Henceforth, for the Specified Life blog, I'll stick to issues of data retrieval, organization, indexing, annotation, and analysis.

For readers who have been following the germ cell series, you might be interested in a new blog series I'll be starting, on "epigenomic tumors", in the Neoplasms blog site. I'll be starting with a discussion of malignant rhabdoid tumors.

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, tumor biology, tumour biology, carcinogenesis

Germ cell cancers of testes: conclusion

2010-10-07T13:38:00.000-07:00

This is the last entry on a series of blogs on germ cell cancers of the testes.

I've added forward and backward links for each of the blogs in the series, so you can visit the first blog in the series and click forward or backward through the sequential entries.

Basically, in this series, we showed, using the SEER public use data files, that there has been a large increase in the incidence of germ cell cancers of the testis in white non-Hispanic males since the first SEER observation year (1973) up to the most recent data year (2007).

Along with the increase in seminomatous germ cell cancers was a lesser but parallel increase in the non-seminomatous germ cell cancers of the testis, when compared in birth cohort populations.

The seminomatous and non-seminomatous germ cell cancers, though derived from very different cell types (germ cells versus embryonic/extra-embryonic primitive cells) develop from the same precanceous lesion (usually intratubular germ cell neoplasia and sometimes gonadoblastoma). Precancerous germ cells are characterized by epigenomic erasure, and this "erased" state seems to allow precancerous germ cells to develop into seminomas or into tumors derived from totipotent stem cells.

Testicular precancers develop from disorders of sex development. The incidence of disorders of sex development, like the incidence of testicular germ cell cancers, has been rising. The cause for the rise of disorders of sex development (and the concomitant rise in testicular germ cell cancers) is unknown. However, the ubiquitous appearance of the platicizer and endocrine disruptor, Bisphenol A, has captured the interest of toxicologists and cancer researchers.

All of these issues were discussed in this completed series of blogs on testicular germ cell cancers.

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, germ cell tumor, germ cell tumour, tumor epidemiology, increasing germ cell cancer rates, germ cell cancer, seminomas, seminomatous, non-seminomatous, non-germinomatous, embryonal carcinoma, choriocarcinoma, testis, testes, itgcn, intratubular germ cell neoplasm, plasticizers, endocrine disruptors

Explaining the rise in testicular germ cell tumors

2010-10-06T05:31:00.000-07:00

In yesterday's blog we saw that the rise in testicular cancer rates in white males showed a parallel increase in seminomatous and non-seminomatous germ cell cancers among birth cohorts.

What can explain this increase?

There is one class of conditions that is overwhelmingly associated with the development of germ cell tumors of the testis: disorders of sex development of the testes.[1] Among the conditions within this general group are testicular dysgenesis, testicular feminization (insensitivity to androgens), and cryptorchidism. Disorders of sex development of the testis raise the incidence of intratubular germ cell neoplasia or of gonadoblastoma, both of which are testicular precancers.

As you might expect, along with the observed increase in testicular germ cell cancers in white males, there has been an observed increase in the incidence of disorders of sex development in the same population. [1,2] These disorders are characterized by a retardation in the maturation of primordial germ cells, along with an apparent mitotic over-stimulation of these same cells: leading to a proliferative, precancerous condition.

Though there is no proof at the moment, we might expect that males who develop testicular germ cell cancer who have clinically normal testes, may harbor small foci of [clinically unobserved] germ cell proliferative lesions.

What has caused the increased incidence of disorders of sex development in the testes? We don't know, but we have a candidate: the ubiquitous plasticizer and endocrine disruptor, Bisphenol A.

Bisphenol A is a synthetic estrogen used in the process of manufacturing plastics, and has been detected in the serum, milk, saliva, urine, and amniotic fluid of humans.[3] Because we get our daily dose of Bisphenol A from plastic bottles, one would expect that the levels of Bisphenol A in our blood would have increased steadily over the past several decades [coinciding with our increased dependence of plastic food and drink containers]. You might also expect that if Bisphenol A produced testicular cancers, you would see the largest increases in incidence among the wealthiest populations in the most industrialized nations [as we do].

Can we assume that Bisphenol A is causing the rise of incidence of testicular germ cell cancers? Absolutely not. All of the evidence, so far, is very weak (if it can be called evidence at all!). Still, nobody would suggest that Bisphenol A has much to recommend itself as a healthy addition to our diets. It seems prudent to try to limit our exposure to this compound when feasible, particularly among infants and pregnant women.

1. Pleskacova J, Hersmus R, Oosterhuis JW, Setyawati BA, Faradz SM, Cools M, Wolffenbuttel KP, Lebl J, Drop SL, Looijenga LH. Tumor Risk in Disorders of Sex Development. Sex Dev 4:259-269, 2010.

2. Skakkebaek NE, Rajpert-De Meyts E, Jorgensen N, Main KM, Leffers H, Andersson AM, Juul A, Jensen TK, Toppari J. Testicular cancer trends as 'whistle blowers' of testicular developmental problems in populations. Int J Androl 30:198-204, 2007.

3. Bouskine A, Nebout M, Brucker-Davis F, Benahmed M, Fenichel P. Low Doses of Bisphenol A Promote Human Seminoma Cell Proliferation by Activating PKA and PKG via a Membrane G-Protein-Coupled Estrogen Receptor. Environ Health Perspect 117:1053-1058, 2009.

This series of blogs has drawn heavily from the public use SEER data sets produced by the U.S. National Cancer Institute's Surveillance, Epidemiology and End Results project. In the next blog, I'll discuss how this data can be obtained and used by the science-minded public.

Jump to Tomorrow's blog

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, germ cell tumor, germ cell tumour, tumor epidemiology, increasing germ cell cancer rates, germ cell cancer, seminomas, seminomatous, non-seminomatous, non-germinomatous, embryonal carcinoma, choriocarcinoma, testis, testes, itgcn, intratubular germ cell neoplasm, plasticizers, endocrine disruptors

Explaining the germ cell cancer rates

2010-10-05T08:47:00.000-07:00

In yesterday's blog we explained how the precancer of testicular germ cell tumors, intratubular germ cell neoplasia, gives rise to seminomas (differentiated germinomatous lineage) and non-seminomas (tumors of pluripotent progenitor cells that are not of germ cell lineage).

In the first blog of this series on germ cell tumors , we noted that the increase in occurrences of seminomas has outpaced the occurrences of the nonseminomatous germ cell tumors.

Here is a graph, produced from the SEER public use data sets, of the crude occurrences of seminoma and non-seminoma testicular germ cell tumors, in white males, since 1973.

The light blue bars are the seminomas, and the maroon bars are the non-seminomatous germ cell tumors of the testes. Since 1973, the seminomas increased from a number much lower than the occurrences of the non-seminomatous germ cell tumors; exceeding them in 1977. Since 1977, the crude occurrences of seminomas has greatly outpaced the occurrences of the non-seminomatous germ cell tumors of testes in white males.

Why? If both types of tumors are coming from the same precancer, why are their trends of occurrence non-parallel?

Well, there are several possible answers. It is possible that some external influence has modified the step in the progression of precancer to cancer, to favor the occurrence of seminomas.

However, it is also possible that their increases in occurrence are indeed parallel, and we're just not seeing it in our graph. Bray et al have looked at the incidence of testicular seminoma and non-seminoma germ cell tumors, by cohort (i.e., year of birth), not by year of occurrence.[1]

Bray F, Richiardi L, Ekbom A, Forman D,Pukkala E, Cuninkova M, Moller H. Do Testicular Seminoma and Nonseminoma Share the Same Etiology? Evidence from an Age-Period-Cohort Analysis of Incidence Trends in Eight European Countries. Cancer Epidemiol Biomarkers Prev 15:652–658, 2006.

When the comparisons are based on cohort (comparing incidence for people born the same year), most of the differences vanish [between the incidence of seminomas and non-seminomatous germ cell tumors].

When do we see a birth corhort effect on tumor incidence? For cancers, a cohort effect is best observed when individuals born in one year are exposed (as a population group) to a causal agent that is different from the exposure of individuals born in other years. The cancers that result may occur at many different ages, thus erasing the cohort effect when the data is stratified by year of occurrence (as we had done the graph above). Only when you look at the birth cohort will you find a trend that may relate to a carcinogenic exposure.

OK. The birth cohort data reported by Bray et al would seem to indicate that some generational effect is acting on succeeding cohorts to produce a shared increase in the incidence of all testicular germ cell cancers. Furthermore, whatever is causing the generational effect is likely to be of short duration or differ significantly from year to year. Why is that? If the exposure of a carcinogen were of long duration or were the same from year to year, then every cohort would be exposed similarly, and there would be no birth-year specific effect.

So, now the mystery is: What are the conditions and carcinogens that might cause testicular germ cell tumors, which have changed, year-by-year, to produce the observed rise in these cancers in white males? This will be the topic of the next blog.

Jump to Tomorrow's Blog

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, germ cell tumor, germ cell tumour, tumor epidemiology, increasing germ cell cancer rates, germ cell cancer, seminomas, seminomatous, non-seminomatous, non-germinomatous, embryonal carcinoma, choriocarcinoma, testis, testes, itgcn, intratubular germ cell neoplasm

Germ cell precancers and epigenetic erasure

2010-10-03T16:21:00.000-07:00

In yesterday's blog , we left off with a question: "How it is possible for intratubular germ cell neoplasia to be the precursor for both germinomatous germ cell cancers (i.e., seminoma) and non-germinomatous germ cell cancers (e.g. embryonal carcinoma, choriocarcinoma)?

The key is epigenetic erasure, a phenomenon unique to germ cells.

As an organism develops, cells specialize into about 200 differentiated cell types. All these different types of cells have the same genetic sequence (genome). Cell types are distingued, one from the other, by epigenetic modifications. Epigenetic modifications to genes involve base methylation, conformational changes in chromosomes, protein modifications... anything other than changes in DNA sequence.

Germ cells, like all other differentiated cells, have epigenetic modifications. The unique thing about germ cells is that they must undergo epigenetic erasure prior to the production of gametes; otherwise the gametes would be imprinted with the epigenetic modifications characteristic of the parent organism and would not be capable of recombining during fertilization to produce a fully de-differentiated, totipotent product.[1]

The cells of intratubular germ cell neoplasia (the precancer of most male germ cell tumors) and of seminomas, are all characterized by DNA hypomethylation; not so for the cells of non-germinomatous germ cell tumors.[2,3] DNA Hypomethylation is seen in epigenomic erasure [of germ cells].

"Erased" germ cells are capable of developing into totipotent embryonic cells.[4] It would seem that a plausible mechanism for the development of non-germinomatous germ cell cancers from a germ cell precursor (intratubular germ cell neoplasia, itgcn) is that the "erased" itgcn cells, during cancer development, transform into totipotent cells, capable of differentiating into cells from any embryonic layer (e.g., embryonal carcinoma), or into extra-embryonic tissue (e.g., choriocarcinoma).

This explains why the itgcn, the germ cell precancer, can give rise to both germinomatous (erased) and non-germinomatous (epigenetic-modified) cancers.

There is only one mystery left to solve (the original mystery that we started with, about 4 blog entries back ). If germinomatous and non-germinomatous germ cell cancers both arise from the same precursor, why is there a much greater increase in the rate of occurrence of seminomas compared with the rate of occurrence of non-germinomatous cancers, since 1973?

1. Allegrucci C, Thurston A, Lucas E, Young L. Epigenetics and the germline. Reproduction 129:137-149, 2005.

2. Netto GJ et al.Global DNA hypomethylation in intratubular germ cell neoplasia and seminoma, but not in nonseminomatous male germ cell tumors. Modern Pathology 21: 1337-1344, 2008.

3. Lind GE, Skotheim RI, Lothe RA. The epigenome of testicular germ cell tumors. APMIS (Acta Pathologica, Microbiologica et Immunologica Scandinavica) 115:1147-1160, 2007.

4. Turnpenny L. Derivation of human embryonic germ cells: an alternative source of pluripotent stem cells. Stem Cells 21:598-609, 2003.

Jump to Tomorrow's Blog

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, germ cell tumor, germ cell tumour, tumor epidemiology, increasing germ cell cancer rates, germ cell cancer, seminomas, seminomatous, non-seminomatous, non-germinomatous, embryonal carcinoma, choriocarcinoma, testis, testes, itgcn, intratubular germ cell neoplasm

Germ cell tumor common precancer: itgcn

2010-10-03T07:32:00.000-07:00

In yesterday's blog , we discussed the terminology problem with the germ cell tumors. Basically, if you have a sub-class of germ cell tumors that are called "non-germinomatous germ cell tumors," isn't that a contradiction in terms? Isn't it like saying that dehydrated water is a subclass of water?

The answer is simple: the classic germ cell tumor of the testes (seminoma), as well as most of the malignant non-germinomatous germ cell tumors of the testes, arise from the same precancer: intratubular germ cell neoplasia (itgcn). Because itgcn is composed of dysplastic (early neoplastic) germ cells, both the germinomatous and non-germinomatous tumors have a germ cell origin.

You can easily appreciate the morphologic similarity between itgcn and seminoma by looking at a histologic preparation of each.

Image of Intratubular Germ Cell neoplasia
Distributed by Wikimedia
under a Creative Commons License

The germ cell precancer, itgcn, is a collection of atypical gonocytic cells lining seminiferous tubules in the testis.

Image of Seminoma
Distributed by Wikimedia
under a GNU License

Seminoma cells closely resemble the cells of itgcn, from which they derive (with the rare exception of the so-called spermatocytic seminoma, which behaves unlike the other types of seminomas).

The same precancer (itgcn) precedes the development of most of the invasive non-germinomatous germ cell tumors of the testis.

So, the terminologic mystery is solved. The germinomatous and the non-germinomatous germ cell tumors are classified together because most of them are derived from neoplastic intratubular germ cells (i.e., intratubular germ cell neoplasia).

But solving the terminologic mystery does not help us understand the biology of what's happening. Why does itgcn give rise to tumors of germ cells (e.g., seminomas) and to tumors of primimitive non-germ cells (e.g., embryonal carcinoma, choriocarcinoma)? How can a tumor be derived from cells that have a committed lineage (i.e., sperm cells in the case of males) that is completely unrelated to the lineages found in the tumor?

There's an answer. It has a lot to do with a phenomenon unique to germ cells called epigenomic erasure. This will be the topic of the next blog in our series on germ cell tumors.

Jump to Tomorrow's Blog

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, germ cell tumor, germ cell tumour, tumor epidemiology, increasing germ cell cancer rates, germ cell cancer, seminomas, seminomatous, non-seminomatous, non-germinomatous, embryonal carcinoma, choriocarcinoma, testis, testes, itgcn, intratubular germ cell neoplasm

Germ cell tumors: definition problems

2010-10-02T07:47:00.000-07:00

OK, getting back to the prior post on germ cell tumors, we found that the rate of occurrence of seminomatous germ cell tumors of the testes has been greatly increasing, in the white male population, since (at least) 1973. During the same period, the rate of occurrence of the other type of germ cell tumors (non-seminomatous) has hardly increased at all, for white men.

Why has the rate of occurrence of seminomas increased since 1973, in the white male population? Also, if seminomatous and non-seminomatous germ cell tumors are just morphologic variants of the same basic tumor (i.e., germ cell tumor), why wouldn't they both increase to the same extent?

Perhaps some of the problem relates to the definition of these two tumors.

Seminomas are tumors of gonocytes, a differentiated cell committed to producing gametes (sperm in males, eggs in females), or a committed progenitor cell of gamete-producing cells (i.e., an ancestral cell of a gamete-producing cell). Since seminomas are considered the neoplastic equivalent of gonocytes, there seems to be little leeway in their classification: they must be included among the germ cell tumors.

But what about the other type of germ cell tumors. This other type is known by two different names that tell us a lot about the ambivalent nature of the tumor:

From wikipedia:

"The nongerminomatous or nonseminomatous germ cell tumors (NGGCT, NSGCT) include all other germ cell tumors, pure and mixed."

How can a germ cell tumor be non-germinomatous? Wouldn't the adjective "non-germinomatous" pretty much tell you that the tumor can't be a germ cell tumor?

It reminds me of one of my favorite limericks.

As I was sitting in my chair,
I sensed the bottom was not there.
Nor legs, nor back,
But I just sat,
Ignoring little things like that.

- Anonymous

Well, what are the non-germinomatous germ cell tumors? These are tumors that usually arise in the gonads and are composed of primitive pluripotent cells. We can find pure or mixed populations of embryonal carcinoma, teratomatous tissue, and choriocarcinoma in the non-germinomatous germ cell tumors. These are the same cells that are found in the very earliest embryo and placenta. But these primitive cell types are not gonocytes (i.e., they are not differentiated cells committed to producing sperm or eggs). These tumors are composed of primitive non-germ cells.

So why are the primitive non-germ cell tumors included among the germ cell tumors?

The answer to this question comes from our understanding of the common precancer of most of the seminomatous and non-seminomatous germ cell tumors: intratubular germ cell neoplasia.

In the next several blogs, we'll discuss germ cell precancer, and we'll explain the unifying concept of germ cell neoplasia. We'll also see how a study of precancers is crucial to our understanding of neoplasia, in general.

Jump to Tomorrow's Blog

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, germ cell tumor, germ cell tumour, tumor epidemiology, increasing germ cell cancer rates, germ cell cancer, seminomas, seminomatous

Povray script for book cover

2010-10-01T15:48:00.001-07:00

Currently, I'm writing a series of blogs on germ cell tumors. But I took a break today to prepare a web page that provides the PovRay script for the cover image of my new book, Methods in Medical Informatics, Fundamental of Healthcare Programming in Perl, Python, and Ruby.

The PovRay script and a high-resoluion version of the image used in the book cover, are available at:

http://www.julesberman.info/book/coverhow.htm

I'll resume the germ cell tumor series soon.

- Jules Berman

Non-seminomatous germ cell tumors

2010-09-27T12:00:00.000-07:00

In yesterday's blog, we examined the enormous increase in the incidence of seminomatous germ cell tumors occurring in white non-Hispanic males.

There are two categories of germ cell tumors: seminomatous and non-seminomatous.

The seminomatous tumors are tumors composed predominantly of a single cell type, the gonocyte. The non-neoplastic gonocyte would normally produce sperm cell in the testis. Seminomas are permitted to contain a few neoplastic trophoblasts, but otherwise, seminomas are composed of a population of large, round, monomorphic cells.

The other type of germ cell tumors is the non-seminomatous tumors, and these tumors are composed of malignant cells resembling those of the pluripotent primitive embryonic (from the early embryo) or extra-embryonic (from the placenta) malignant cells. Consequently, the non-seminomatous germ cell tumors may be teratomatous, primative embryonic, choriocarcinomatous, or some mixture of these. Non-seminomatous germ cell tumors may even contain foci of seminoma! The key characteristic of non-seminomatous germ cell tumors of the testis is that they must have a component of primitive neoplastic cells that are not seminoma cells.

Can we observe the same increased incidence of non-seminomatous germ cell tumors as we saw (yesterday) in the seminomatous germ cell tumors.

NO. Here are the numbers, computed from the SEER (the U.S. National Cancer Institute's Surveillance Epidemiology and End Results) public use data files. The first column is the crude number of occurrences of non-seminomatous germ cell tumors of testes in white, non-Hispanic males. The second column is the number of occurrences expressed as a proportion of all of the seer cases for the year examined, and the third column is the number of occurrences expressed as a proportion of the U.S. population for the year examined.


       crude   of SEER  of U.S. Pop
1973   000109   000196   000051
1974   000147   000218   000068
1975   000157   000213   000072
1976   000165   000218   000075
1977   000189   000246   000085
1978   000167   000214   000075
1979   000182   000226   000080
1980   000216   000260   000095
1981   000222   000259   000096
1982   000203   000234   000087
1983   000226   000251   000096
1984   000219   000234   000092
1985   000238   000243   000100
1986   000233   000232   000097
1987   000253   000238   000104
1988   000222   000206   000090
1989   000263   000238   000106
1990   000243   000209   000097
1991   000237   000192   000094
1992   000237   000185   000092
1993   000245   000194   000095
1994   000222   000176   000085
1995   000216   000169   000082
1996   000247   000202   000093
1997   000225   000178   000084
1998   000234   000180   000086
1999   000245   000185   000089
2000   000237   000177   000084
2001   000223   000162   000078
2002   000258   000185   000089
2003   000230   000166   000079
2004   000278   000192   000094
2005   000275   000188   000092
2006   000251   000169   000084
2007   000277   000182   000091

Here's the graph. The blue columns are the crude occurrences. The maroon columns are the numbers as a proportion of the year's seer records, and the white column are the numbers as a porportion of the U.S. population in the examined year.

There's a small increase since 1973, but much of the increase is accounted for by the increase in the SEER population and the increase in the U.S. population for the same years. The relative (population adjusted) rate of occurrence of non-seminomatous germ cell tumors has not increased by much; certainly nothing like the increase seen yesterday, for the seminomatous germ cell tumors.

What about the germ cell tumors that occur outside the gonads? Are they increasing in occurrence since 1973? Though germ cell tumors can occur outside the gonads, they are very rare. Here are the SEER numbers for non-seminomatous non-testicular germ cell tumors in white non-Hispanic males.


       crude   of SEER  of U.S. Pop
1973   000011   000019   000005
1974   000008   000011   000003
1975   000014   000019   000006
1976   000011   000014   000005
1977   000011   000014   000004
1978   000011   000014   000004
1979   000019   000023   000008
1980   000021   000025   000009
1981   000016   000018   000006
1982   000019   000021   000008
1983   000018   000020   000007
1984   000012   000012   000005
1985   000019   000019   000007
1986   000014   000013   000005
1987   000025   000023   000010
1988   000016   000014   000006
1989   000020   000018   000008
1990   000011   000009   000004
1991   000023   000018   000009
1992   000011   000008   000004
1993   000018   000014   000006
1994   000012   000009   000004
1995   000010   000007   000003
1996   000009   000007   000003
1997   000018   000014   000006
1998   000010   000007   000003
1999   000013   000009   000004
2000   000005   000003   000001
2001   000015   000010   000005
2002   000013   000009   000004
2003   000010   000007   000003
2004   000017   000011   000005
2005   000020   000013   000006
2006   000011   000007   000003
2007   000012   000007   000003

Here are the numbers for seminomatous non-testicular germ cell tumors in white non-Hispanic males.


       crude   of SEER  of U.S. Pop
1973   000002   000003   000000
1974   000001   000001   000000
1975   000005   000006   000002
1976   000004   000005   000001
1977   000010   000013   000004
1978   000008   000010   000003
1979   000004   000004   000001
1980   000014   000016   000006
1981   000016   000018   000006
1982   000011   000012   000004
1983   000011   000012   000004
1984   000011   000011   000004
1985   000013   000013   000005
1986   000016   000015   000006
1987   000014   000013   000005
1988   000011   000010   000004
1989   000016   000014   000006
1990   000010   000008   000004
1991   000011   000008   000004
1992   000014   000010   000005
1993   000013   000010   000005
1994   000023   000018   000008
1995   000017   000013   000006
1996   000020   000016   000007
1997   000018   000014   000006
1998   000010   000007   000003
1999   000009   000006   000003
2000   000016   000012   000005
2001   000014   000010   000004
2002   000017   000012   000005
2003   000016   000011   000005
2004   000022   000015   000007
2005   000026   000017   000008
2006   000014   000009   000004
2007   000017   000011   000005

Non-testicular germ cell tumors represent a tiny fraction of the germ cell tumors occurring in men. For the purposes of analysis, there's not much you can do with these tumors. They're not going to give you statistically significant results when you try to test a hypothesis; some of them may represent misdiagnoses (e.g., colon cancer mistaken for monomorphic teratoma in a peri-testicular appendage), or a conservative topographic assignment (peri-testicular metastasis from a regressed primary germ cell tumor).

So, for the purposes of this blog, where we're trying to find a biological explanation for the rise in seminomas in non-Hispanic white males, we'll ignore the non-testicular germ cell tumors.

Jump to Tomorrow's Blog

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, germ cell tumor, germ cell tumour, tumor epidemiology, increasing germ cell cancer rates, germ cell cancer, seminomas, seminomatous

Germ cell tumors: the problems

2010-09-27T05:40:00.000-07:00

Germ cell tumors are very rare neoplasms that occur most often in young adults and children.

For a variety of reasons, which I'll try to explain in the next few blog posts, much of what we think we understand about these tumors is highly confusing and probably wrong.

Considering that these are rare tumors, you might accept a certain degree of ignorance, but sometimes the mysteries that surround rare tumors must be solved before we can make any headway understanding the more common tumors.

Also, for some strange reason, the incidence of seminomatous germ cell tumors of the testes, in the white population, has been increasing over the past 35 years (at least).

Here are the numbers, computed from the SEER (the U.S. National Cancer Institute's Surveillance Epidemiology and End Results) public use data files. The first column is the crude number of occurrences of seminomatous germ cell tumors of testes in white, non-hispanic males. The second column is the number of occurrences expressed as a proportion of all of the seer cases for the year examined, and the third column is the number of occurrences expressed as a proportion of the U.S. population for the year examined.


       crude   of SEER  of U.S. Pop
1973   000036   000064   000016
1974   000026   000038   000012
1975   000044   000059   000020
1976   000069   000091   000031
1977   000197   000257   000089
1978   000169   000216   000075
1979   000192   000239   000085
1980   000225   000271   000099
1981   000200   000234   000087
1982   000240   000277   000103
1983   000257   000286   000109
1984   000252   000270   000106
1985   000256   000262   000107
1986   000293   000292   000122
1987   000302   000285   000124
1988   000299   000278   000122
1989   000343   000311   000138
1990   000338   000290   000135
1991   000303   000245   000120
1992   000352   000274   000138
1993   000340   000269   000131
1994   000385   000305   000147
1995   000303   000237   000115
1996   000371   000304   000139
1997   000379   000300   000141
1998   000408   000315   000150
1999   000363   000274   000133
2000   000413   000310   000146
2001   000409   000297   000143
2002   000398   000285   000138
2003   000371   000268   000127
2004   000400   000277   000136
2005   000382   000262   000129
2006   000374   000252   000125
2007   000378   000249   000125

Here's the graph. The blue columns are the crude numbers. The maroon columns are the numbers as a proportion of the year's seer records, and the white column are the numbers as a porportion of the U.S. population in the examined year.

When the incidence of a tumor increases almost every year, and we're clueless to explain the increase, it's probably worth thinking about the problem.

Jump to Tomorrow's Blog

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, tumor development, tumour development, germ cell tumor, germ cell tumour, tumor epidemiology, increasing germ cell cancer rates, germ cell cancer, seminomas, seminomatous

Tumor speciation revisited

2010-09-26T14:51:00.000-07:00

"Things that are new are wont to be set forward rudely and formlessly, and then must be polished and perfected in succeeding centuries."
- Pappus, a Helenistic mathematician (circa 350-300 B.C.E.)

Regular readers of this blog all know that I have a keen interest in the tumor speciation (why we encounter a the set of tumor types that are familiar to all pathologists, and no others). Some examples of different types of tumors are: follicular lymphomas, glioblastomas, oligodendrogliomas, seminomas, hepatocellular carcinomas, etc.)

I find the question of tumor speciation to be profound for the following reason: Research in the genetics of tumors has found that tumors are incredibly complex, with some tumors having thousands of genetic mutations, making each tumor unique from every other tumor that has ever occurred in humans. If every tumor is unique, and if many tumors are genetically complex (many mutations) and internally heterogeneous (a tumor cell may be genetically separable from another tumor cell from the same tumor), then why are there only a finite number of different kinds of tumors? Shouldn't there be a near-infinite number of tumor types?

In a prior post, I tried to answer this question, drawing an analogy from animal speciation, and though the argument seems valid, I can see how it might confuse readers. I spent much of a chapter in my Neoplasms book explaining tumor speciation, but I can't help but wonder if there's a shorter explanation.

Here's my third try:

Basically, cancer is caused by alterations in the genome. Tumor speciation is restricted to a relatively small set of patterns in the epigenome.

That's it! Here's the explanation of what it means and why it makes sense.

When oncogenic mutations occur in cells, a malignant phenotype can only arise in cells that have a specific type of differentiation. The type of differentiation that a cell manifests is determined by the epigenome (the non-sequence modifications to DNA). There are about 200 different cell types in the body. Each cell type within an individual animal has the same exact genome (DNA sequence) as every other cell type in the same animal. Neutrophils, enterocytes, neurons, thyroid cells differ from one another because of differences in their epigenomes.

Because we only observe about 200 different cell types in the body, it's likely that only a finite set of epigenomic patterns "work"; i.e., sustain cell viability. In the case of cancer, the cancer genotype can only manifest itself within a finite set of epigenomic patterns. Specific types of genetic alterations are found in specific types of epigenomic patterns (e.g., the bcr/abl mutation is seen in myeloid lineage cells). Even when cancer mutations become complex, their malignant phenotype can only occur in a restricted epigenomic background.

What happens during the process of carcinogenesis (the period following a carcinogenic mutation and leading to the emergence of an invasive cancer, often years later)? Maybe carcinogenesis requires epigenomic accommodation of the cancer genotype. Over multiple cell generations, the epigenome continuously changes until a stable epigenomic pattern is selected. Because most epigenomic patterns are not viable, most cancer mutations never lead to the emergence of a tumor.

This argument hardly constitutes proof of anything, but in my Neoplasms book, I provide many examples of how tumors develop within the constraints of allowable epigenomic patterns (i.e., the observed differentiated cell types).

Jump to the next Specified Life blog

- © 2010 Jules Berman

key words: carcinogenesis, neoplasia, neoplasms, oncogenes, cancer development, epigenome, genome, cancer diversity, cancer phenotype, cancer genotype, cancer epigenotype, tumor development, tumour development

Melanoma and the precancer time machine

2010-09-24T05:45:00.000-07:00

In an earlier post, I explained the precancer time machine phenomenon. Basically, when you successfully treat precancers, you don't see an immediate drop in the incidence of invasive cancers; you see a drop in invasive cancers at some point in the future, corresponding to the time at which the treated precancers would have developed into invasive cancers.

In the earlier post, I demonstrated that the precancer time machine seemed to apply in the case of dcis and invasive breast cancer.

In today's post, I've used the latest SEER (The U.S. National Cancer Institute's Surveillance Epidemiology and End Results) cancer data to show that there seems to be the same phenomenon going on for melanoma precancers.

The topic of treating melanoma precancers has been somewhat controversial. It would seem to be a no-brainer that we can reduce the incidence of invasive melanomas by treating melanoma precancers (dysplastic nevi and in situ melanomas). Unfortunately, there is no epidemilogiic evidence to support this assertion. Basically, the incidence of invasive melanoma seems to be rising every year, despite our best efforts to stem the tide (through the use of sunscreens, avoiding exposure to strong sunlight, and treating precancers).

The latest SEER data (which covers cancer cases from 1973 to 2007) seems to offer some hope that conditions might be improving.

Here's the graph of invasive melanome incidence in the U.S. SEER population.

The tallest bars (blue) are the crude numbers of occurrences of invasive malignant melanoma. The middle bars (maroon) are the occurrences of invasive melanoma expressed as a proportion of the total number of SEER cases. The bottom bars (white), are the occurrences of melanoma expressed as a proprotion of the total population of the U.S. in the perspective years.

Here are the numbers:


       crude   of SEER  of U.S. Pop
1973   001062   001916   000501
1974   001305   001938   000610
1975   001559   002116   000721
1976   001601   002116   000734
1977   001791   002337   000813
1978   001829   002347   000821
1979   001973   002460   000876
1980   002190   002645   000963
1981   002298   002690   001001
1982   002352   002714   001015
1983   002350   002617   001005
1984   002462   002638   001043
1985   002795   002864   001174
1986   002947   002942   001227
1987   003050   002879   001258
1988   002948   002743   001205
1989   003194   002896   001294
1990   003272   002816   001311
1991   003518   002853   001395
1992   003569   002786   001399
1993   003611   002860   001400
1994   003904   003097   001499
1995   004189   003283   001594
1996   004438   003641   001673
1997   004627   003668   001728
1998   004739   003663   001753
1999   004893   003704   001794
2000   005105   003832   001814
2001   005380   003911   001886
2002   005377   003857   001867
2003   005514   003993   001898
2004   005859   004062   001998
2005   006451   004431   002180
2006   006431   004335   002152
2007   006325   004172   002097

In the last two years studied (2006, 2007), the incidence of invasive malignant melanoma has dropped. Is this just a fluke, or does it indicate a real trend? There's no way to be sure, but inspection of the graph would indicate that it's the first time since 1973 when incidence has dropped two years running.

What about the corresponding incidence of in situ melanoma (the non-invasive precursor for invasive melanoma)?

Here's the graph for in situ melanoma.

Here are the numbers.


       crude   of SEER  of U.S. Pop
1973   000035   000063   000016
1974   000059   000087   000027
1975   000081   000109   000037
1976   000096   000126   000044
1977   000155   000202   000070
1978   000177   000227   000079
1979   000177   000220   000078
1980   000263   000317   000115
1981   000276   000323   000120
1982   000326   000376   000140
1983   000384   000427   000164
1984   000466   000499   000197
1985   000638   000653   000268
1986   000723   000721   000301
1987   000782   000738   000322
1988   000824   000766   000337
1989   000976   000885   000395
1990   001127   000970   000451
1991   001192   000966   000472
1992   001373   001072   000538
1993   001375   001089   000533
1994   001599   001268   000614
1995   001920   001504   000730
1996   002091   001715   000788
1997   002258   001790   000843
1998   002533   001957   000937
1999   002765   002093   001013
2000   003202   002404   001137
2001   003508   002550   001230
2002   003656   002622   001269
2003   003429   002483   001180
2004   003604   002499   001229
2005   004060   002788   001372
2006   004048   002728   001354
2007   004291   002831   001422

The incidence of in situ melanoma keeps going up and up. That's as it should be. When we successfully cure more and more in situ melanomas, we reduce the incidence of invasive melanomas.

The lag between the rise in incidence of the in situ lesions and the drop in incidence of the invasive lesions is due to the precancer time machine phenomenon.

Can we be sure? Not yet. Hopefully, over the next five years or so, the data will become a little more convincing.

- © 2010 Jules Berman

key words: precancer, precancerous, skin cancer, dysplastic nevi, dysplastic nevus, dysplastic naevus, dysplastic naevi, cancer mortality, cancer prevention, carcinogenesis

Perl script for moving files

2010-09-23T07:15:00.000-07:00

This morning I prepared a web page containing a Perl script that provides more functionality for file copying than the current (quite limited) DOS utility that is bundled with Windows operating systems. Though there are many file-copy utilities available, this one is quite simple, and you can tweak the code to best suit your own purposes.

This script transfers files from one subdirectory to another. When it copies a file from a source subdirectory to a destination subdirectory, it deletes the file on the source subdirectory. It prompts the user for the pathname of the source and destination subdirectories, and it permits the user to enter a Regex expression describing the file types that should be transferred. If you bypass the prompt asking for a Regex expression, it will transfer all types of files.

http://www.julesberman.info/book/dirtrade.htm

-Jules Berman

Methods in Medical Informatics

2010-09-22T13:18:00.000-07:00

My new book, Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python,and Ruby, was published yesterday by CRC Press.

I've prepared web pages with a book description and the Table of Contents for anyone interested.

- Jules Berman

Naming rocks, minerals, and gems

2010-09-15T10:03:00.000-07:00

Rocks, minerals, and gems have a rich vocabulary. There seems to be only one naming rule: no uppercase letters. This might be intended to simplify the nomenclature, but it can be confusing when you encounter a name such as "childrenite." You assume that name was inspired by a child, but the name comes from an adult; J.G. Children. The practice of using lowercase for eponyms is different from anatomic nomenclatures, wherein capitalization is preserved (e.g., Eustachian tube, not eustachian tube, after Bartolomeo Eustachio).

Similarly, you would think that "greenockite" must be a green rock (it's most often yellow, not green). It gets its name from Lord Greenock.

Likewise, biotite does not derive from a biologic precursor. It's named after Jean Biot.

Contrarily, rocks are sometimes named for lowercase (common) nouns.

Sepiolite is named for the cuttlefish bone, sepia.

Serpentine is named after the snake.

Where rocks get names from people, it's usually the surname. But not always.

Torbernite is named for Torbern Olaf Bergmann (the given name).

But don't get carried away. Bruceite was not named after someone whose given name was Bruce. It was named for a surname (Archibald Bruce). Likewise, Vivianite was not named after a woman named Vivian. It also came from the surname: J.G. Vivian

Perhaps the final solution for naming rocks after mineralogists was solved with the naming of frankhawthorneite after Professor Frank Hawthorne, of the University of Manitoba.

Here are a few more surprises:

If a gem is given a name, you'd think that it must be distinguishable from other gems with different names. No. Sapphire and ruby are the same gem, with different colorations (due to impurities in the stone). They're color-variants of corundum. Similarly, amethyst is just a color variant (purple) of common quartz.

Color in a mineral's name can be highly misleading. Glaucodot (greek for "blue"), is a gray to white mineral; never blue. Glaucodot is, however, used in the manufacture of blue glass, but you'd never know that by looking at the mineral.

Some rocks are named after the place where it was discovered or mined.

For example bytownite is named for Bytown, the former name for what is now called Ottawa.

This can be confusing, as franklinite is not named for Ben Franklin. It's named for Franklin, New Jersey. The city was named for Ben Franklin, but not the rock.

Consider Trona, California, where trona (sodium bicarbonate, and variously called tron) is mined. The mineral was not named for the city. The city was named for the mineral, which took it's name from tron, a shortened form of natron, the Arabic word for sodium.

Some rocks are named for their taste:

Calomel (probably from ancient Greek, meaning honey-taste)

or odor:

Scorodite (garlic-like, in Greek)

Some rocks are named after their included elements.

Bismuthinite contains bismuth, as you would expect. Zincote (a zinc dispersion) contains zinc, as does zincite (a true mineral).

But

Zinkenite contains no zinc (named after JKL Zinken, a German mineralogist).

Similarly, selenite, a clear crystal form of gypsum, contains no selenium.

If you're interested in the names of rocks, you must really know your Latin. Septarian concretions have complex internal structures, with multiple branches. You might think that the term comes from the latin septem (seven) referring to the number of branches. You'd be wrong. The name comes from the latin septum (partition).

It's also good to know your mythology. Pollucite was named for Pollux, the twin of Castor. The name has a certain inevitability. Pollucite is often found alongside petalite, previously known as castorite.

Some rocks are named for their geometry. There's tetrahedrite (tetrahedral crystals), triplite, clinoclase, microcline, and anorthoclase. But you can never generalize in mineralogy. Anglesite is not named for the angles in the crystal. It's named for Anglesey, Wales, where it is mined.

Sometimes, the relationship between a rock and it's name can be the opposite of what you might imagine. Fluorite is a rock that fluoresces. You might imagine that it was named because it had the property of fluorescence. Wrong. Fluorite was named for fluorine, from which it is composed (CaF2). The mineral fluorite was found to change color under UV light. The phenomenon was called fluorescence, after the first mineral shown to produce the effect. Today, every mineral that changes it's emission color under UV light is said to be fluorescent, whether it contains fluorine or not.

Sometimes you're sure a rock's name has been mispelled. Surely goethite should be geothite. Alas, no. Goethite is named for the polymath Wolfgang van Goethe.

In summary, if you're interested in the semiotics of rocks, you (unlike the rocks) will need to be flexible.

- © 2010 Jules Berman

Precancer time machine

2010-09-11T03:47:00.000-07:00

In the previous post , we discussed breast precancer. We saw that as mammography picked up earlier and earlier lesions (precancers and early breast cancers), deaths from breast cancer dropped, along with the incidence of invasive breast cancer.

In this graph, which covers the years 1975 to 2007, the top line of bars (blue) represent the incidence of breast cancers (including invasive and non-invasice lesions). The next lower line of bars (maroon) is the incidence of the invasive breast cancers (the kind that account for breast cancer deaths), and the bottom line of bars represents the rate of precancers (ductal carcinoma in situ) collected by SEER.

As you recall from the previous blog , the big drop in cancer death rates did not occur until about 1990, well after there was a rise in the number of diagosed breast precancers.

Why didn't the precancers diagnosed in the 1980s produce an immediate drop in the rate of breast cancer deaths or in the incidence of invasive cancers?

Precancer treatment works like a time machine. When you cure a precancer today, you don't see a reduction in the number of cancers that would arise that same day. You see a reduction in the number of cancers that will arise in some future date (if the precancer had been allowed to develop into a cancer, over time).

If a precancer would ordinarily require 5 years to develop invasive features (i.e., become a cancer), and you diagnose and treat the precancer in 2010, then you will eliminate a cancer that would have occurred in 2015. This explains the delay in the decrease in cancer mortality that you can always expect to see with successful precancer treatment initiatives.

- © 2010 Jules Berman

key words: precancer, precancerous, dcis, ductal carcinoma in situ, breast cancer, breast cancer mortality, cancer prevention, carcinogenesis

Treating breast precancers saves lives

2010-09-09T05:07:00.001-07:00

Breast cancer deaths rose through the '70s and '80s, but declined in the '90s. For nearly the past 20 years, American women have had about a 2% annual drop in the breast cancer death rate.

Here is the mortality graph provided by the U.S. National Cancer Institutes SEER (Surveillance, Epidemiology and End Results) program.

Though nobody wants to take the blame for the rise in breast cancer deaths in the '70s and '80s, lots of people want credit for the fall of breast cancer deaths that began in the '90s. Was it due to a reduction to the exposure of carcinogens, or to better treatment, or to earlier diagnosis?

The fall in breast cancer deaths does not seem to be due to cancer prevention. While the deaths from breast cancer were falling, there was an apparent rise in the incidence of breast cancer cases. Here is the SEER graph for the incidence in breast cancer in the U.S.

Since the breast cancer incidence rose while the deaths from breast cancer dropped, it seemed as though the benefit must have come from better treatment or earlier detection.

A major study, attempting to resolve this issue, was published in the New England Journal of Medicine, in 2005:

Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen M, Mandelblatt JS, Yakovlev AY, Habbema JD, Feuer EJ. Effect of screening and adjuvant therapy on mortality from breast cancer. Cancer Intervention and Surveillance Modeling Network (CISNET) Collaborators. N Engl J Med 353:1784-1792, 2005.

They concluded that that 28 to 65 percent of the sharp decrease in breast cancer deaths from 1990 to 2000 was due to mammograms. The remainder of the improvement was was attributed improved breast cancer treatment.

The study did not take into account the great contribution of precancer treatment to the reduction of breast cancer deaths.

Let's review this SEER data, this time taking into account the diagnosis of DCIS (ductal carcinoma in situ) a precancer that precedes the development of invasive breast cancer. Here is the SEER data for the incidence of all breast cancer and of DCIS (the precancer for breast cancer).

In the past few decades, there has been a huge rise in the number of diagnosed cases of breast precancers. This is due largely to the use of mammography, which can detect lesions that cannot be found by palpation. When a precancer is detected and removed, the patient does not develop invasive cancer.

The total number of breast cancer cases includes cases of DCIS. If we subtract the number of breast precancer cases (DCIS) from the total number of breast cancer cases, we get the incidence of invasive breast cancer cases. Here is the SEER data.

In this graph, which covers the years 1975 to 2007, the top line of bars (blue) represent the incidence of breast cancers (including invasive and non-invasice lesions). The next lower line of bars (maroon) is the incidence of the invasive breast cancers (the kind that account for breast cancer deaths), and the bottom line of bars represents the rate of DCIS.

Look carefully at the middle bars (maroon), representing the incidence of invasive breast cancers. The graph shows that incidence of invasive breast cancers has actually dropped since the early '90s, as the diagnosis and treatment of DCIS has risen.

Much of the decrease in breast cancer mortality can be accounted for by the diagnosis and treatment of breast precancers. In fact the drop in breast cancer deaths follows the same slope, and has about the same magnitude, as the drop in invasive breast cancers that follows the increase in breast precancer treatments.

- © 2010 Jules Berman

Mystery of the missing prelymphomas

2010-09-08T06:07:00.000-07:00

A large problem in pathology is the lack of any consistent nomenclature for the precancers. Consequently, many precancerous lesions are simply not recognized as such, and cannot be included in clinical trials that assess the effectiveness of precancer treatments.

For example, lymphoma experts do not use the terms "precancer" or "prelymphoma" [prelymphomas are lesions that precede the development of lymphomas].

In a recent article, Elaine Jaffe discussed a condition that can be detected by flow cytometry in which monoclonal populations of CD5+ B-cells are found in 3% of healthy adults over the age of 40.

Jaffe ES. The 2008 WHO classification of lymphomas: implications for clinical practice and translational research. Am Soc Hematol Educ Program 523-531, 2009.

Many of these clones have the same marker chromosomes found in chronic lymphocytic leukemia (CLL). A small percentage of patients with these lesions will progress to CLL. This lesion is a precancer for CLL; and is strictly analogous to MGUS (monocloncal gammopathy of undetermined significance) a condition that precedes virtually every case of multiple myeloma (MGUS was discussed in a prior blog entry).

The condition has been given a name: monoclonal B-cell lymphocytosis. This is the only name by which the lesion is addressed in the WHO (World Health Organization) lymphoma classification.

Monoclonal B-cell lymphocytosis has all of the biological properties of a precancer It should be recognized as such (in this specific case, as the prelymphoma for CLL). If it were, it could be included in clinical trials for precancers.

The WHO has grappled with several different proliferative lymphoid lesions that can precede the development of lymphomas. They have used, or currently use, terms such as "proliferations of uncertain malignant potential" or "intrafollicular neoplasia", or "in situ follicular neoplasia." Why bother? There is an accepted term for lesions that precede cancers of every cell type of origin: precancers. The word "precancer" does not appear anywhere in the WHO classification or in Dr. Jaffe's discussion of the WHO classification. It would be very helpful if hematopathologists climbed aboard on this issue.

Though we are currently treating only a few of the different kinds of precancers in man, there is ample evidence that precancer treatment effectively reduces the number of people who die from cancer. In the next blog, I'll expand the topic of precancer treatment.

- © 2010 Jules Berman

key words: lymphoma, lymphoma classification, prelymphoma, pre-lymphoma, precancer, lymphocytosis, precancer treatment

Precancer: missed opportunities for diagnosis

2010-09-05T04:06:00.000-07:00

"And what physicians say about disease is applicable here: that at the beginning a disease is easy to cure but difficult to diagnose; but as time passes, not having been treated or recognized at the
outset, it becomes easy to diagnose but difficult to cure. The same thing occurs in affairs of state; for by recognizing from afar the diseases that are spreading in the state (which is a gift given only to a prudent ruler), they can be cured quickly; but when they are not recognized and are left to grow to the extent that everyone recognizes them, there is no longer any cure."

- Niccolo Machiavelli

Today's blog continues yesterday's discussion of the precancers. The theme of all these blogs is that precancers, the lesions that precede the development of cancers, can be easily treated. Treatment of all precancers will lead to the eradication of all human cancers.

One of the obstacles in the treatment of the precancers comes from the reluctance of many pathologists and oncologists to recognize precancers when they see them. If you don't recognize the precancers, the clinical trials for new cancer chemotherapeutic agents becomes virtually uninterpretable.

Here's an example:

Suppose you have a new drug that targets a specific gene that is altered in a particular type of cancer. You collect a group of patients with the cancer, and you treat them with your drug, comparing their response to a group of cancer patients who are treated with conventional chemotherapy. You find that 10% of your patients respond well to the drug, and 90% don't respond at all. On average, the group of people who received the new drug had a shorter survival than the group who received conventional chemotherapy. You abandon your new drug.

Now suppose that your population of patients did not all have the same cancer. Suppose that 10% of them actually had a precancerous lesion, and this population accounted for the good responders in your experimental treatment group. In this case, your new therapy failed miserably as a treatment for people with developed cancers, but it succeeded remarkably well as a treatment for precancers.

Unless you have a way of distinguishing the precancers from the cancers, you cannot adequately assess the results of a clinical trial that includes a subset of people who have the precancerous lesion!

If you are a pathologist or an oncologist, you might be thinking that this cannot occur. Patients accrued to clinical trials are carefully evaluated to ensure that they all have the same cancer and have not been misdiganosed [with precancers]. In tomorrow's blog I will show that this is not always the case. In fact, the blurring of precancers with cancers is a prevalent, but avoidable, obstacle to progress against cancer.

- © 2010 Jules Berman

Precancer properties

2010-09-04T06:50:00.000-07:00

Readers of this blog know that I have a keen interest in precancers. Precancers are the lesions that precede the development of cancers. Unlike cancers, precancers are easy to treat. If we successfully treated precancers, we would stop cancers from developing.

Why are cancers so easy to treat? There are several reasons. First, precancers are fragile lesions. Spontaneous regression is common in precancers. It is easier to treat a localized lesion that is always skirting-on-the-edge of its existence, than to treat fully developed cancers, that virtually never regress spontaneously and that have metastasized to throughout the body.

In addition, fully developed cancers are biologically complex, with many different genetic and epigenetic alterations that confer their malignant phenotype (properties). When a cell has many different genetic lesions, you would expect it to be difficult (or impossible) to effectively treat cancers by targeting any single molecular alteration. This has proven to be the case. Most of the new chemotherapeutic drugs, that target specific molecules, have not proven effective at curing the common cancers (i.e., epithelial cancers of lung, colon, prostate, pancreas). However, there has been remarkable success using the newer, targeted agents, against cancers that have simple genetic alterations (such as chronic myelogenous leukemia, GIST, and a variety of rare cancers).

Because the molecular targeted therapies work best against cancers with simple genetic alterations, you can expect them to work better against the precancers than against the cancers. This is because during carcinogenesis (cancer development), genetic alterations are continuously increasing in number. Precancers will never have as many genetic alterations as the cancers into which they eventually develop. Therefore, precancers, like the tumors that respond best to molecularly targeted agents, are genetically simpler than the common epithelial cancers that account for the majority of cancer deaths.

In the next few blogs, I will discuss some of the issues related to the precancers that were not discussed in my recently published book on the subject.

- © 2010 Jules Berman

Medical abbreviation nightmare

2010-08-13T07:26:00.000-07:00

In a separate essay, I discussed some of the intricacies of medical abbreviations, and I included a list of so-called "fatal" abbreviations, whose use could actually jeopardize patient care.

Here's another problem abbreviation: NSCLC

NSCLC is the abbreviation for Non-Small Cell Lung Cancer. In itself, this is a very awkward term, because it doesn't stand for a specific type of cancer, but for a group of cancers that happen not to be a specific type of cancer (i.e., not small cell lung cancer).

The two most common types of non-small cell lung cancers are squamous cell lung cancer and adenocarcinoma.

One possible abbreviation for squamous cell lung cancer is SCLC, hence NSCLC could mean, to some people, non squamous cell lung cancer.

The much-preferred abbreviation for squamous cell lung cancer is SCCL (squamous cell carcinoma of lung), which could just as easily be an abbreviation for small cell carcinoma of lung.

But let's pretend that everybody understands how to use these abbreviations, and nobody makes the most common orthographic error encountered, tranliteration, switching the positions of the C and the L (SCCL for SCLC).

There's still the problem of finding a coherent meaning in the term NSCLC. This term was invented because small cell carcinoma of lung has a clinical behaviour and a treatment that is different from all the other tumors of the lung. Small cell carcinoma of the lung, more than any other lung cancer, is likely to metastasize widely, before the primary lung tumor is detected clinically. This means that surgery for small cell carcinoma serves little purpose (because most tumors have have already metastasized at the time of surgery). Early aggressive chemotherapy makes more sense. Hence oncologists have created two major groups of lung cancer: 1) small cell cancer, and 2) everything else.

The problem now is that there are treatment differences among the NSCLCs. For example, bevacizumab (an antibody targeted against vascular endothelial growth factor and used to treat NSCLCs) seems to cause hemoptysis in some patients who have sqamous cell carcinoma of the lung. Hence, bevacizumab is currently recommended only for patients with non-squamous non-small cell carcinoma of lung (note the double negative). Hence, using abbreviations, bevacizumab is used for NSCCL NSCLC.

This illustrates just one more example of the confusing and potentially error-causing abbreviations used in medicine.

- © 2010 Jules Berman

Image montage with Perl Ruby Python

2010-08-03T16:41:00.000-07:00

On occasion, you might want to create a single image from a list of individual images. The easiest way of making such an image is with ImageMagick.

ImageMagick is a free, open source, image application. Information is available at:

http://www.imagemagick.org/Usage/

If you use Windows, a compiled binary download is available:

Perl, Python, and Ruby each have their own interfaces to ImageMagick.

Today's blog provides equivalent implementations of the ImageMagick montage feature, in three languages.

Perl:


#!/usr/bin/perl
use Image::Magick;
my $image = Image::Magick->new;
my $montage = Image::Magick->new;
my $status = $image -> Read("mach_cov.jpg", 
 "071.jpg", "072.jpg", "073.jpg", "075.jpg", 
"076.jpg", "077.jpg",  "079.jpg");
$image -> Scale('200x200');
print STDERR $status;
$montage = $image -> Montage(background => "Blue", 
borderwidth => "5", geometry => '+5+5', 
tile => "4x2");
$montage -> Write('jpg:orig.jpg');
system("imdisplay orig.jpg");
exit;

Python:


#!/usr/bin/python
import os
os.system("montage mach_cov.jpg \
071.jpg 072.jpg 073.jpg 075.jpg \
076.jpg 077.jpg 079.jpg \
-background #0000ff -geometry 200x200+5+5 \
-borderwidth 5 -tile 4x2  orig.gif")
os.system("imdisplay orig.gif");
exit

The "\" appearing at the end of lines is the DOS command-line continuation character.

Ruby:


#!/usr/local/bin/ruby
require 'RMagick'
include Magick
orig = Magick::ImageList.new( 
"mach_cov.jpg", "071.jpg", "072.jpg", 
"073.jpg", "075.jpg", "076.jpg", 
"077.jpg", "079.jpg")
new_img = orig.montage() {
self.background_color='blue'; 
self.border_color='red'; 
self.tile='4x2'
self.geometry='200x200+5+15'
self.border_width=5}
new_img.write("orig.gif")
system("imdisplay orig.gif")
exit

The input images are the 8 book covers that appear on the right hand side of this blog. Obviously, you'll substitute any number of images from your own collection. You'll also need to provide a an appropriate tiling geometry for the number of images you use (I used 4x2, four across and 2 down) for this example.

The output looks like this:

Each of the scripts calls provides the list of images to be included in the output image, and provides the montage method with attributes for the image display (i.e., background collor, image sizes, image border, space between images, tiling geometry).

In the case of the Perl script, the external module Image::Magick is called (available from ActiveState). In the case of Ruby, the RMagick gem is used. Users of these languages should known how to acquire these modules.

In the case of Python, a system call is made to the installed ImageMagick application. I had a little problem getting the Python script to work because the system call to the "montage" executable apparently executed some other program of the same name. This problem was skirted when I simply visited the subdirectory that held the ImageMagick files, and copied the montage.exe file over to the same subdirectory that held my Python script. When the python script was interpreted, the system used the montage.exe file in the same subdirectory as the script.

The montage method is explained in detail on the following web page:

http://www.imagemagick.org/Usage/montage/

Many of the basic algorithms of Medical Informatics, with equivalent implementations in Perl, Python, and Ruby are provided in my book, Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and Ruby, which will be published next month by CRC Press.

- © 2010 Jules J. Berman

New book coming in September

2010-07-23T05:51:00.000-07:00

If you've noticed a decline in my blogging of late, it's because I've been putting the finishing touches on my new book, Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and Ruby, which will be published in September in the CRC Press series in Mathematical and Computational Biology.

This book is a departure from my earlier language-specific works. The new book stresses descriptions of medical informatics algorithms, all presented in the following format:

1) Each method has a background discussion, explaining how the method is used by healthcare professionals.

2) This is followed by a narrative description of each of the steps of the algorithm.

3) Equivalent implementations of the algorithm are provided in Perl, Python and Ruby. Scripts draw data from freely available datasets (OMIM, SEER, CDC, Census Bureau, MeSH, and PubMed, Taxonomy).

4) Then comes an analysis of the output of the algorithm.

At the end of every chapter, a list of exercises provides students with short projects, that they can develop in the language of their own choice.

There are about 70 algorithms or methods provided in the book, and with a few exceptions (some of the introductory-level methods), these algorithms have not appeared in any of my prior publications.

The unique offering of this book is that students can master the fundamental methods and algorithms of medical informatics, in their preferred programming language. Instructors can concentrate on teaching useful algorithms, without wasting class time on language-specific programming instruction.

The book is divided into four parts: Part I. Building blocks of medical informatics; Part II. Free, publicly available medical data resources; Part III. The common tasks of medical informatics, and Part IV. Medical Discovery.

The purpose of Part I, Building blocks of medical informatics, is to introduce the basic computational subroutines that will be used in more complex scripts later in the book. Everyone who reads the book is expected to have a basic understanding of either Perl, Python, or Ruby. Readers can skip over the parts written for the other languages. Over the years, students will find it valuable to re-read the text, this time paying attention to the languages they ignored in the first pass.

In Part II, Free, publicly available medical data resources, students are shown how to use the U.S. government biomedical datasets. The chapters in Part II explain the intended uses of these datasets, how the data sets are organized, and how students can retrieve and analyze the data.

Part III, The common tasks of medical informatics, covers some of the computational methods of biomedical informatics, including autocoding, data scrubbing, and data de-identification.

Part IV, Medical discovery, provides examples of the kinds of questions that biomedical scientists can ask and answer with public data and open source programming languages. Students will learn techniques for combining heterogeneous data sources, measuring and visualizing trends in complex data, and testing new hypotheses. The majority of research and development projects that use biomedical data can be developed using the methods described in Part IV.

This book is written specifically as a textbook for courses in medical informatics. Instructors should appreciate this book, because it frees them to teach medical informatics, without wasting time teaching basic programming skills. The included scripts are for the students, who need code samples, from a familiar language, that they can use throughout their careers. The instructors will teach the algorithms (not the language implementations). This strategy allows instructors to teach a big subject (computational medical informatics), in a small amount of time (one academic course).

The book is designed to eliminate the inequities that result when an instructor imposes his choice [of programming language] on all of his students. Students who are trained in an alternate language will resent taking a course in an unfamiliar language. These students will be at a disadvantage compared to other students who happen to be trained in the course-book language. When a professor selects a computer language, he's usually got to start the course with the basics of the selected language, and this instruction can takes weeks of time away from the subject of the course [in this case, medical informatics]. This is just lame.

Creative Writing courses do not waste time teaching people about pens and pencils. Informatics courses should not be waste time teaching programming skills. When a medical informatics instructor tells everyone in the class to use Python, it's equivalent to a writing instructor telling everyone in the class to use a Papermate pen. A good course in medical informatics should expect students to have programming skills, but should not be focused on any particular programming language. Instructors should focus on the tasks, issues, and questions that comprise the core activities of professionals in the field. By providing a discussion of the importance and utility of each algorithm and method, instructors can teach the fundamentals of medical informatics, without wasting time teaching programming.

All of the data used in this book are free and publicly available. Most of the data comes from U.S. government sources, providing hundreds of gigabytes of high quality, curated biomedical data to a global community of scientists, healthcare experts, clinicians, nurses, and students. Every student should become familiar with these data sources, and understand their medical value. This book provides instructions for downloading all of the data sources discussed.

One of the most surprising features of medical informatics is that scripts capable of parsing large data sets, very quickly, can be quite short. By focusing on methods, not full-scale applications, scripts are kept short, concise, and easy to learn. In many cases, the scripts that implement an algorithm are actually shorter than the step-by-step description that precedes the implementation. Students will find that most of their future data analysis efforts can be accomplished by modifying and combining the scripts provided with this book.

-© 2010 Jules J. Berman

Anatomic adjectival forms

2010-06-14T05:44:00.001-07:00

In anatomy, most nouns have a corresponding adjective. In medicine, the adjectival forms are not always derived from the same root as the noun form.

Metaplasia of bone is not typically called metaplasia of bone or even bony metaplasia; it's called osseous metaplasia.

Doctors don't usually refer to stomach flu, when they can use a term like gastric flu.

Likewise, the adjective for finger is not finger-like or fingy; it's digital. Speaking of "digital," medical software developers who work in natural language processing, need to have lists of the adjectival forms of anatomic nouns. I prepared the following computer-parsable list for my own use. I thought that others working in the field of medical informatics may have a use for these terms. If you have additional terms to add, please submit them as a comment to this posting.

As with all the documents and software I provide, the following
disclaimer holds:

The data list is provided "as is", without warranty of any kind,
express or implied, including but not limited to the warranties
of merchantability, fitness for a particular purpose and
noninfringement. in no event shall the authors or copyright
holders be liable for any claim, damages or other liability,
whether in an action of contract, tort or otherwise, arising
from, out of or in connection with the data list or the use or
other dealings in the data list.

The data list, created by Jules J. Berman, on June 5, 2010, is
donated to the Public Domain.

abdomen <=> abdominal
adenohypophysis <=> adenohypophyseal
adnexa <=> adnexal
adnexae <=> adnexal
alveolus <=> alveolar
amygdala <=> amygdaloid
anatomy <=> anatomic or anatomical
antecubitus <=> antecubital
antrum <=> antral
anus <=> anal
aorta <=> aortic or aortal
appendix <=> appendiceal
arm <=> brachial
artery <=> arterial
aryepiglottis <=> aryepiglottic
atrium <=> atrial
bone <=> osseous
brachium <=> brachial
bronchus <=> bronchial
caecum <=> caecal
callosum <=> callosal
cecum <=> cecal
cerebrum <=> cerebral
cervix <=> cervical
clitoris <=> clitoral
cloaca <=> cloacal
coelom <=> coelomic
colon <=> colonic
commissure <=> commissural
cranium <=> cranial
cuticle <=> cuticular
cutis <=> cutaneous
cytology <=> cytologic or cytological
decidua <=> decidual
dermis <=> dermal
diaphragm <=> diaphragmatic
dorsum <=> dorsal
duct <=> ductal
duodenum <=> duodenal
dura <=> dural
ear <=> aural
embryo <=> embryonic or embryonal
endocervix <=> endocervical
endometrium <=> endometrial or endometrioid
endothelium <=> endothelial
epicanthus <=> epicanthal or epicanthic
epicardium <=> epicardial
epidermis <=> epidermal or epidermic
epiglottis <=> epiglottal or epiglottic
epithelium <=> epithelial
esophagus <=> esophageal
ethmoid <=> ethmoidal
eye <=> ocular
face <=> facial
faeces <=> faecal
fascia <=> fascial
feces <=> fecal
fetus <=> fetal
finger <=> digital
focus <=> focal
foetus <=> foetal
foot <=> pedal
front <=> frontal
gestation <=> gestational
gland <=> glandular
globe <=> global
gluteus <=> gluteal
gonad <=> gonadal
gyrus<=> gyral
haemorrhoid <=> haemorrhoidal
heart <=> cardiac
hemorrhoid <=> hemorrhoidal
hepatocyte <=> hepatocellular
hernia <=> hernial
hiatus <=> hiatal
histiocyte <=> histiocytic
histology <=> histologic or histological
hypophysis <=> hypopyseal
ileum <=> ileac or ileal
intestine <=> intestinal or enteric or enteral
ischium <=> ischial
jejunum <=> jejunal
kidney <=> renal or nephric or nephroid
labium <=> labial
larynx <=> laryngeal
leukocyte <=> leukocytic
liver <=> hepatic
lung <=> pulmonary or pulmonic
lymph <=> lymphatic or lymphoid
megakaryocyte <=> megakaryocytic
meninges <=> meningeal
metaphysis <=> metaphyseal
monocyte <=> monocytic or monocytoid
mouth <=> oral
mucus <=> mucous
myocardium <=> myocardial
myometrium <=> myometrial
node <=> nodal
nose <=> nasal
oesophagus <=> oesophageal
omentum <=> omental
orbit <=> orbital
os <=> oral
ovaries <=> ovarian
ovary <=> ovarian
pallidus <=> pallidal
pancreas <=> pancreatic
pathologic <=> pathologic or pathological
pelvis <=> pelvic
penis <=> penile
pericardium <=> pericardial
peritoneum <=> peritoneal
peroneum <=> peroneal
phalanges <=> phalangeal
phallus <=> phallic
pharynx <=> pharyngeal
pia <=> pial
prostate <=> prostatic
rectum <=> rectal
retina <=> retinal
skin <=> cutaneous
spine <=> spinal
spleen <=> splenic or lienal or splanchnic or splenial
stomach <=> gastric
subcutis <=> subcutaneous
synovium <=> synovial
tear <=> lacrimal
testis <=> testicular
thalamus <=> thalamic
thorax <=> thoracic
throat <=> glottal
thymus <=> thymic
tongue <=> lingual or glossal
trachea <=> tracheal
ureter <=> ureteral or ureteric
uterus <=> uterine
vagina <=> vaginal
vein <=> venous
ventricle <=> ventricular
vessel <=> vascular
vulva <=> vulvar
vulva <=> vulvar

- Jules Berman

Machiavelli's Laboratory updated

2010-06-13T14:22:00.000-07:00

I just updated my free online ebook, Machiavelli's Laboratory. Machiavelli's Laboratory is a satire on scientific ethics, told from the perspective of an unethcial scientist.

There have been multiple changes in the text since the first version came out on April 13. The book has been downloaded about 10,000 times. Most of the downloads have been the simple HTML format, with the PDF version pulling a distant second. I've also made a MobiPocket version for Kindle readers, but only about 100 people have chosen this format. I wrote a new CSS stylesheet that provides enhanced formatting for the text and a more appealing visual experience.

The book is available at:

http://www.julesberman.info/integ/machfree.htm

- Jules Berman

Updated web pages

2010-05-08T10:38:00.000-07:00

I've recently updated four of my web pages:

1. Book resource page (scripts, documents and other supplemental materials for my published books):

http://www.julesberman.info/factoids/index_g.htm

2. Chronology of earth page (timeline of some of the most significant events in the history of earth):

http://www.julesberman.info/chronos.htm

3. Precancer consensus meeting page (the published consensus paper from the NCI - George Washington University conference on precancer):

http://www.julesberman.info/factoids/precmeet.htm

4. Archive page for the Machiavelli's Laboratory blog site (links to all of the blog posts on my recently published ebook, Machiavelli's Laboratory:

http://www.julesberman.info/blogmach.htm

- Jules Berman

Cover art design with PovRay 6

2010-04-23T04:58:00.000-07:00

This will be the last of the blogs on cover art design with Pov-Ray.

Here is the tiled Machiavelli world. Other than the script provided, and Pov-Ray, you'll need the public domain image of Machiavelli, obtained from Wikipedia.

Click on image for larger size
Notice that there are two light sources, and two shadows to the globe.


#include "colors.inc" 
#include "textures.inc"
#include "shapes.inc"

camera {
 right      < -1.33, 0, 0 > 
 up         < 0, 1, 0 > 
 direction  < 0, 0, 1 > 
 location   < 0, 4, 20 > 
 look_at    < 0, 0, 0 > 
}

light_source { < -20, 35, 34 > 
color White
}

light_source { < 0, 0, 24 > 
color White
}

plane { y, -20
    pigment{ color Gray80}
    normal { ripples 1.0 frequency 3 scale 8}
    finish {
        reflection 0.15
        ambient 0.3      
    }
}
                 
sphere { <0, 0, 0>, 5.0
      pigment { image_map 
                { gif "c:\ftp\mach_wiki_public.gif" 
                //filepath for the Machiavelli image                       
                  map_type 0                       
                  interpolate 2 
                 } } 
          }

A modified version of this script was used to produce the cover art for Machiavelli's Laboratory, a satire on science ethics, currently available as a free ebook.

-© 2010 Jules J. Berman

Cover art design with PovRay 5

2010-04-22T10:17:00.001-07:00

Here's one more simple Pov-Ray image and its script.

Click on image to see higher resolution


  #include "colors.inc"
  camera {
    location <0, 1, -10>
    look_at 0
    angle 36
  }            
  
  sky_sphere {
  pigment {
    gradient z
    color_map {
      [0.0 rgb <0.6,0.7,1.0>]
      [0.2 rgb <0.2,0.3,0.9>]
    }
  }
}
  
   light_source { <500, 500, -1000> White }
  plane { y, -1.5                          
      pigment { checker Black White }
  }

  sphere { <0, 0, 0>, 1
texture{pigment{color Blue filter 2 } 
finish{phong .8}} interior {ior 0.1}
    translate -0.5*x
  }
  sphere { <0, 0, 0>, 1
texture{pigment{color Red filter 2 } 
finish{phong .8}} interior {ior 0.1}
    translate 0.5*x
  }
       
 sphere { <0, 0, 0>, 1
texture{pigment{color Yellow filter 2 } 
finish{phong .8}} interior {ior 0.1}
        translate -0.5*y
  }
  sphere { <0, 0, 0>, 1 
texture{pigment{color Green filter 2 } 
finish{phong .8}} interior {ior 0.1}
        translate 0.5*y
  }

- Jules J. Berman

Cover art design with PovRay 4

2010-04-21T07:03:00.000-07:00

Here is a sample of what you can generate with a simple Pov-Ray script.

Click on the image to see a higher resolution picture.

This is basically the same image that I showed in my blog a few days ago. But I increased the number of reflecting spheres and assigned them random locations in the scene-space. Basically, I just kept tweaking a stock script for a sphere on a checkered plane, until I got what I wanted.

Here's the entire script that produced the image rendered by Pov-Ray.


global_settings { assumed_gamma 1.0 }

camera {
  location  <5.0, -12.0, 2.0>
  up z sky z
  look_at   <0.0, 0.0, 0.5> 
  angle 40
}

sky_sphere {
  pigment {
    gradient z
    color_map {
      
      [0.2 rgb 0.2]
      [0.2 rgb 0.2]
      
    }
  }
}

light_source {
  <3, 1, 2>*1000
  color rgb <2.2, 1.8, 1.5>
}   

plane {
  z, 0
  texture {
    pigment {
      checker
      color rgb 1, color rgb 0
    }
  }
}

#macro Sphere(Pos, Radius)
  sphere {
    <Pos.x, Pos.y, -1 + Radius*3.3>, Radius
    texture {
      pigment { color rgb 1 }
      finish{
        diffuse 0.3
        ambient 0.0
        specular 0.6
        reflection 0.8
      }
    }
  }
#end

#local Cnt=0;
#local Seed=seed(0);

#while (Cnt<10000)
  Sphere(
    -100+<rand(Seed), rand(Seed)>*200, 
    0.3+pow(rand(Seed),2)*0.8
  )  
  #local Cnt=Cnt+1;
#end

If you try to run this .pov script in Pov-Ray, you'll find that the image looks a little different with each run. That's what happens when you use a random function.

- © 2010 Jules Berman

tags: graphic design, 3-D rendering, pov-ray, povray, cover art, machiavelli's laboratory

Cover art design with PovRay 3

2010-04-20T05:41:00.000-07:00

Every Pov-Ray newbie should start at the Pov-Ray organization's web site:

http://www.povray.org/

There, you can download the latest version of Pov-Ray, read tons of Pov-Ray documentation, look at amazing galleries of art created with Pov-Ray, find ancillary software that you can use with Pov-Ray, and acquire libraries of Pov-Ray objects that you can insert into your own renderings.

I use Windows, and my Pov-Ray installation was easy. When you open the application, you can choose to open a .pov script editing window [in which you write and save your scripts]. Whenever you get to a point where you think you can render the script, you simply click on an icon, and Pov-Ray will try to render your image.

In practice, especially for people new to Pov-Ray, you will build your first images by taking parts of sample .pov scripts created by others, and placing them into your own scripts. You will repeatedly render the images, making changes in the positions of the camera and lighting, in selection of colors, texture, and size, in modifications of the properties of textures and pigments, until you are happy with the rendered image, and then it becomes "your own". You will eventually add new objects into your image. Eventually, you might reach the stage where most, if not all, of the image is entirely your creation. Even then, you will be indebted to hundreds of Pov-Ray enthusiasts whose works led you to a level of competence.

Like so many other programming languages, you can write your first simple .POV script in under an hour, and you can spend the remainder of your life sharpening your skills.

Pov-Ray needs a little help for certain types of objects. 3-D text is actually very difficult to create "from scratch" in Pov-Ray. For text, you might want to use another freely available software application, Breeze Designer.

Breeze Designer is available at:

http://www.imagos.fl.net.au/

For the cover image I created a few days ago, I used Breeze Designer to make the text object, then I simply copied the object specifications from Breeze Designer and pasted them into my Pov-Ray script for the cover image. Then I added a marble texture to the text.

Click on image for larger size
Finally, I positioned the text on top of the globe from the scene, to produce the final image.

Click on image for larger size
Pov-Ray blends geometry, mathematics, and programming, and art. The online Pov-Ray tutorials are excellent.

- © 2010 Jules Berman

tags: graphic art, 3-D rendering, cover art, self-publishing

Cover art design with PovRay 2

2010-04-19T04:01:00.000-07:00

This is the second blog in a multi-part series on Pov-Ray.

Pov-Ray (Persistence of Vision Ray Tracer) is software that renders a 3-D image from a script. Pov-Ray was initially started in the 1980s by David Kirk Buck and Aaron A. Collins. In the 1990s, development was handled by a team of extremely talented and dedicated programmers who have produced upgraded versions of this remarkable, and free software.

Pov-Ray is covered in a very good article in Wikipedia. The Wikipedia article includes a superb, public domain Pov-Ray rendering, shown here:

Click on image for larger size
Before you try any project like this, you've got to start with something small. Every Pov-Ray newbie starts with a simple scene consisting of a ball, placed over a plane.

Here is an example:

Click on image for larger size
The Pov-Ray script for the scene is:


global_settings { assumed_gamma 1.0 }

// ----------------------------------------

camera {
  location  <5.0, -12.0, 2.0>
  up z sky z
  look_at   <0.0, 0.0, 0.5> 
  angle 40
}

sky_sphere {
  pigment {
    gradient z
    color_map {
      [0.0 rgb <0.6,0.7,1.0>]
      [0.2 rgb <0.2,0.3,0.9>]
    }
  }
}

light_source {
  <3, 1, 2>*1000
  color rgb <2.2, 1.8, 1.5>
}   

// ----------------------------------------

plane {
  z, 0
  texture {
    pigment {
      checker
      color rgb 1, color rgb 0
    }
  }
}

sphere {
  z*1.4, 1
  texture {
    pigment { color rgb 1 }
    finish{
      diffuse 0.3
      ambient 0.0
      specular 0.6
      reflection 0.8
    }
  }
}

Every Pov-Ray script contains command lines that specify a camera, one or more sources of light, and one or more objects. The camera, light source, and objects are provided coordinates on an imaginary 3-D grid. The Pov-Ray script calulates the pixels on an imaginary camera screen (corresponding to the final image) receiving rays of light that leave the light source(s) and travel through scene. The color of the light that reaches each pixel position on the camera is determined by the properties of the objects in the scene.

Tomorrow, I'll explain a little more about the mechanics of building and executing a Pov-Ray script (called a .pov script) from within the Pov-Ray application.

- © 2010 Jules Berman

tags: graphic art, 3-D rendering, cover art, self-publishing

Cover art design with PovRay

2010-04-18T05:39:00.000-07:00

I have no artistic skill. I cannot draw anything. I would have no way of even starting to represent a 3-D object on a piece of paper. It's been like this all of my life.

I recently self-published an e-book, Machiavelli's Laboratory, (discussed in several prior blogs). I needed to produce a striking cover image for the book. I turned to PovRay and used a public domain photograph from wikipedia of Niccolo Machiavelli, for the tiled globe:

Source: Wikipedia, public domain

The final image (tiled globe, on an infinite rippled plane, with 3-D block letters painted with multi-colored marble) was produced by a script, NOT with computerized drawing aids. The PovRay application rendered the entire image, with script commands, just like any programming language produces an output by following the commands in a program.

Click on image for larger view

I thought the 3-D lettering was a bit too flashy, so I ended up using a flat font, and this final image:

In the next few blogs, I'll discuss PovRay, and Breeze Designer (used to create the 3-D text object in the first rendered image), and I'll provide the two PovRay scripts that produced the final images. I'll also show you how you can obtain PovRay and Breeze Designer (both can be downloaded at no cost).

- © 2010 Jules Berman

tags: graphic art, rendering software, cover art, ebook, e-book

New Blog site: Machiavelli's Laboratory

2010-04-17T03:34:00.000-07:00

Machiavelli's Laboratory is my new ebook, currently available at to cost in HTML, PDF, or MobiPocket formats. It's a satire on ethics, written from the perspective of an unethical scientist.

Rather than continue taking space in this blog for discussion of Machiavelli's Laboratory, I've started a new blog just for that purpose.

It's address is:

http://machiavelli-lab.blogspot.com

- Jules Berman

tags: MobiPocket, mobile device reader, satire, ethics, science, research, morality, humor, free, ebook, book, e-publication, parody, medicine, medical, academics, ghost writers, authorship, universities, scientific misconduct,fraud, misconduct, data fabrication, Jules J. Berman, Ph.D., M.D.

Machiavelli's Laboratory Contents

2010-04-16T04:56:00.000-07:00

As per my prior blogs this week, Machiavelli's Laboratory. is a satire on ethics, written from the perspective of an unethical scientist.

At present, the book is available at no cost, in html format or as a pdf file.

The MobiPocket version works well on my desktop PC, but it is still untested on mobile devices. It is available at:

http://www.julesberman.info/integ/mach_lab.prc If you try it out, please let me know if you had any problems uploading and viewing it on your Kindle or other mobile reader.

Here are the Table of Contents of Machiavelli's Laboratory:

Preface

Chapter 1 Falsification And Fabrication Of Data
1.1 Lessening the guilt: retractions, scapegoats, and clueless conspirators
1.2 Elite liars
1.3 When you are caught
1.4 Advice for evil scientists

Chapter 2 Improving The Truth: The Art Of Scientific Misinterpretation
2.1 All statistical studies are open to misinterpretation
2.2 Introducing biases into your study
2.3 Falsification of conclusions
2.4 Misrepresenting progress in the war against cancer
2.5 Advice to evil scientists

Chapter 3 The Evil Writer
3.1 Co-authors
3.2 First credit
3.3 Evil books
3.4 Altering the past
3.5 Plagiarism
3.6 Plagiarism in a computer world
3.7 Advice for evil scientists

Chapter 4 Evil Editors And Reviewers
4.1 Advice for evil scientists

Chapter 5 Grantsmanship
5.1 True funding means never having to say you're sorry
5.2 Investing in science
5.3 Advice for evil scientists

Chapter 6 Rejection
6.1 Applauding bad science
6.2 Progress? what progress?
6.3 The consequences of rejection
6.4 Advice to evil scientists

Chapter 7 Complexity: The Devil Is In The Details
7.1 Advice for evil scientists

Chapter 8 Scientific Globetrotting
8.1 Advice for evil scientists

Chapter 9 Evil Intellectual Property
9.1 Advice for evil scientists

Chapter 10 Evil Standards
10.1 What the standards development organizations never do
10.2 Advice for evil scientists

Chapter 11 Abusing Power
11.1 The department chief
11.2 Advice for evil scientists

Chapter 12 Governments And Evil Science
12.1 Government cover-ups
12.2 Government against the people
12.3 The power of bureaucrats
12.4 Advice for evil scientists

Chapter 13 Corporations And Evil Science
13.1 Erbitux and the brave new world of gene targeted therapy
13.2 It pays to advertise
13.3 Scientific organizations are instruments of large corporations
13.4 Advice for evil scientists

Chapter 14 Universities And Evil Science
14.1 Academic freedom is the freedom to lie to your students
14.2 The fertile ground excuse
14.3 Advice for evil scientists

Chapter 15 Ethics, And The Avoidance Of Same
15.1 Consent and unconsent
15.2 Conflicts of interest
15.3 Betraying confidentiality
15.4 Evil patients
15.5 Harming animals
15.6 Advice for evil scientists

Chapter 16 Clinical Trials On Trial
16.1 Biases in survival data
16.2 Advice to evil scientists

Chapter 17 Scientific Disasters
17.1 Advice for evil scientists

Chapter 18 Belief And Disbelief
18.1 Mathematics is neither science nor religion
18.2 Advice for evil scientists

Chapter 19 Sex And Gender
19.1 Where the boys are
19.2 Advice for evil scientists

Chapter 20 Greed
20.1 Cashing in on cancer
20.2 Off-label physicians
20.3 Ghost writers
20.4 Advice for evil scientists

Chapter 21 The Future Of Evil Scientists
21.1 Advice for evil scientists

Final Exam

Glossary

References

- Jules Berman

tags: satire, ethics, science, research, morality, humor, free, ebook, book, e-publication, parody, medicine, medical, academics, ghost writers, authorship, universities, scientific misconduct, data fabrication, Jules J. Berman, Ph.D., M.D.

Kindle Mobi ebook of Machiavelli's Laboratory

2010-04-15T05:44:00.000-07:00

Machiavelli's Laboratory is now available as a free ebook formatted for Kindle and other mobile viewers, in Mobi format. The book is a satirical study of ethics, from the perspective of an unethical scientist.

The file reads fine on my desktop PC, using the MobiPocket reader, but I don't have a mobile reader and the file is currently untested for the Kindle or other mobile devices. If you load Machiavelli's lab onto a mobile device, I'd be very grateful if you provided some feedback on the process of downloading the file from my website, uploading the file to your Kindle, viewing the file, and navigating the text.

The Mobi version of the file is available at:

http://www.julesberman.info/integ/mach_lab.prc

Readers who would like to read the Mobi Kindle version on their PCs can download a free Mobi reader from:

http://www.mobipocket.com/en/DownloadSoft/Default.asp?Language=EN

Machiavelli's Laboratory is also available, at no cost, in html format or as a pdf file.

- Jules Berman

tags: MobiPocket, mobile device reader, satire, ethics, science, research, morality, humor, free, ebook, book, e-publication, parody, medicine, medical, academics, ghost writers, authorship, universities, scientific misconduct,fraud, misconduct, data fabrication, Jules J. Berman, Ph.D., M.D.

Free ebook: Machiavelli's Laboratory

2010-04-13T19:04:00.000-07:00

I've just self-published an ebook entitled, Machiavelli's Laboratory. It's a satire on ethics, focusing on scientists and physicians.

At present, the book is available at no cost, in html format or as a pdf file.

- Jules Berman

tags: satire, ethics, science, research, morality, humor, free, ebook, book, e-publication, parody, medicine, medical, academics, ghost writers, authorship, universities, scientific misconduct, data fabrication, Jules J. Berman, Ph.D., M.D.

Reviews are in on Precancer book

2010-03-29T08:48:00.000-07:00

My recently published book, Precancer: The Beginning and the End of Cancer, written with Dr. G. William Moore, has now gotten six customer reviews on Amazon.

In the interest of complete disclosure, all six of the reviewers are people known to myself or to Dr. Moore. But the reviews were entirely voluntary and unpaid, and the reviewers were obviously free to write whatever they chose. Most of the reviewers included their own professional opinions on the book's subject, and I found it very interesting to see the different types of reactions to the book's central message (i.e., that we can virtually eliminate the disease known as cancer if we treated the precancers).

The reviews are available at Amazon.com's Precancer book page.

- Jules J. Berman, Ph.D., M.D.

key words: pre-cancer, precancers, precancerous lesions, early cancer, cancer precursor, preneoplastic lesions, cancer prevention, cancer treatment, eradication of cancer

FIRST CREDIT 5

2010-02-28T04:20:00.000-08:00

This is the fifth and last in a multi-part blog on the topic of FIRST CREDIT in the sciences.

Sometimes, credit falls on the person who least understood the significance of his own work. In 1771, Charles Messier (1730 - 1817) , selected 103 heavenly objects that have captured the rapt attention of astronomers for nearly two and a half centuries. Messier selected regions of space that were nebulous, and obscured his view of comets (his sole interest). He made a point of categorizing the Messier objects as areas of space that should be avoided by serious astronomers. In 1771, his chosen spots might have been accurately called the Messier non-objects.

Charles Messier.
Source: Wikipedia, public domain.

Today, the Messier objects are credited with holding some of the most fascinating galaxies and cosmologic curiosities in the known universe. Though Messier was completely wrong, he has achieved scientific immortality, just the same.

Messier object 51.
Source: Wikipedia, public domain NASA image.

The first observation of a particular type of anemia associated with sickled red blood cells, was made by Ernest E. Irons (1). Dr. Irons was a young intern when he encountered a patient, Walter Clement Noel, and made his historic observation. He alerted his attending physician, James B. Herrick. Irons sketched the shaped of the cells directly into the patient's hospital record. Herrick wrote the 1910 case report as a single author submission, excluding Irons (2). To this day, the disease sickle cell anemia carries the eponym, Herrick's disease (not Irons disease).

Sometimes first credit goes to the wrong species. Salicylic acid has been used as a medicinal by several different ancient cultures. In the western tradition, Hippocrates (5th century BC) claimed that a bitter powder extracted from willow bark could ease aches and pains. How did the ancients know that willow bark would relieve pain? Bears were observed rubbing against the bark of willow trees when wounded. The humans stole credit for an ursine discovery.

[1] Savitt TL, Goldberg MF. Herrick's 1910 case report of sickle cell anemia: the rest of the story. JAMA 261: 266-271, 1989.

[2] Herrick JB. Peculiar elongated and sickle-shaped red blood corpuscles in a case of severe anemia. Arch Intern Med 6:517-521, 1910.

This is the last entry on the topic of FIRST CREDIT in the sciences. If you have read the five-part series, I'd appreciate reading your comments.

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

FIRST CREDIT 4

2010-02-27T04:12:00.000-08:00

This is the fourth in a multi-part blog on the topic of FIRST CREDIT in the sciences.

Johann Franz Encke (1791 - 1865) is given credit for the discovery of [Encke's] comet (1818), but Encke merely calculated the orbit, using a technique first developed more than a century earlier by Edmond Halley (1656-1742). In 1705, Halley applied Newton's laws of physics to correctly predict that a particular comet (known today as Halley's comet), observed in 1531, 1607, and 1682, would return in 1758. The comet known today as Encke's comet was named after a person who neither first-sighted the comet nor discovered the methodology to predict the comet's orbit. The person who made the first sight of the commet has descended into scientific obscurity.

Johann Franz Encke.
Source: Wikipedia (public domain).

When a new technology becomes available to the practitioners of a field, it often happens that a new discovery is made by multiple independent researchers, simultaneously. For example, sunspots were discovered by Thomas Harriot (England, 1610), Johannes and David Fabricius (Frisia, now parts of The Netherlands and Germany, 1611), Galileo Galilei (Italy, 1612), and Christoph Scheiner (Germany, 1612). First credit usually goes to the most influential of the discoverers.

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

FIRST CREDIT 3

2010-02-26T04:05:00.001-08:00

This is the third in a multi-part blog on the topic of FIRST CREDIT in the sciences.

"If you want to make an apple pie from scratch, you must first create the universe."

- Carl Sagan

Carl Wilhelm Scheele (1742 - 1786) made some of the most important discoveries in the field of chemistry, but, through a series of bad breaks, lost first credit for all of them. Scheele discovered Oxygen a full two years before Priestley, but Scheele sent his manuscript to a publisher who held the work for several years, during which time Priestley got his discovery into print. Today, Joseph Priestley (1733 - 1804) is widely held to be the discoverer of Oxygen. In 1774, Scheele laid the groundwork for the discovery of Manganese, but Johan Gottlieb Gahn (1745 - 1818) finished the task and received the credit. Also in 1774, Scheele isolated chlorine but failed to identify it as an element; credit eventually went to Humphry Davy (1778 - 1829). Scheele did great service to science during his 46 years on earth.

Carl Wilhelm Scheele. Source: Wikipedia, public domain

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

FIRST CREDIT 2

2010-02-25T03:49:00.000-08:00

This is the second in a multi-part blog on the topic of FIRST CREDIT in the sciences.

Antonie Philips van Leeuwenhoek (1632-1723) is sometimes credited with inventing the modern microscope. Not so. Leeuwenhoek improved the microscope with his superb lens grinding technique, but he did not invent the microscope and did not make any particularly important modifications to the design of the microscope.

Source: Garrison FH. History of medicine.
WB Saunders, Philadelphia, 1921.

In 1595, fifteen-year old fledgling Dutch lens grinder (and part-time counterfeiter), Zacharias Jansen (1580 - 1638) placed two lenses in a tube, and created the first compound microscope.

Source: Wikipedia (public domain)

This amazing invention sat dormant until 1667, when Robert Hooke (1635 - 1703) studied insects and plant material, particularly cork, with this 72 year old invention. Hooke used the word "cell" to describe the complex, living structures that compose every organism. In 1675 (eight years after Hooke), with improved lenses, Leeuwenhoek studied micro-organisms in water and cells of the human body. Hooke and Leeuwenhoek kick-started modern microscopy, but Zacharias Jansen invented the microscope.

Smallpox was the first disease for which vaccination was successful. As early as 200 B.C.E. in China and 1000 B.C.E. in India, physicians knew that infection with smallpox conferred immunity against subsequent infection. Based on this observation, they were the first to develop a vaccination, administered nasally, of attenuated virus. Arabic doctors developed their own treatment, consisting of transferring material from an infected pox blister to another person via a small cut. Emmanuel Timoni (1670 - 1718) was a physician practicing in Constantinople. He introduced the Arabic vaccination process to West, in 1717. In 1796, Edward Jenner (1749 - 1823) developed a new vaccine, developed from a bovine pox virus (vaccinia) that seemed to confer cross-immunity against smallpox (variola). When you consider that the word, "vaccine", derives from Jenner's choice of inoculum (vaccinia), it seems reasonable to give Jenner the credit for developing the first effective vaccine. Incidentally, Jenner's paper describing his smallpox vaccine was rejected, in 1976, by a peer-reviewed journal (1).

Source: Garrison FH. History of medicine.
WB Saunders, Philadelphia, 1921.

If you want to give credit to the first person to save European lives by immunizing against smallpox, you would need to go 80 years earler than Jenner; to Timoni. To be really fair, you would need to go back many centuries, to the Chinese, Indian and Arabic physicians, to find the origin of human immune treatments.

[1] Altman LK. When peer review produces unsound science. The New York Times June 11, 2002.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

FIRST CREDIT 1

2010-02-24T13:28:00.000-08:00

This is the first in a multi-part blog on the topic of FIRST CREDIT in the sciences.

Stigler's law of eponymy, "No scientific discovery is named after its original discoverer."

- SM Stigler (1)

According to Stigler, credit always goes to the wrong person, and this is the essence of Stigler's law of eponymy (which, according to Stigler, must have been invented by someone other than Stigler). Stigler provides numerous examples of credit going to the wrong scientist (1). "Laplace employed Fourier Transforms in print before Fourier published on the topic, that Lagrange presented Laplace Transforms before Laplace began his scientific career, that Poisson published the Cauchy distribution in 1824, twenty-nine years before Cauchy touched on it in an incidental manner, and that Bienayme stated and proved the Chebychev Inequality a decade before and in greater generality than Chebychev's first work on the topic."

Yes, misleading eponymous terms are commonplace in the sciences. Marcello Malpighi (1628 - 1694) was an Italian physician who was one of the earliest scientists to use the microscope to describe tissues and their diseases. He was the first to describe lymphoadenoma, the lymphoma known today as Hodgkin's disease. More than a century later, Thomas Hodgkin (1798 - 1866) wrote a manuscript and credited Malpighi with the first description of the disease. Nonetheless, the eponym for the lymphoma went to Hodgkin.

Source: Garrison FH. History of medicine.
WB Saunders, Philadelphia, 1921.

Likewise, the Wheatstone bridge, introduced in 1843, was not invented by Charles Wheatstone (1802 - 1875). Wheatstone, working from the prototype, improved and popularized the device. The eponym was bestowed on Wheatstone, despite his protestations. The original bridge was invented by Samuel Hunter Christie (1784 - 1865), in 1833.

[1] Stigler SM. Statistics on the table: the history of statistical concepts and methods. Harvard University Press, Cambridge, p 277, 1999.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

INTELLECTUAL PROPERTY 6

2010-02-22T03:54:00.000-08:00

This is the sixth and last in a multi-part blog on the topic of INTELLECTUAL PROPERTY in the sciences.

"Most people are other people. Their thoughts are someone else's opinions, their lives a mimicry, their passions a quotation."

- Oscar Wilde

In earlier blogs, we covered uses of the patent system that had dubious societal value, specifically:

1. Patenting to suppress innovation.
2. Patent farming.
3. Patent spreading.
4. Patent holding.
5. Patent shifting.
6. Remixing prior patents.
7. Patenting the uses of unpatented inventions.
8. Patenting the obvious and the previous.
9. Patenting life.
10. Viral patenting.
11. Royalty stacking.
12. Reaching through a patent.

The government awards patents, but when someone infringes on a patent, the government takes no action. Only the patent holder is harmed, and only the patent holder can litigate against the infringing party. For this reason, a patent is sometimes referred to as the right to sue. Paradoxically, the typical patent holder is terribly frightened of lawsuits and will do almost anything to avoid a court appearance. Why?

I am not a lawyer, and the following paragraphs should not be construed as competent legal advice. They are included here to indicate that some patents, software patents in particular, sit on shaky ground, and that they are often vulnerable to declaratory judgments.

Imagine that you hold a software patent, and you have identified a person whose software contains some code that seems to infringe on one or more of the claims contained in your patent. Your lawyer sends this person a letter claiming infringement and demanding that the person either stop using the patented property or begin paying an assigned royalty. This is the so-called "demand letter" that every software programmer fears.

The alleged infringer, if smart, will seek remedy in a federal court, arguing that your patent is invalid or unenforceable, or that he did not infringe. He will ask for a declaratory judgment to stop you from pursuing your patent demands.

The declaratory judgment is a preventive adjudication. Its purpose is to clear the air, so that the person who received the demand letter need not labor under the constant fear of an impending lawsuit filed by the patent holder (1). Your alleged infringer will bring his case to a federal court venue, near where he lives (you and will need to travel to the location), giving him the home court advantage. If he asks for a declaratory judgment based on non-infringement, you will be required to pursue a counterclaim of infringement; an action that you may not be prepared to pursue. In the case of software patents, virtually every patent holder stands on very weak ground. All software is derivative of someone else's work; hence, every software patent is vulnerable to a declaratory judgment. You may have spent millions of dollars developing your invention and seeking your patent, but all of your investment could be lost through a declaratory judgment.

Declaratory judgment cases must be triggered by a significant controversy, usually a threat of litigation. Your demand letter, indicating infringement and requiring compensation, is often sufficient to trigger a claim for declaratory judgment. This means that, if you have a vulnerable patent, you must NOT send a demand letter that has the effect of a threat.

You may try having a salesman send the letter (not a lawyer). A letter from a salesman is less likely to imply the threat of legal action than a letter from retained counsel. In the letter, you might want to simply identify the patent and indicate that it was available for licensing. It may be wise not to suggest that infringement has occurred.

The purpose of a "demand" letter is to motivate the receiver to buy a license, without triggering a declaratory judgment action. If the letter is sufficiently bland and non-threatening, it may do the trick. Remember, though, that the receiver will likely interpret your letter as a thinly veiled threat. When determining jurisdiction for a declaratory judgment, courts look at all the relevant circumstances. If you have a history of vigorously pursuing patent claims, or you have a history of intimidating people with the implied threat of legal action, a court may interpret any letter from you, no matter how bland, as an intent to litigate.

What is the moral of this multi-part blog on INTELLECTUAL PROPERTY? The patent system does not always work to the benefit of the patent holder, or of society. Scientists who develop basic algorithms, or implementations of existing patents, or whose works do not qualify as inventions or devices, or whose discovery was a creation of nature, or a finding of vital importance to the health of others, should think very deeply before seeking patent protection for their works.

[1] Uniform Declaratory Judgments Act. National Conference of Commissioners on Uniform State Laws. August 2 - 8, 1922.

PLEASE POST COMMENTS ON THIS SERIES: INTELLECTUAL PROPERTY IN THE SCIENCES.

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

INTELLECTUAL PROPERTY 5

2010-02-21T04:16:00.000-08:00

This is the fifth in a multi-part blog on the topic of INTELLECTUAL PROPERTY in the sciences.

In the blogs from yesterday and the day before, we covered uses of the patent system that had dubious societal value, specifically:

1. Patenting to suppress innovation.
2. Patent farming.
3. Patent spreading.
4. Patent holding.
5. Patent shifting.
6. Remixing prior patents.
7. Patenting the uses of unpatented inventions.
8. Patenting the obvious and the previous.
9. Patenting life.

Here are three more common practices:

10. Viral patenting. Asserting a patent on the manufacturer of an assembled device, and asserting the same patent on the users of the manufactured device. Viral patenting is risky for the patent owner. In a precedential case, the U.S. Supreme Court unanimously ruled that LG Electronic could not assert a patent against Intel (the manufacturer that implemented a memory-technology patent owned by LG Electronics) and on the computer makers that install Intel chips in their computers (1). The patent power to collect royalties was effectively exhausted by its first license (with Intel).

11. Royalty stacking. For a complex process, it may be possible to assert different patents on various steps in a process. For example, a medical test may involve processing cells using a patented technology, using a one or more patented reagents, performing a patented analytic process, using a patented machine, and evaluated using patented software. After all the royalties are stacked, the costs are transferred to the patient or to a third party payer (2).

12. Reaching through a patent. Savvy patent holders may issue licenses that contain an insidious "reach-through" clause. The clause may stipulate that license holders can use the patent under the condition that any future technologies, that the license holder develops with the licensed technology, will be assessed a royalty. The clause allows the patent holder to reach through into the intellectual property created by the license holder, and impose an additional royalty.

Of course, nobody is obligated to patent his discoveries. On November 8, 1895 Wilhelm Roentgen performed the experiment that marked the discovery of X-ray imaging. Six years later, in 1901, Roentgen's effort was awarded with the Nobel prize. Roentgen declined to seek patents or proprietary claims on his discovery and even declined, unsuccessfully, the eponymous appellative, "Roentgen ray." Such altruistic behavior is uncommon.

The government awards patents, but when someone infringes on your patent, the government takes no action. Only the patent holder is harmed, and only the patent holder can litigate against the infringing party. For this reason, a patent is sometimes referred to as the right to sue. Paradoxically, the typical patent holder is terribly frightened of lawsuits and will do almost anything to avoid a court appearance. Why? This will be answered in tomorrow's blog.

-- TO BE CONTINUED --

[1] U.S. Supreme Court ruling sets limits on patent royalties. The New York Times June 9, 2008.

[2] Jones KJ, Whitham ME, Handler PS. Problems with royalty rates, royalty stacking, and royalty packing issues. In: Intellectual Property Management in Health and Agricultural Innovation: A Handbook of Best Practices, eds. A Krattiger, RT Mahoney, L Nelsen, et al. MIHR, Oxford, U.K., pp. 1121-1126, 2007.

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

INTELLECTUAL PROPERTY 4

2010-02-20T03:58:00.000-08:00

This is the fourth in a multi-part blog on the topic of INTELLECTUAL PROPERTY in the sciences.

In yesterday's blog, we were discussing uses of the patent system that had dubious societal value. We covered:

1. Patenting to suppress innovation.
2. Patent farming.
3. Patent spreading.
4. Patent holding.
5. Patent shifting.
6. Remixing prior patents.

TO CONTINUE:

7. Patenting the uses of unpatented inventions. The wheel is an unpatented invention. If you were to come up with a novel, useful, and nob-obvious application of the wheel, you might be able to patent your work. This means that when you use an invention that is not covered by a patent, your use of the invention may still constitute a patent infringement. Here is an example. DICOM (Digital Imaging and Communications in Medicine) is a freely available, unpatented standard for radiologic images. Currently, there is an effort to have all medical specialties adopt DICOM as the exclusive format for all medical images. Nonetheless, there there are specific circumstances for which the DICOM standard cannot be used without infringing on patented intellectual property. U.S. Patent 6725231, issued Apr 20, 2004, to Jingkun Hu and Kwok Pun Lee and assigned to Koninklijke Philips Electronics N.V., has the following claim.

"1. A method for mapping a DICOM specification into an XML document, comprising: mapping each entry of a DICOM table of the DICOM specification into a corresponding XML element of a plurality of XML elements,outputting each XML element of the plurality of XML elements to the XML document, in an output format that conforms to at least one of: an XML document-type-definition and an XML Schema."

In addition, the patent owners have been granted a similar patent by the European Patent Office (EPO). Mapping image information from a free specification, such as DICOM, into another free specification, such as XML, is a common task for medical informaticians. Does this activity constitute an infringement on an existing "use" patent? These are the types of questions that keep patent lawyers busy.

8. Patenting the obvious and the previous. Take the time to visit the USPTO website. You might find that many patents in your field are trivial, obvious, derivative, or useless. True "Eureka" moments are rare. Those who file patents are often motivated by fear ("If I don't patent this, somebody else will, and I can't bear to think that I may be required to pay royalties for my own invention), opportunism ("Hmmm. I can't believe nobody has patented this! I'd better do it before someone else does"), security ("My boss will not give me that raise unless I produce another patent this year"), or greed ("I'll squeeze every penny out of my competitors"). To receive a patent, an invention should be novel, non-obvious, and useful, but the reviewers at the patent office cannot always reach a wise determination.

Software developers are among the angriest critics of the USPTO. In recent years, the USPTO has awarded many software patents, a practice that seems to counter the principle that "ideas" are not patentable. Software developers argue that all software is built from recycled algorithms whose original sources are lost to techno-history. You cannot create a software application without using bits of code that were developed by legions of software developers, over the past half century. Today, software developers live in fear that a line of their code or a brief algorithm included in a complex software application will infringe on one or more software patents. The ever-present risk of patent infringement is a nightmare for earnest software developers, and a dream-come-true for opportunists. If you can patent an algorithm or subroutine that every developer uses, you stand to make a fortune.

9. Patenting life. What must it feel like to own an entire species of living organism? It must be how God would feel, if God had the the Supreme Court on his side. In a 1980 5-4 ruling, the Supreme Court upheld that a living organism could be patented. The case was Diamond v. Chakrabarty and involved a dispute over a patent for a genetically modified bacterium (3), (4).

After a patent on life is awarded, the consequences can be far-reaching. For example, Monsanto developed and patented genetically engineered corn that is resistant to Monsanto's own Roundup weed killer. Using Monsanto's corn seed, robust corn grows in fields that are liberally treated with Roundup. This guarantees that farmers who buy Roundup-resistant corn will also buy Roundup, at Monsanto's price. When farmers buy Roundup-resistant corn, they agree not to collect seed (from their corn crops) for replanting. Each growing season, they must buy new seed from Monsanto, at Monsanto's price (5). The use of genetically engineered seed is rapidly spreading. As more and more farmers use Monsanto's seed, the risk increases that genetically engineered seed will drift (from the winds, or from passing seed transport trucks) onto the fields of farmers who chose not to use genetically engineered corn. After genetically engineered corn invades a field, Monsanto can assert its seed patent on the clueless farmer. As we come to rely on a small genetic pool of crop seeds, the risk increases that a newly emerging disease will decimate the world's food supply.

The Diamond v. Chakrabarty ruling extends "life" patents to genes and sequences of DNA. Jensen and Murray reported in 2005 that 4,382 of 23,688 human genes in National Center for Biotechnology Information had been patented (6). The two most highly patented genes were BMP7, an osteogenic factor, and CDKN2A, a tumor suppressor gene (6). These two genes are claimed in more than 20 patents.

-- TO BE CONTINUED --

[1] Crichton M. Patenting life. The New York Times February 13, 2007.

[2] Thirty-five U.S.C. 287 Limitation on damages and other remedies; marking and notice. http://www.uspto.gov/web/offices/pac/mpep/documents/appxl_35_U_S_C_287.htm Comment. This important patent provisions provides a level of protection to healthcare practitioners from patent infringement claims. It permits healthcare practitioners to perform customary medical activities (e.g. surgical procedures), even when a patent claim may apply to the procedure.

[3] Poste G. The case for genomic patenting. Nature 378:534-536, 1995.

[4] Eisenberg RS. Biotech patents: looking backward while moving forward. Nature Biotechnology 24:317-319, 2006.

[5] Barlett DL, Steele JB. Monsanto's Harvest of Fear. Vanity Fair, May, 2008.

[6] Jensen K, Murray F. Intellectual property. Enhanced: intellectual property landscape of the human genome. Science 310:239-240, 2005.

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

INTELLECTUAL PROPERTY 3

2010-02-19T04:07:00.000-08:00

This is the third in a multi-part blog on the topic of INTELLECTUAL PROPERTY in the sciences.

In the U.S., the first patents were issued in 1790; three in total. By 1800, there were 41 patents issued. In 1900, there were 26,414 patents issued. In the year 2000, there were 159,255 patents issued, of which 157,494 were inventions, 17,413 were designs, and 548 were plants (1). The reason that the rate of patent issuance has increased through the centuries has less to do with the heady pace of scientific progress and more to do with the profitability of holding intellectual property.

The original purpose of the patent system was to grant the inventor the exclusive right to make, use, or sell, or license his invention. Over the years, the uses of patents have expanded to include practices of dubious societal value, including the following:

1. Patenting to suppress innovation. If you were in the oil business, and an inventor developed a source of free, unlimited energy (e.g., solar power), you might be inclined to buy the patent for his solar energy invention for the sole purpose of halting its implementation. Likewise, if you held a patent on a gene or a drug, you could assert your patent to squelch research or medical testing on your property, for the duration of the patent (2).

In the case of healthcare, there are some limits on the use of patents to suppress a scientific discovery. In 1999, Congress passed 35 U.S.C. 287 specifying conditions that would limit the damages collected by patent holders from healthcare practitioners (3). If you held the patent on a new way of tying a knot, and if a surgeon required the knot to tie a ligature in a surgical procedure, the patent would probably not be enforceable on the surgeon, under 35 U.S.C. 287. For the moment, patent holders cannot stop physicians from saving lives.

2. Patent farming. If you hold a patent for an algorithm or a manufacturing process that could be used in other technologies, you might benefit by "seeding" your invention into the derivative technology. When the new technology is released, you can "farm" your patent by announcing that anyone using the new technology will need to pay you royalties. For example, if a committee is creating a new software standard, you might strive to become a member of the committee. If you can insert your algorithm or subroutine into the new standard, then your patent rights will extend to the final standard. If the standard is mandatory, you can expect to collect royalties from thousands or millions of unwilling users.

3. Patent spreading. Every patent contains a set of claims that specify the intellectual components that are protected by the patent. For example, a patent for a software application may claim each of the algorithms or subroutines that are featured in the application, the graphic user interface by which the application is accessed, and novel features included in the application. A cynical inventor will maximize his list of claims, dividing the patent into minimal patentable units. After the patent is awarded, the patent holder can spread his litigation over the many claims in his patent portfolio, effectively magnifying his intellectual property.

4. Patent holding. A shrewd capitalist can buy patents that cover fundamental processes that are necessary for a particular field. Whereas a single patent may be vulnerable to challenge, a collection of patents that insinuate their claims throughout a complex industry, might be impossible to fight. Patent holding companies (called patent trollers by their detractors) strategically collect patents on devices and processes that are vital to an industry. When the time is ripe, after a new technology has become an indispensable component of business, the patent holding companies can assert their portfolio.

5. Patent shifting. Sometimes, a patent holder may find himself in a position where it would be unwise to assert his patent. Large corporations and patent holding companies occasionally reach agreements with their competitors to hold each other harmless from patent infringements. These kinds of agreements can save companies a vast amount of time and expense. In such cases, a corporation may choose to sell various patents to a third party (an individual, a corporation, or a holding company). The third party, unrestricted by a non-litigation agreement (depending on the agreement's specific language), can prosecute the patent. This works best if the patent is not owned directly by the company that sells the patent.

For example, if a corporation sits on a committee that is developing a new industry standard, it may need to sign an agreement promising not to prosecute patents held by the corporation and implemented by the standard. This kind of agreement is developed by standards committees to discourage patent farming. The company can simply sell the patent to a holding company. Sometime in the future, when the standard becomes entrenched in an industry, the holding company would assert the patent against all of the patent users. The corporation that developed the patent would have made its profit up front, at the time of the patent's sale to the holding company.

6. Remixing prior patents. You can re-mix prior art to make a new device that you can patent for yourself. This provision in patent law is particularly useful for software corporations; virtually all new software is made by re-mixing old software. You must be careful, though, to produce a re-mixed product that is not obvious to your peers. In KSR v. Teleflex (April 30, 2007), the U.S. Supreme Court, in a unanimous opinion, reversed a Court of Appeals decision, and determined that a prior patent was unenforceable because it was obvious (4). The Supreme Court opinion discussed, at length, the principles of obviousness. In particular, the Supreme Court indicated that merely putting together prior art to make a new device can only qualify for a patent if the resulting device is unexpected by people working in the field; hence, not obvious.

-- TO BE CONTINUED --

[1] U.S. Patent Activity Calendar Years 1790 to the Present. U.S. Patent and Trademark Office. April 16, 2009.

[2] Crichton M. Patenting life. The New York Times February 13, 2007.

[3] Thirty-five U.S.C. 287 Limitation on damages and other remedies; marking and notice. http://www.uspto.gov/web/offices/pac/mpep/documents/appxl_35_U_S_C_287.htm

[4] KSR International Co. v. Teleflex Inc. et al. Supreme Court of the United States April 30, 2007.

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

INTELLECTUAL PROPERTY 2

2010-02-18T03:55:00.001-08:00

This is the second in a multi-part blog on the topic of INTELLECTUAL PROPERTY in the sciences.

"What is mine is mine. What is yours is negotiable."

- Nikita Khruschev, who is credited with using it to describe the American approach to arms control negotiations with the former U.S.S.R.

Though depriving society of a medical advance is not a crime, few holders of intellectual property resort to secrecy nowadays; they use patents, copyrights, and courtrooms to protect their interests. The modern patent is a property right (lasting 20 years) given by a government to an inventor of a method, or invention, or a novel item. Patent means "open," so named because the patent process opens the invention to scrutiny. The U.S. Patent and Trademark Office (USPTO) publishes detailed descriptions of every awarded patent, and equivalent patent archives are available in other countries. The right to patent is sometimes referred to as the right to sue patent infringers. The idea is that patents are made public. Users of patented inventions must pay the patent holder a royalty. In return for a royalty, the patent holder refrains from taking legal action against the user.

When a patent or a copyright has expired, the work falls into the public domain and can be used freely. Many patent holders have been ruined by poor timing. Patent holders need to recoup their investment and earn all their profits within a twenty year window. When a patented invention requires twenty years or more to develop a market, the patent holder cannot profit from his work, unless he sells or licenses his patent during the patent's life. Likewise, patent holders may not profit if the practical implementation of their invention requires a second technological advance, that comes twenty years later.

A fine example of a patent issued before its time is the Lamarr/Antheil patent for Frequency Hopping Spread Spectrum (U.S. patent 2,292,387, 1942), issued to Hedy Lamarr and George Antheil. Circa WWII, Hedy Lamarr was a glamorous actress, and George Antheil was a Hollywood music composer. The two came up with and idea for secretly passing messages by jumping a signal from frequency to frequency, giving it the appearance of noise to enemy interceptors. When the sender and the receiver change frequencies simultaneously, the message can be retrieved. Their patent preceded the technology required to implement the idea. Today, decades after the patent expired, spread spectrum radio uses the Lamarr/Antheil technique. In a symbolic gesture, Wi-LAN, a telecommunications firm, purchased the original patent as an historical document, for an undisclosed amount. This was the only income that Hedy Lamarr and George Antheil received from their patent.

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

INTELLECTUAL PROPERTY 1

2010-02-17T04:46:00.000-08:00

This is the first in a multi-part blog on the topic of INTELLECTUAL PROPERTY in the sciences.

"Don't worry about people stealing your ideas. If your ideas are any good, you'll have to ram them down people's throats."

- Howard Aiken (American computer engineer and mathematician 1900-1973)

Intellectual property is the "dark matter" of the scientific world. We know that there's a lot of it, that it's everywhere, and that it has a strong effect on our lives, but it's all quite invisible to our senses.

When we think of intellectual property, we usually think in terms of patents (for inventions and processes) and copyright (for literature). Patents are rights assigned to an inventor, for a specified interval, in exchange for disclosing his invention to the public. Patents probably came to us, like most great ideas, from the acient Greeks. In 500 B.C.E., the Greek colony Sybarus (in Southern Italy), gave inventors the exclusive rights to profit from their invention for a period of one year. The length of a patent grew over the centuries. In 1449 King Henry VI granted a 20-year patent to John Utynam, who brought colored glass-works to England. The holder of a patent collects royalties from those who use the patent. The term royalties carries the idea that money that would ordinarily go to the king is assigned to the patent holder.

The idea of copyright seems to descend from the settlement of sixth century Irish dispute over the copies of a book of psalms. King Diarmait reasoned, "To every cow belongs her calf, therefore to every book belongs its copy." Basically, copyright guarantees that a book's creator owns the copies. In the United Kingdom, modern copyright was enacted by the Statute of Anne (Copyright Act of 1709). Every nation extends copyright protection to authors. Today, copyright protection extends to the form and content of the text and images and does not apply to particular ideas that might be expressed in the copyrighted work. Copyright protection lasts much longer than patent protection. In the U.S., Copyright persists 70 years after the death of author, unless the author is a corporation, in which case, copyright extends 95 years from publication or 120 years from creation, whichever expires first. As in the case of patents, royalties are paid to the copyright holder, in lieu of the king.

Scientists have used and abused intellectual property protection. A legal and popular method of bypassing the patent system is through "trade secret." If nobody knew your secret, your exclusive use of the property could be leveraged to your financial advantage. Nobody understood the concept of trade secret better than the surgeon William Chamberlen. Circa 1570 Chamberlen invented or acquired the design of an improved delivery forceps (tongs with large curved grasping handles that can be pressed together with a scissors action). The forceps was highly profitable to William and to his heirs. His son Peter became the attending physician to Queen Anne, the wife of James I and to Queen Henrietta Maria, wife of Charles I. The forceps kept the Chamberlen family in riches for over a century. A descendant fell upon hard times and sold the secret of the forceps in 1720 to Dutch surgeons. The forceps monopoly was broken when several of the new owners published the secret. A largely apathetic world paid little notice until the highly influential William Smellie published his description of the improved model of the forceps, in 1750. Because an intellectual property was kept secret, the world was deprived of a life-saving medical advancement for approximately 180 years (1).

[1] Strathern P. A brief history of medicine from Hippocrates to gene therapy. Carroll and Graf Publishers, New York, pp 169-171, 2005.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

ARCHIVE FOR JULES BERMAN'S BLOG

2010-02-16T04:15:00.000-08:00

This blog, Specified Life, deals with the general topic of data specification (including data organization, data description, data retrieval and data sharing) in the life sciences and in medicine. Beyond data sharing issues, it covers a range of topics that might be of interest to biomedical researchers.

All of the blog entries have been collected and organized into a convenient web-linked archive.

- Jules J. Berman

REJECTION 11

2010-02-15T05:46:00.000-08:00

This is the last blog in this series on REJECTION in the sciences. I'd like to thank Trish Parnell, Hua, and several anonymous visitors who, over the past several weeks, left encouraging comments on the Specified Life blog site. If you've read through this 11-part series on rejection, your comments would be greatly appreciated.

To summarize the first ten blogs, scientists live in a pervasive culture of rejection. Recent and past history provides ample proof that good science is not exempted from the withering effects of rejection. It would also appear that scientific advancement is stagnating, despite access to unprecedented funding largesse and a huge workforce of highly trained scientists.

There are many factors that contribute to the slowdown in scientific progress. I've heard claims that we've already made most of the scientific discoveries that will ever be made; that there's not much left to do. I've also heard that we've shifted into a new era of collective intelligence (e.g., twitter, facebook, social networking via texting) that transcends outmoded notions of scientific advancement.

Nonetheless, I can't get past the notion that unrelenting, spirit-crushing rejections take their toll on our societal effort to advance science. Rejections follow a scientist throughout his/her career: disappointing SAT scores, rejections to college of choice, disappointing grades, rejections to graduate schools, scornful treatment in graduate school, cold reception of research ideas, rejection of manuscripts, lack of any peer response to published manuscripts (i.e., research papers that nobody bothers to read), grant rejections, tenure rejections, and so on.

Many scientists cope by taking the path of least rejection throughout their careers: non-innovative grant applications (grant reviewers tend to approve applications that they can easily grasp, for work that can be easily accomplished ), never challenging existing paradigms (i.e., the paradigms championed by grant reviewers), big-budget research programs (favored by tenure committees), narrow, incremental advances (less likely to be rejected by reviewers), delegating lab work and paper authorship to post-docs (let somebody else get rejected), demise of the single author research paper (dozens of co-authors decrease rejection rate).

Maybe we should re-evaluate the way that scientists treat one another. Maybe there is a workable alternative to the rejection-based culture that permeates scientific life.

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

REJECTION 10

2010-02-14T07:04:00.000-08:00

This is the tenth in a multi-part blog on the topic of REJECTION in the sciences.

Among the many shortcomings of modern science, cancer research heads the list. Scientists tell us that they are making great advances in the treatment of cancer. Anyone can see that this is not so. The total U.S. age-adjusted cancer death rate today is about where it was 60 years ago (1). Though deaths from some types of cancer have dropped, these drops have been offset by the rise in other cancers. Of the cancers that have dropped the most: stomach cancer, cancer of the uterine cervix, and (most recently) lung cancer in men, improved mortality is due to a drop in cancer incidence, not due to any progress in treating advanced cancers. The reduced incidence of stomach cancer is generally credited to refrigeration and improved methods of food preservation. The drop in cervical cancer has been due to effective Pap smear screening for precancerous lesions (small lesions that precede the development of invasive cancer). When uterine precancers are excised, the cancer never develops. Further reduction in deaths from uterine cancer will probably result from population-wide inoculations with the new HPV vaccine; an effective measure that bypasses the need to treat advanced cancers. With the exception of curing a few types of rare tumors, cancer research has yielded none of the dramatic advances seen in the 1950s, with diseases such as polio and tuberculosis.

Cancer death rates that have increased since 1950 include: esophageal cancer, liver cancer, pancreatic cancer, lung cancer, melanoma, kidney cancer, brain cancer, non-Hodgkins lymphoma, and multiple myeloma. The list includes some of the most common types of cancer. If cancer research were effective, we would expect to have ways of preventing the rise in incidence of these common cancers.

Over the decades, clever cancer researchers have discovered a successful strategy for attracting millions and millions of dollars of research funding for diseases that they cannot cure. Each year, they point to the number of people who will die from cancer, and they say, that cancer is a terrible disease, causing untold suffering, and killing hundreds of thousands of Americans each year. Certainly, they argue, research to cure this dreadful disease must be fully funded. They fail to mention that the reason that hundreds of thousands of people die each year from cancer is that prior funding efforts failed to deliver a cure. Every year, the bulk of cancer funding is awarded to the very same institutions and laboratories that failed to produce a reduction in the cancer death rate.

If you speak to any cancer researcher, he will tell you that we have made great advances in understanding cancer genetics: the mutations in DNA that contribute to the development of cancers. What they do not say is that all of the advances in our understanding of cancer genetics come in the form of bad news. We now know, after billions of dollars of funding, that cancer cells are remarkably complex, often containing thousands of genetic alterations. No two genetically complex cancers are characterized by the same set of mutations, and no two tissue samples from any one cancer will be genetically identical. The complexity of cancer far outstrips our ability to characterize the alterations in a cancer cell. Consequently, it is highly unlikely that any single drug will correct all of the genetic changes in the cells of advanced cancers. Breakthroughs in cancer genetics have taught us that it will be difficult to develop a cure for the advanced common cancers, any time soon. You won't hear this from funded cancer researchers; nobody wants to kill the goose that lays the golden egg.

[1] Fifty-six Year trends in U.S. cancer death rates. SEER Cancer Statistics Review 1975-2005 National Cancer Institute. http://seer.cancer.gov/csr/1975_2005/results_merged/topic_historical_mort_trends.pdf, viewed October 12, 2009.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words: history of science , specified life blog , Jules J Berman PhD, MD

REJECTION 9

2010-02-13T04:10:00.000-08:00

This is the ninth in a multi-part blog on the topic of REJECTION in the sciences.

If the rate of scientific accomplishment is dependent upon the number of scientists on the job, you would expect that that rate of scientific accomplishment would be accelerating, not decelerating. According to the National Science Foundation, 18,052 science and engineering doctoral degrees were awarded in the U.S., in 1970. By 1997, that number had risen to 26,847, nearly a 50% increase in the annual production of the highest level scientists (1). The growing work force of scientists failed to advance science very much, but it was not for lack of funds. In 1953, according to the National Science Foundation, the total U.S. expenditures on research and development was $5.16 billion, expressed in current dollar values. In 1998, that number has risen to $227.173 billion, greater than a 40-fold increase in research and development spending (1).

Unproductive scientists always promise a breakthrough just around the corner. What else would you expect them to say? Humans live in hope, but funding agencies are expected to calculate the future based on measurements of past performance. The U.S. Department of Health and Human Services has published a sobering document, entitled, "Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. (2) " The authors note that fewer and fewer new medicines and medical devices are reaching the Food and Drug Administration. Significant advances in genomics, proteomics and nanotechnology have not led to significant advances in the treatment of diseases. Extrapolating from the level of scientific progress in the past half century, there's not much reason to expect great improvements in the next 50 years. The last quarter of the 20th century has been described as the "era of Brownian motion in health care" (3).

Meanwhile, science has become irrelevant for many people (4). A growing number of Americans (perhaps the majority), do not believe in global warming, do not believe in evolution, and do not believe that vaccines are safe and effective. A large number of people, without much evidence to support their fears, believe that vaccines cause autism, that water fluoridation is harmful, and that AIDS was invented by government scientists as a genocidal agent to be used against black populations. These days, scientists are met with suspicion, if not outright hostility, by a large percentage of the world.

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[1] National Science Board, Science & Engineering Indicators - 2000. Arlington, VA: National Science Foundation, 2000 (NSB-00-1).

[2] Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. U.S. Department of Health and Human Services, Food and Drug Administration, 2004. http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html

[3] Crossing the Quality Chasm: A New Health System for the 21st Century. Quality of Health Care in America Committee, editors. Institute of Medicine, Washington, DC., 2001.

[4] Furedi F. Downsizing the Status of Science. The Scientist volume 18, Issue 21, Number 10, Nov. 8, 2004.

© 2010 Jules Berman

key words:informatics, Jules J Berman PhD, MD

REJECTION 8

2010-02-12T03:56:00.001-08:00

This is the eigth in a series of blogs on the topic of REJECTION

When you watch a movie circa 1960, and you look at their streets and houses, and furniture, and clothing, do you see any differences between then and now? Not many. Basically, the scientific advances that shaped the world today were discovered prior to 1960. The only visible difference between people then and people now is personal appearance. The 1969 man in the street was trimmer, and better groomed.

What did we have in 1960? We had home television (1947), transistors (1948), commercial jets (1949), computers (Univac, 1951), nuclear bombs (fission , fusion in 1952), solar cells (1954), fission reactors (1954), satellites orbiting the earth (Sputnik I, 1957), integrated circuits (1958), photocopying (1958), probes on the moon (Lunik II, 1959), practical business computers (1959), lasers (1960).

These engineering and scientific advancements pale in comparison to the advances in medicine that occurred by 1960. We had the basic principles of metabolism, including the chemistry and functions of vitamins; the activity of the hormone system (including the use of insulin to treat diabetes and dietary methods to prevent goiter), the methodology to develop antibiotics and to use them effectively to treat syphilis, gonorrhea, and the most common bacterial diseases. We had effective vaccines that protected us from deadly viruses, such as smallpox, that had killed millions of people throughout human history. Sterile surgical technique was practiced, bringing a precipitous drop in maternal post-partum deaths. We could provide safe blood transfusions, using A,B,O compatibility testing (1900). X-ray imaging had improve medical diagnosis. Disease prevention was a practical field of medical science, bringing methods to prevent a wide range of common diseases using a clean water supply and improved waste management; and safe methods to preserve food, such as canning, refrigeration, and freezing. In 1941, Papanicolaou introduced the smear technique to screen for precancerous cervical lesions, resulting in a 70% drop in the death rate from uterine cervical cancer in populations that implemented screening. By 1947, we had overwhelming epidemiologic evidence that cigarettes caused lung cancer.

When we entered 1950, Linus Pauling had essentially invented the field of molecular genetics by demonstrating a single amino acid mutation accounting for the the defective gene responsible for sickle cell anemia. In 1950 Chargoff discovered base complementarity in DNA. Also, in 1950, Arthur Vineburg routed an internal mammary artery, in place, to vascularize the heart. In 1951, fluoridation was introduced, greatly reducing dental disease. Then came isoniazid, the drug that virtually erased tuberculosis (1952). Also, in 1952, Harold Hopkins designed the fibroscope, heralding fiberoptic endoscopy. In 1953, Watson and Crick showed that DNA was composed of a double helix chain of complementary nucleotides encoding human genes. John Gibbon performed the first open heart surgery using a cardiopulmonary bypass machine (1953), and D.W. Gordon Murray used arterial grafts to replace the left anterior descending coronary artery (the coronary artery bypass graft). Oral contraceptives (birth control pills) were invented in 1954. That same year, Salk developed an effective killed vaccine for polio, followed just three years later with Sabin's live polio vaccine. Thus, in the 1950s, the two most dreadful scourges of developed countries, tuberculosis and polio, were virtually eradicated.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words: history of science, history of medicine, Jules J Berman PhD, MD

REJECTION 7

2010-02-11T04:02:00.000-08:00

This is the seventh in a series of blogs on the topic of REJECTION

"Intellectuals can tell themselves anything, sell themselves any bill of goods, which is why they were so often patsies for the ruling classes in nineteenth-century France and England, or twentieth-century Russia and America." - Lillian Hellman

It is often easier to believe a bad idea than a good idea. Bad ideas can be carefully designed to provide people with something they want to believe, unconstrained by reality.

Canals on Mars - Well into the twentieth century, scientists believed that there were canals on the planet Mars. Optical lines criss-crossing Mars were first observed by the Italian astronomer Giovanni Virginio Schiaparelli (1835–1910), in 1877. The observations stirred the public's imagination. Here was irrefutable evidence that a riparian civilization thrived on an alien-created canal system. We now know that the canals are an optical illusion, and do not represent any physical structure, water-filled or otherwise.

Status thymicolymphaticus - In the late nineteenth and early early twentieth centuries, doctors attributed childhood asthma and crib death (now known as sudden infant death syndrome) to enlarged thymus glands; they named the condition status thymicolymphaticus. In the 1920s, doctors radiated enlarged thymus gland of children as a preventive measure against crib death. It is estimated that about 20,000 - 30,000 people died from cancers produced by "therapeutic" radiation for this and other real or imagined disorders (1). We now know that status thymicolymphaticus is not a disease. Some children are born with larger thymus glands than other children, but no disease syndrome results from this anatomic disparity.

Stomach cancer produced by a worm - The 1926 Nobel prize for medicine went to Johannes A.G. Fibiger, who discovered a cause of cancer that that was eventually shown to be an artifact. According to Fibiger, a larval parasite caused stomach cancer in rats. Years later, scientists concluded that the tumors must have been caused by some other factors. Humiliated by their mistake, the Nobel Assembly waited four decades before they awarded the prize to another cancer researcher.

Frontal lobotomies - The frontal lobotomy was invented by Dr. Antonio de Egas Moniz in 1935 and popularized by Dr. Walter Freeman. For the procedure, Dr. Freeman applied a some local anesthetic, then inserted a gold-plated ice-pick just above the eyeball, and shoved it into the brain. The Doctor gingerly scraped the ice-pick through the frontal lobe, the presumed site of unrestrained emotions. Apathy and mental impairment often followed the procedure, and this was considered an improvement in most cases. Dr Freeman performed over 3,500 procedures; his disciples, performed about 40,000 more. The procedure has since been largely discredited, but not before Dr. Moniz received the 1945 Nobel prize in medicine for the dubious gift of frontal lobotomy.

Polywater - In the 1960s, soviet researcher Nikolai Fedyakin introduced polywater to the world; a polymerized form of water with a higher boiling point, lower freezing point, and higher viscosity than ordinary water. Other workers seemed able to repeat and extend Fedyakin's early observations. After many years of wasted effort, the scientific community finally conceded that polywater experiments were unrepeatable and that polywater does not exist.

Phrenology - Phrenology is the pseudoscience that predicts personality by inspecting the surface features of a person's skull. Phrenology was invented by the German physician, Franz Joseph Gall, in 1796, but was practiced well into the twentieth century. Nineteenth and twentieth century discoveries in brain science helped to establish a theoretical basis for phrenology by assigning specific cognitive functions to specific anatomic locations of the brain. Modern phrenologists reasoned that personality, the aggregate expression of many different brain functions, could be predicted by measuring protrusions of the skull overlying brain regions enlarged by high levels of activity or depressed by hypofunctioning regions. It was a nice idea, but completely wrong.

Cold fusion - Stars are fueled by fusion. For decades, physicists have been trying to develop a controlled fusion reactor that would provide unlimited energy, from hydrogen; without much success. In 1989 Martin Fleischmann and Stanley Pons held a press conference to announce that they had produced fusion in a tabletop experiment involving electrolysis of heavy water and a palladium electrode. Fleischmann and Pons did not fully specify the theoretical basis for their success, but physicists throughout the world were only too happy to oblige. Several laboratories reported that the observations of Fleischmann and Pons were repeatable! Meetings, seminars, and workshops were hastily assembled and attended by the top minds in physics. Lectures were delivered explaining how cold fusion worked. As time went by, despite early declarations of success, other laboratories could not achieve cold fusion. The theoretical works explaining cold fusion have been discredited. The long, frustrating quest for free, unlimited fusion energy continues.

The lesson is clear, scientists, like non-scientists, accept what they want to believe; and reject what they want don't want to believe.

[1] Jacobs MT, Frush DP, Donnelly LF. The right place at the wrong time: historical perspective of the relation of the thymus gland and pediatric radiology. Radiology 210:11-1, 1999.

© 2010 Jules Berman

key words: medical history, science history, Jules J Berman PhD, MD

REJECTION 6

2010-02-10T04:12:00.000-08:00

This is the sixth in a series of blogs on the topic of REJECTION

Sudden Infant Death Syndrome (SIDS) is a disease feared by every infant's parent. Typically, the baby is left to sleep. When the parents check the baby, they find that it is dead. Moments of temporary sleep apnea are common in babies and adults. In SIDS, it is as though the process of breathing simply stops, and does not resume. Autopsies on SIDS patients have never shown any consistent conditions in organs that may have caused death. Many bright medical researchers have devoted their careers to SIDS. The obvious suspects (respiratory controls in the brain and lungs) were examined intensely, in hundreds of studies, extending over decades, with little to show for the effort. These expensive but fruitless research efforts were conducted in a time when an effective method to prevent SIDS was known (and ignored).

Simple observations of the circumstances made at the death scene were shown to have enormous import. As early as the 1940s, the observation was made that many victims of SIDS were found in the prone position on pliant bedding, often in soft layers of bedclothes (1). Similar observations were made again and again, and by the 1970s, people began to wonder whether babies could breathe adequately under these conditions (2). New Zealanders are credited with showing the drop in infant mortality when achieved with a supine sleep position on a firm mattress. Numerous population studies have confirmed these observations. Currently, the "back-to-sleep" campaign is a worldwide effort whose goal is to spread awareness of a new breakthrough in SIDS prevention, discovered more than six decades ago.

Scientists will reject observations that challenge their belief systems. No story better exemplifies this than the tale of seventeen centuries of night-blindness experienced by European astronomers. Hipparchus was an early Greek astronomer who correctly calculated the distance from the earth to the moon (250,000 miles) 150 B.C. Some years later, in 134 B.C. Hipparchus looked in the sky and saw a new star. He was certain that the star was new because he had just finished mapping the known heavens when the new star appeared. The next European to see a new star in the sky was Tycho Brahe, in 1572. The curious thing about this dark interim is that nova occur frequently. Several dozen nova, visible from earth with the naked eye, occur each year (3). Moreover, in the year 1054, the Crab Supernova was recorded by Chinese, Japanese, Persian/Arab and Indian astronomers. The Europeans, who were literally in the dark ages, missed the event.

The reason that no nova were observed in seventeen centuries is very simple. The Europeans believed in the fixed heavens. If you believe that the heavens are the same now as they were when the universe was created, and will stay the same until the universe ends, then you will not see new stars twinkling in the night sky.

For many, the purpose of science is to confirm a set of preconceptions. When something new comes along that contradicts a previously held belief, it is ignored or rejected.

[1] Abramson H. Accidental mechanical suffocation in infants. J Pediatr 25:404-413, 1944.

[2] Vennemann MM, Fischer D, Jorch G, Bajanowski T. Prevention of sudden infant death syndrome (SIDS) due to an active health monitoring system 20 years prior to the public "back-to-sleep-campaigns." Arch Dis Child. Jan 6, 2006.

[3] Baade W. Zwicky F. On Super-Novae. PNAS (U.S. Proceedings of the National Academy of Sciences) 20:254-259, 1934.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words: medical history, Jules J Berman PhD, MD

REJECTION 5

2010-02-09T04:29:00.000-08:00

This is the fifth in a series of blogs on the topic of REJECTION

Reality is the thing that can kill you whether you believe in it or not. For centuries, navies refused to believe that citrus can cure scurvy (Vitamin C deficiency disease). Most animals can synthesize ascorbic acid (Vitamin C), and do not require a dietary source. Humans and guinea must acquire the vitamin in their diets, or they will die. Without Vitamin C, the body cannot properly synthesize collagen, the fibrous protein that braces connective tissues in animals. The first known large epidemic of Vitamin C deficiency occurred in 1497, when Vasco da Gama sailed from Lisbon to Calcutta. About three-fifths of his crew died. Prior to the age of European sea explorations, voyages were shorter, or they involved foraging for food along the way. The Europeans set larder on their ocean-going ships with provisions for the full journey. Unfortunately, their stored foods lacked sufficient Vitamin C, resulting in death by scurvy, a particularly nasty condition marked by a general collapse of the body's structural integrity, often ending with stroke due to vascular hemorrhage.

In 1593, Sir Richard Hawkins showed that scurvy could be prevented and cured by eating oranges and lemons. If citrus fruits were not to a sailor's liking, a type of salad cress was shown, in 1597, to work just as well. It would seem that by 1597, one century after the medical disaster aboard Vasco da Gama's ship, scurvy was eliminated as a threat to naval adventurers.

Not so. Soon after the cure for scurvy was found, it was abandoned. Scurvy deaths re-emerged on ships. It was up to James Lind to re-discover, in 1747, that citrus prevented and cured scurvy. Lind's re-discovery was lost on some explorers. A century later, in 1848, the ships Erebus and Terror, while navigating through the Northwest Passage, became trapped in ice. Citrus was absent from their provisions. The crew died of scurvy.

Asepsis is another idea whose time has come and gone and come and gone. The basic theory of asepsis is simple; keep wounds clean, and avoid contaminating anyone with infected materials from other persons, and everyone stands to live a lot longer. The limitation of asepsis, as a medical procedure, are three-fold: 1) humans are dirty; 2)humans are lazy, and 3) nobody in human history has ever been paid to wash his hands.

Asepsis has been used for millenia. Hippocrates (460-377 BC) irrigated wounds with wine or boiled water. Galen (130 - 200 A.D.) knew enough to boil his surgical instruments. In 1266 A.D., Todorico Borgognoni taught aseptic wound treatment.

In 1847, Ignaz Phillipp Semmelweis was working at the Vienna General Hospital's maternity clinic, on a three year contract. Through much of human history, mothers commonly died of infections arising in the days immediately following childbirth. As many as forty percent of mothers contracted and died from puerperal fever, also known as childbed fever. The cause of these deaths would have been obvious to Hippocrates, Galen or Borgognoni. Doctors scurried between sick patients and healthy patients without washing their hands. Semmelweis saw the problem, and contrived an experiment; Doctors and medical students would wash their hands between patient examinations. The staff doctors were skeptical, but they agreed to humor Semmelweis, if only to prove him wrong. The death rate from puerperal fever dropped precipitously. Unfortunately for the women at Vienna General, staff doctors abandoned the hand washing ritual when Semmelweis's contract expired. Hand washing was an annoying diversion. All told, they preferred the filth and the high death toll to the incessant hygienic obligations. The patients supported the staff. They didn't like to see doctors washing their hands after touching them; it made them feel dirty.

Source: Garrison FH. History of medicine.
WB Saunders, Philadelphia, 1921.

Aseptic techniques, including hand washing, have a firm scientific footing. Today, nobody doubts the effectiveness of a clean environment for patients, but hand washing is still a lot of work. Doctors, even in the best hospitals, neglect washing their hands (1).

Aseptic technique is an example of a great idea that is discovered over and over again. The medical community pushes each new re-discovery of aseptic technique back into obscurity. Why? For physicians, not hand washing is the perfect crime. It can kill as easily as a bullet, but no doctor has ever been punished for having dirty hands. As Dr. Robert M. Wachter, an expert in patient safety has said, "I can lose my hospital privileges if I fail to sign a dictated discharge summary or operative note. But if I don't clean my hands for the next 10 years, nothing will happen to me" (2). When hand washing becomes a billable procedure, its scientific value will be re-discovered, again.

[1] Lipsett PA, Swoboda SM. Handwashing compliance depends on professional status. Surg Infect (Larchmt) 2(3):241-245, 2001. Comment. In this study, physicians washed their hands much less frequently than nurses. Surprise!

[2] Chen PW. Holding doctors accountable for medical errors. The New York Times December 17, 2009.

© 2010 Jules Berman

key words:informatics, Jules J Berman PhD, MD

REJECTION 4

2010-02-08T03:53:00.000-08:00

This is the fourth in a series of blogs on the topic of REJECTION

Back in 1668, just as Redi was trying to convince his colleagues that living organisms cannot generate from nothing, Richard Lower was putting the finishing touches on his Tractatus De Corde: Item De Motu Et Colore Sanguinis. Lower demonstrated experimentally that venous blood pumped from the heart, into the lungs, is transformed (from venous dark red, to arterial bright red) by aeration and returned to the heart, where arterial blood is pumped to the peripheral circulation. This seems obvious today; barely worthy of explanation. We need to be reminded that for about 1500 years, all medical thought in Europe was dominated by one honored physician whose legacy of medical dogma was held sacred.

Galen (129 - 199 C.E.) was a Greek physician who lived in Rome, and Pergamum (Turkey), and retired early to live a life of scholarship. He wrote many books, including "On Prognosis," (177 C.E.), and produced a total of about 3 million bon mots before he died (1). For the subsequent 1500 years, his words were accepted on blind faith by virtually all European physicians. To reject Galen was a type of heresy, that almost always resulted in professional ostracism.

Galen, great as he was, labored under the somewhat limited scope of second century science. Some of his most far-reaching thoughts fell into the realm of superstition, not science. For example, Galen believed that blood was embued with natural spirts by the liver, and vital spirits by the heart. Furthermore, Galen believed that blood moved through the septum of the heart through invisible pores. The concept of a closed circulation was unknown to Galen.

Probably every child who rides an escalator must wonder where the steps go when they reach the top. Mysteriously, they slink under the floor, and drop off into a hidden chamber. Meanwhile, another mysterious process creates new steps that emerge from the floor of the elevator, and rise upwards. The idea of a continuous belt of stairs seldom catches the imagination of very young children, who prefer magical beliefs over mundane observations. Basically, medieval physicians accepted Galen's magic stairs version of blood circulation. Blood was constantly replaced by the liver at a rate that equaled its issuance through invisible pores in the heart. It was just fantastic.

Anatomists who gave any thought to Galen's theory of blood circulation knew that Galen could not be correct. Still, to doubt Galen was clearly unacceptable. In frustration, Henri de Mondeville, the author of Cyrurgia (1312), an early textbook of surgery, wrote, "God did not exhaust all His creative power in making Galen (1)." Andreas Vesalius (1514 - 1564) published "De Fabrica Humani Corporis," in 1543. This book provided a detailed description of human anatomy that corrected some of the misconceptions and superstitions left by Galen. Vesalius' closest friends turned against him. Others in his profession condemned, mocked, or ignored his work. Die-hard Galen fans insisted that any discrepancies between Galen's second century human anatomy, and Vesalius' sixteenth century observations were due to naturally occurring modifications in the human condition. Sylvius, Vesalius' teacher in Paris, grumbled, "Man had changed but not for the better (1)." Vesalius departed Venice, and died alone, impoverished, ridiculed by his colleagues, shipwrecked on the Island of Zante (Zakynthos) (1).

Ten years later, Servetus (1509 - 1553) published Restitutio Christianismi, in which he noted that the pulmonary vessels deliver blood to the heart, after the blood has mixed with air in the lungs. That same year, Servetus was burned at the stake (along with most of the copies of his book) by Calvin for a poorly written sentence that seemed heretical at the time.

By 1628, the world was ready to take a second look at some of Galen's opinions. In this year, William Harvey (1578 - 1657) published De Motu Cordis, describing the circulation of blood from heart to lungs and back, and from the heart to the periphery and back. This brings us finally to 1669, when Richard Lowers' publication synthesized our current understanding of peripheral and pulmonary circulations with intrapulmonary transformation of blood, through aeration.

The Italians credit Andrea Cesalpino (1524 - 1603), a professor of medicine at Pisa, with discovering the closed heart-lung circulation prior to Harvey. The point is moot. In 1242 C.E., the Arabic polymath Ibn an-Nafis (1213 - 1288) described the heart-lung role in circulation and aeration; four centuries before either Harvey or Cesalpino. At the time, nobody in Europe cared to listen. Again we learn that new ideas will be rejected if they contradict cherished beliefs.

As Galen dictated medieval medicine, so did Ptolemy dictate medieval science. Claudius Ptolemaeus (90 - 168), known in English as Ptolemy, was contemporary with Claudius Galenus (129 - 199), known in English as Galen. Like Galen, much of what he said was wise and true. Some of what he said was false. According to Ptolemy, the earth sits in the center of the universe, and the size of the universe has a radius equal to 20,000 times the radius of the earth. Wrong or right, European scientists held his opinions correct and inviolate for nearly fifteen centuries.

[1] Garrison FH. History of medicine. WB Saunders, Philadelphia, 1921.
© 2010 Jules Berman

key words:informatics, Jules J Berman PhD, MD

REJECTION 3

2010-02-07T04:43:00.000-08:00

This is the third in a series of blogs on the topic of REJECTION

In 1668, the world was modernized, in many ways. We had the fundamentals of cryptography (Viete, 1589), Pi calculated to 20 decimal places (Ludolf, 1596), logarithms (Napier, 1624), Fermat's last theorem (1637), the fundamentals of probability (Pascal, 1654), and the ability to diagnose cancers by pathologic examination (Malpighi, 1659). Differential equations were understood (1662), and the last details of integral calculus were being written, independently, by Newton and Leibnitz. Despite all of these scientific advancements, the world believed that the life of very small organisms arose spontaneously, from thin air, or possibly from inanimate particles of dirt. Otherwise rational intellects were comfortable with magical thinking, and believed that life could be explained by postulating forces acting in a realm beyond human perception.

Francesco Redi, in 1668, designed an experiment to test whether maggots arose through spontaneous generation. He incubated meat in flasks; some covered to stop the entry of flies, and others left uncovered, permitting flies to enter. After a some time, he examined the meat in both sets of flasks. Only the meat from the open flasks contained maggots. Redi correctly concluded that maggots do not generate spontaneously from dead meat. We now know that maggots generate from tiny eggs laid by flies.

Redi's experiment had very little influence on the prevailing belief systems. Seventy-two years later, John Turberville Needham conducted his own meat-related test for spontaneous generation. He heated mutton broth in a closed container, and examined the contents a few days later. The container swarmed with micro-organisms, proving, to the satisfaction of many, that micro-organisms arise by spontaneous generation. In retrospect, we can assume that the broth was not heated sufficiently to kill all of the organisms initially present in the container, or that the container was not sufficiently closed to exclude the entry of micro-organisms. For some time, though, Needham's experiment successfully vanquished many doubts regarding the validity of spontaneous generation.

In 1768, a full century after Redi's experiments, Lazzaro Spallanzani repeated Needham's experiment, this time boiling the broth for forty-five minutes. No organisms grew in the closed container.

Spallanzani's experiment should have put the kibosh on spontaneous generation, but it did not. Ninety-two years passed before Pasteur revisited the issue. In 1860, scientists knew enough about the biology of life to infer that spontaneous generation was a needless and absurd theory. At that time, Virchow, a highly influential pathologist, argued that cells arise from other cells, through cell division. Pasteur is often credited with settling any remaining beliefs in spontaneous generation. In 1860, Pasteur showed that dust particles in air carried micro-organisms. If boiled meat is exposed to purified air (without dust particles), bacterial growth does not occur.

Nearly two centuries were required to convince the world that Redi's experiment, disproving spontaneous generation, was valid. People reject ideas that undermine their chosen belief systems, even when the science is valid.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words: history of science, Jules J Berman PhD, MD

REJECTION 2

2010-02-06T05:39:00.000-08:00

This is the second in a series of blogs on the topic of REJECTION

Sometimes, the best works are found amongst the rejected efforts. In mid-nineteenth century France, rejected artists rose far above the level of orthodox painters. The story goes that the Salon de Paris, the official exhibition of art sponsored by the Academie des Beaux-Arts, rejected works by Monet, Manet, Pissarro, Cezanne, and many others whose concept of art conflicted with long-prevailing sensibilities. Complaints reached the ears of Napoleon III, who allowed rejected words to be displayed in a Salon de Refuses. These exhibitions of selected works are credited with the rise of impressionism. Today, the term "salon des refuses" refers to any exhibit of works that were rejected by a juried show.

Mathematicians have their equivalent of a Salon de Refuses. Today, mathematicians can publish their rejected papers in Rejecta Mathematica, available online at: http://math.rejecta.org/about-rejecta-mathematica. Sometimes, great ideas are not rejected; they're just ignored. Hundreds of years can pass while a deserving idea is discovered, lost, re-discovered, lost again, and so on.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words:informatics, Jules J. Berman Ph.D., M.D., history of science

REJECTION 1

2010-02-05T04:41:00.000-08:00

This is the first in a series of blogs on the topic of REJECTION

"That it will ever come into general use, notwithstanding its value, is extremely doubtful because its beneficial application requires much time and gives a good bit of trouble, both to the patient and to the practitioner because its hue and character are foreign and opposed to all our habits and associations."

- The London Times, 1834, reviewing a new medical device, the Stethoscope

The life of a scientist is full of rejection. Rejection is a judgment from your peer community that your work has no merit and should not be rewarded, or even acknowledged. It is an official indictment against your work, and your self-image. A few creative persons seem to thrive on rejection; most whither.

Perhaps nobody has been as deeply ignored, during his short, obscure lifetime, than Vincent Van Gogh (1853 - 1890) . In the last decade of his short life, he produced over 2,000 paintings. He just kept getting better and better at his craft, producing many of his most beloved works in the last two years of his life. Though he had connections to a successful art dealer (his brother Theo), his paintings had no buyers. Rejected and depressed, he took his own life.

John Milton (1608 - 1674) received only 5 pounds, from his publisher, for the manuscript and the copyright for Paradise Lost. The epic poem did not achieve critical acclaim until 30 years after Milton's death.

Herman Melville (1819 - 1891) finished Moby Dick in 1851. He considered it to be his greatest novel, but reviewers disagreed. His publisher printed a small number of first edition books; most went unsold. Melville's career delined after disappointing sales for Moby Dick. Finding publishers for his subsequent works was difficult. Melville was forced to take a job as a customs inspector to make ends meet. He died in almost total obscurity, leaving behind the unpublished manuscript of his last work, Billy Budd. Today, Moby Dick is considered one of America's greatest novels.

From the 1930s to 1960, publishers had little or no interest in Louis Zukofsky (1904 - 1978); he wrote with virtually no audience. His book Barely and Widely sold only 26 copies two months after release. Today, Zukofsky is considered to be one of the greatest poets of the 20th century (1).

David Oshinsky wrote an essay on book rejections discovered in the Alfred A. Knopf, Inc., archive (2). In 1950, Alfred A. Knopf Inc. rejected The Diary of a Young Girl, by Anne Frank. The publisher found the work dull and "a dreary record of typical family bickering, petty annoyances and adolescent emotions." After 15 other publishers passed on the title, Doubleday published the book (over 30 million copies sold). In the same essay, Oshinsky reported that Pearl Buck's The Good Earth was rejected by Knopf (Americans not interested in China), as was George Orwell's Animal Farm, (animal stories don't sell). Also rejected was Isaac Bashevis Singer (rich Jews again), and Sylvia Plath (not enough talent).

Art and literature are subject to personal taste. Science is tethered to objective reality. You would expect that scientific discoveries would be greeted with immediate acceptance because legitimate scientific assertions can be tested and verified. Such is not the case, and the history of discovery is filled with sad stories of great works rejected. A short chronology of scientific rejection follows (3):

480 B.C.E. Democritus (460 B.C.E. - 370 B.C.E.) invents atoms, a theory supplanted by the much more popular "Earth, air, fire, and water" school.

350 B.C. Aristotle (384 B.C.E. - 322 B.C.E.) recognizes that dolphins are mammals. The rest of the world disagrees, classifying dolphins as fish. After two thousand years of derisive laughter, the world eventually agrees with Aristotle.

325 B.C.E. Pytheas (350 B.C.E. - 285 B.C.E.)sails from Greece to Iceland. Pytheas describe Atlantic tides (absent in the smaller Mediterranean sea). When he returned from his remarkable voyage, Pytheas described his voyages and his observations. Nobody believed him.

280 B.C.E. Aristarchus of Samos (310 B.C.E. - 230 B.C.E.) reasons that the sun is the center of the heavens.

240 B.C.E. Eratosthenes of Cyrene (276 B.C.E. - 195 B.C.E.) working in Alexandria, computes size of earth correctly. At the time, the preponderance of scientific opinion favored a flat earth, supported by a giant (Atlas) or possibly a turtle.

134 B.C.E. Hipparchus (190 B.C.E. - 120 B.C.E.) observes a newly appearing star (nova). The western world remains incredulous until Tycho Brahe's observation 1500 years later.

1705 Edmond Halley (1656 - 1742), calculates that his comet would return to the solar system in 1758. Nobody took him seriously, until 1758, when Halley's comet returned.

1747 James Lind (1716 - 1794) determines that citrus prevents scurvy. Takes another 50 years and hundreds, perhaps thousands, of deaths, before British navy listens.

1796 Edward Jenner (1749-1823), writes paper on smallpox vaccination; rejected. Forced to self-publish research results RstrbR.

1847 Ignaz Philipp Semmelweis (1818 - 1865) reduces rate of puerperal fever by hand-washing. Hand washing was soon abandoned by the hospital staff. Semmelweis eventually lost his sanity. To this day, many physicians and healthcare professionals neglect to wash their hands.

1884 Svante August Arrhenius (1859 - 1927) defends his PhD thesis on ionic dissociation. His professors thought it was all wrong, reluctantly passing him with the lowest possible qualifying grade. In 1903, the very same thesis earned Arrhenius the Nobel prize.

1869 One-armed civil war veteran John Wesley Powell (1834 - 1902) is denied federal funding to explore the Grand Canyon; his privately funded exploration is credited with many of the significant discoveries of the Colorado basin.

1892 Georg Ferdinand Ludwig Phillip Cantor (1845 - 1918) publishes the theory of transfinite numbers, to the immediate and vociferous condemnation of the religious, philosophical, and scientific communities. Mathematics, unlike the natural sciences, yields to logic. In 1904, the Royal Society bestowed its highest honor on Cantor.

1987 Fred Cohen, who introduced the term, "computer virus" in a 1984 paper, and who was one of the first scientists to predict the threat of computer viruses, asks the National Science Foundation for a grant to study countermeasures. His grant was denied; not of current interest (4).

[1] Scroggins M. The Poem of a Life: A biography of Louis Zukofsky. Shoemaker & Hoard, Washington, D.C., 2008

[2] Oshinsky D. No thanks, Mr. Nabokov. The New York Times September 9, 2007.

[3] Asimov I. Asimov's Chronology of Science and Discovery. Harper Collins, New York, 1994.

[4] Lemos R. Decades after creation, viruses defy cure. CNET News.com November 25, 2003.

© 2010 Jules Berman

key words:informatics, rejection, history of science, Jules J. Berman Ph.D., M.D.

One-way hash: Perl, Python, Ruby

2010-01-30T04:32:00.000-08:00

I have prepared short scripts , in Perl, Python, and Ruby, for implementing one-way hash operations. One-way hashes are extremely important in medical informatics. The following text is extracted from a public domain document that I wrote, in 2002 (1).

A one-way hash is an algorithm that transforms a string into another string is such a way that the original string cannot be calculated by operations on the hash value (hence the term "one-way" hash). Examples of public domain one-way hash algorithms are MD5 and SHA (Standard Hash Algorithm). These differ from encryption protocols that produce an output that can be decrypted by a second computation on the encrypted string.

The resultant one-way hash values for text strings consist of near-random strings of characters, and the length of the strings (e.g. the strength of the one-way hash) can be made arbitrarily long. Therefore name spaces for one-way hashes can be so large that the chance of hash collisions (two different names or identifiers hashing to the same value) is negligible. For the fussy among us, protocols can be implemented guaranteeing a data set free of hash-collisions, but such protocols may place restrictions upon the design of the data set (e.g. precluding the accrual of records to the data set after a certain moment)

In theory, one-way hashes can be used to anonymize patient records while still permitting researchers to accrue data over time to a specific patient' record. If a patient returns to the hospital and has an additional procedure performed, the record identifier, when hashed, will produce the same hash value held by the original data set record. The investigator simply adds the data to the "anonymous" data set record containing the same one-way hash value. Since no identifier in the experimental data set record can be used to link back to the patient, the requirements for anonymization, as stipulated in the E4 exemption are satisfied (vida supra).

There is no practical algorithm that can take an SHA hash and determine the name (or the social security number or the hospital identifier, or any combination of the above) that was used to produce the hash string. In France, the name-hashed files are merged with files from many different hospitals and used in epidemiologic research. They use the hash-codes to link patient-data across hospitals. Their methods have been registered with SCSSI (Service Central de la Securite des Systemes d'information).

Implementation of one-way hashes creates some practical problems. Attacks on one-way hash data may take the form of hashing a list of names and looking for matching hash values in the data set. This can be solved by encrypting the hash or by hashing a secret combination of identifier elements or both or keeping the hash value private (hidden). Issues arise related to the multiple ways that a person may be identified within a hospital system (Tom Peterson on Monday, Thomas Peterson on Tuesday), all resulting on inconsistent hashes on a single person. Resolving these problems is an interesting area for further research.

The scripts are available at:

http://www.julesberman.info/factoids/1wayname.htm

1. Berman JJ. Confidentiality for Medical Data Miners. Artificial Intelligence in Medicine 26:25-36, 2002.

- Jules Berman

key words: perl programming, ruby programming, python programming, jules j berman, md5, sha, 1-way hash, 1way hash, oneway hash, confidentiality, de-identification Jules J. Berman, Ph.D., M.D., jules j berman, Jules Berman, Ph.D., M.D.

Chronology of Earth Website Updated

2010-01-29T05:27:00.000-08:00

I've just updated the Chronology of Earth website. It now has about 500 entries, covering significant events of terran interest.

The site is available at:

http://www.julesberman.info/chronos.htm

If you spot any inaccuracies on the page, please let me know.

- © 2010 Jules Berman

key words: world timeline, world time line, terran chronology, terrestrial chronology, chronology of science, timeline of science, science events, history of science, medical history history of earth, science through history, science through the ages, science past and present, date of occurrence

Scripts for fetching and testing web pages

2010-01-28T03:56:00.000-08:00

Web pages are files (usually in HTML format) that reside on servers that accept HTTP requests from clients connected to the Internet. Browsers are software applications that send HTTP requests and display the received web pages. Using Perl, Python, or Ruby, you can automate HTTP requests. For each language, the easiest way to make an HTTP request is to use a module that comes bundled as a standard component of the language.

I've written very simple scripts, in Perl, Python, and Ruby, for fetching web files. The scripts, and an explanation of how they work, are available at:

http://www.julesberman.info/factoids/url_get.htm

Perl, Python and Ruby use their own external modules for HTTP transactions, and each language's module has its own peculiar syntax. Still, the basic operation is the same: your script initiates an HTTP request for a web file at a specific network address (the URL, or Uniform Resource Locator); a response is received; the web page is retrieved, if possible, and printed to the monitor. Otherwise, the response will contain some information indicating why the page could not be retrieved.

With a little effort, you can use these basic scripts to collect and examine a large number of web pages. With a little more effort, you can write your own spider software that searches for web addresses within web pages, and iteratively collects information from web pages within web pages.

© 2010 Jules J. Berman

key words: testing link, ruby programming, perl programming, python programming, bioinformatics, valid web page, web page is available, good http request, valid http request testing if web page exists, testing web links, jules berman, jules j berman, Ph.D., M.D.

Familial and Heritable Neoplasm Syndromes

2010-01-27T03:45:00.000-08:00

Yesterday, I revised my web page on familial and inherited neoplasm syndromes.

The former version of the page was a computer generated compilation of every OMIM (Online Mendelian Inheritance in Man) entry that contained the name of a cancer somewhere in the record.

The current version is a pared down list of about 230 conditions, that fall into one of three categories:

1. Familial cancer and neoplastic syndromes (i.e., affected individuals have an inherited predisposition to a particular set of neoplsms).

2. Mutations in the germ line (i.e., in every cell of the body), that predispose to neoplasia.

3. Inherited diseases or diseases with a heritable component that predispose to neoplasia.

The list is not limited to cancers, and includes benign tumors and hamartomas. The list is described in my book,

Neoplasms: Principles of Development and Diversity.

- © 2010 Jules J. Berman, Ph.D., M.D.

THYROID PRECANCER

2010-01-26T04:52:00.000-08:00

Most, if not all cancers are preceded by a precancerous lesion, which has a number of biologic and morphologic features that are different from the fully developed cancer. Precancers are much easier to treat than cancers. By treating precancers, we can prevent cancers from developing.

In prior blog posts, I have discussed the biological properties of the precancers. One of these properties is an observed co-occurrence with cancers. Basically, if a particular type of cancer arises from a precancer, you would expect to see some instances wherein the cancer co-occurs with the precancer (i.e., where the cancer can be seen adjacent to its precancer). Co-occurrence of precancer and cancer is rare because cancers overgrow and replace their precancers.

Papillary thyroid carcinoma accounts for about 80% of thyroid cancers diagnosed in the U.S. Until recently, little attention has been directed to the putative precursor lesion of this cancer. A recent paper by Cameselle-Teijeiro and associates described a case of papillary thyroid carcinoma adjacent to a focus of solid cell next hyperplasia (1). The authors microdissected both lesions and found the same BRAF mutation in the solid cell nests and in the adjacent cancer.

Their findings suggest that solid cell nest hyperplasia is the precancer lesion for the adjacent cancer.

In their case report, the particular type of papillary carcinoma was the follicular variant of papillary microcarcinoma. More research is necessary to answer the following questions:

1. Is their observation generalizable (i.e., can it be shown that solid cell nest hyperplasia is found in additional cases of thyroid carcinoma)?

2. Does solid cell nest hyperplasia have all of the defining properties of a precancer?

3. If so, for which thyroid cancers is solid cell nest hyperplasia the preacancer (i.e., is it the exclusive precancer for the follicular variant of papillary microcarcinoma, or is it the precancer of other types of cancers that arise from thyroid follicle cells)?

[1] Cameselle-Teijeiro J, Abdulkader I, P‚rez-Becerra R, V zquez-Boquete A, Alberte-Lista L, Ruiz-Ponte C, Forteza J, Sobrinho-Simoes M. BRAF mutation in solid cell nest hyperplasia associated with papillary thyroid carcinoma. A precursor lesion? Hum Pathol 40:1029-1035, 2009.

© 2010 Jules Berman

key words: papillary carcinoma of the thyroid, papillary carcinoma ofthyroid, thyroid cancer, precancer, thyroid precancer, informatics, jules j berman

COMPLEXITY 8

2010-01-25T03:44:00.000-08:00

This is the eighth and last post in a series on complexity in scientific research. The theme of this collection is that scientific progress, particularly in the realm of healthcare, has declined as a consequence of the high complexity in software and other technologies.

-- POST BEGINS HERE --

The U.S. military enjoys working on huge, complex projects, and the scientists involved in these projects will go to extremes to keep failed efforts alive. A fine example of a long-running military research effort is the V-22 Osprey, affectionately renamed "The Grand Ole Osprey." In the history of engineering, there have been many attempts at dual-purposed devices: automobiles that can sprout wings and fly, boats that come ashore and covert to automobiles, washing machines that also dry clothes, houses on wheels that can be towed, behind a car. All of these devices exist, but they have not replaced their single-purposed components. It's very difficult to engineer a reliable and inexpensive composite device when each component is complex.

Circa 1980, the Pentagon decided it needed a hybrid aircraft that could take-off and land like a helicopter, but fly like a plane. Thus began the long, expensive and disappointing sago of the V-22 Osprey. After more than a quarter century in the making, and $16 billion dollars spent, the U.S. government has not created a safe and dependable vertical take-off airplane. Through the years, multiple crashes of the Osprey have resulted in 30 deaths.

In January, 2001, the New York times reported that a Marine Lieutenant-Colonel had been fired for falsifying Osprey records and for ordering the members of his squadron to do the same (1). "We need to lie or manipulate the data, or however you wanna call it," he said (1). The lies were intended to win new funding, but a squadron member caught the orders on tape, and the plan backfired.

Despite these problems, funding continued (2). The Osprey became operational in 2006 and is currently used in a wide variety of operations for the military.

If you try hard enough and long enough, it is possible to create functional complex products (hospital information systems, manned expeditions to mars, supersonic transports). The purpose of this series of posts is to show that complexity has very high costs. Aside from the money, the highest cost of a complex item comes from our inability to fully understand what we have created. We cannot always predict how complex objects will operate, how they will fail, or how they can be fixed. In many cases, it is better to acknowledge our limitations, by building very simple systems, and by developing ways of simplifying complex systems.

[1] Ricks TE. Data Faking Could Lower Osprey's Prospects Further. Washington Post Jan 21, 2001.

[2] Berler R. Saving the Pentagon's Killer Chopper-Plane. 22 years. $16 billion. 30 deaths. The V-22 Osprey has been an R&D nightmare. But now the dream of a tilt-rotor troop transport could finally come true. Wired 13.07, July 2005.

© 2010 Jules Berman

key words:informatics, complexity, jules j berman, medical history

Grabbing, Testing Web Links

2010-01-24T04:32:00.000-08:00

Once more, I'm interrupting a series of blogs on the topic of complexity to provide a pointer to a recently constructed web page showing how to grab web pages and test web links, using Perl, Python, or Ruby.

Perl, Python and Ruby have their own external modules for HTTP transactions. Each language's module has its own peculiar syntax. Still, the basic operation is the same: your script initiates an HTTP request for a web file at a specific network address (the URL, or Uniform Resource Locator). A response is received determining if the page is available (equivalent to testing a link). With a little effort, you can modify the provided scripts to collect and examine a large number of web pages. With a little more effort, you can write your own spider software that searches for web addresses, iteratively collecting information from links within web pages.

The article is at:

http://www.julesberman.info/factoids/url_get.htm

© 2010 Jules J. Berman

key words: html, http, hypertext transfer protocol, testing link, ruby programming, perl programming, python programming, bioinformatics, valid web page, web page is available, good http request, valid http request testing if web page exists, testing web links, jules berman Ph.D., M.D.

Batch image conversions

2010-01-23T04:01:00.000-08:00

I'm interrupting a series of blogs on the topic of complexity to provide a pointer to my recently constructed web page on batch image conversions.

When you write your own image software, you can automate activities that would otherwise require repeated operations, on multiple image files, with off-the-shelf image processing software. For example, you might want to delete, add, or modify annotations for a group of images, or you might want to resize an image collection to conform to specified dimensions. When you have more than a few images, you will not want to repeat the process by hand, for each image. When you have thousands of images, stored in a variety of image formats, it will be impossible to implement global conversions, if you do not know how to batch your operations.

The Batch conversions: Perl, Python, Ruby page provides three equivalent scripts, in Perl, Python and Ruby, that converts a batch of images from color to greyscale. The scripts are preceded by a step-by-step explanation of the code.

- © 2010 Jules Berman

key words: perl programming, python programming, ruby programming, image magick, imagemagick, grayscale, greyscale, Jules J. Berman Ph.D., M.D.

COMPLEXITY 7

2010-01-22T04:56:00.000-08:00

This is the seventh in a series of new posts on the subject of complexity in scientific research. The theme of this collection is that scientific progress, particularly in the realm of healthcare, has declined as a consequence of the high complexity in software and other technologies.

-- POST BEGINS HERE --

The National Reconnaissance Office is the U.S. agency that handles spy satellites. In 1998, the agency offered a contract to build a new generation of satellites. The contract went to Boeing, which had never built the kind of satellite specified in the contract. According to an investigative article written for the New York Times, the Boeing engineers designed subsystems of such complexity that they could not be built (1). Because the workforce were inexperienced in assembling a satellite, they used construction materials that were inappropriate for spacecraft. Most noteworthy was their planned use of tin parts, which deform in space, sometimes leading to short circuits. Seven years later, the project was killed, after running up costs estimated as high as $18 billion dollars. Experts reviewing the failed project indicated that it was doomed from the start. Basically, the level of complexity of the project exceeded Boeing's ability to fulfill the contract, and exceeded the government's ability to initiate and supervise the contract (1).

There are projects that tantalize, hovering just outside human reach: sending men to mars, commercializing supersonic transport jets, long-term stock market predictions, introduction of species to a foreign ecological environment, tamper-proof computerized voting machines, planned tactical warfare, etc. It is not as though the world does not contain complex, and functional, objects. Jet planes, supercomputers, skyscrapers, telecommunication satellites, butterflies, and humans are just a few examples. These highly complex objects all arose from less complex objects. Butterflies and humans slowly evolved, over billions of years, from an early life form. Jets and other complex machines were built by teams of humans, working from a collective experience, adding improvements incrementally, over decades

[1] Taubman P. Failure to Launch: In death of spy satellite program, lofty plans and unrealistic bids. The New York Times. November 11, 2007.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words:informatics, complexity, jules j berman, institutional memory, medical history, blog list

COMPLEXITY 6

2010-01-21T05:00:00.000-08:00

This is the sixth in a series of new posts on the subject of complexity in scientific research. The theme of this collection is that scientific progress, particularly in the realm of healthcare, has declined as a consequence of the high complexity in software and other technologies.

-- POST BEGINS HERE --

"Any informatics problem can be solved by adding an extra layer of abstraction."
- Anonymous source, sometimes referred as the golden rule of computer science

Much can be learned about the consequences of complexity by reviewing technology disasters. A 2003 article in the British Medical Journal described a project to install a computerized integrated hospital information system in Limpopo (Northern) Province of South Africa Rlita. This poor province 42 hospitals and invested heavily to acquire the system. This fascinating article describes what went wrong and provides a list of factors that led to the failure of the system. this included a failure to take into account the social and cultural milieu in which the system would be used. There was an underestimation of the complexity the undertaking and insufficient appreciation of the length of training required by the hospital staff.

One of the most challenging features of many Hospital Information Systems is computerized physician order entry (CPOE). The intent of CPOE is to eliminate the wasteful hand-written (often illegible) doctor's orders that may need to be transcribed by nurses, pharmacists, and laboratory personnel before finally entered into the HIS. Having the physicians directly enter their orders into the HIS has been a long-awaited dream for many hospital administrators. In a fascinating report, patient mortality was shown to increase after implementation of CPOE. In this study, having CPOE was a strong, independent predictor of patient death. Somehow, a computerized service intended to enhance patient care had put patients at increased risk (1).

High-tech medical solutions seldom achieve the desired effect when implemented by low-tech medical staff. Introducing complex informatics services, such as CPOE, requires staff training. There needs to be effective communication between the clinical staff and the hospital IT staff and between the hospital IT staff and the HIS vendor staff. Everyone involved must cooperate until the implemented system is working smoothly. This is virtually impossible. Hospital personnel know that a wide range of standard practices (such as complex tests, tests using specialized imaging equipment, procedures that require patient preparation or transportation, timed-interval dosage administration, expert consultations, interventions that require close attending staff supervision) become very iffy on weekends, holidays, and after about 4:00 PM on weekdays. It is difficult to get shift workers to interface seamlessly with a computer system that never sleeps.

When it comes to hiding in the safe shadow of complexity, nobody does it better than software designers. They will take a problem, such as computer-aided diagnosis, or computer-aided medical decision-making, and produce a software application that purports to provide an answer. Your input is an x-ray or a series of lab tests and clinical finding, and out comes a diagnosis. We fool ourselves into thinking that the designers of complex software systems must understand how the system works. Not so. Computers allow us to design complex, interdependent, systems that are unpredictable and inherently chaotic.

Software failure is probably the most sensitive indicator of the limits of complexity. It is very easy to create software that works at a level of complexity beyond anything found in physical systems. The weakest programmers tend to fix bugs with layers of subroutines. Stronger programmers will simplify the problem and re-write their code, eliminating unnecessary subroutines. A 1995 report by the Standish group showed that most software projects sponsored by large companies are failures. Only 9% of such project are finished on time and within budget, and many of the finished projects do not meet the required performance specifications (2). Complexity is a plague on almost every area of science.

Probably the most famous medical software disaster involved the Therac-25 (3). Between 1985 and 1987, at least 6 patients received massive overdoses of radiation due to a software error in a radiation therapy device. A review of the incidents uncovered numerous errors in the engineering and in procedures for detecting and correcting softare problems.

Medical software errors are not rare. The FDA analyzed 3140 medical device recalls conducted between 1992 and 1998 reveals that 242 of them (7.7%) are attributable to software failures. Of those, 192 (or 79%) were caused by software changes made after the software's initial production and distribution (4).

[1] Han YY, Carcillo JA, Venkataraman ST, Clark RS, Watson RS, Nguyen TC, Bayir H, Orr RA. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 116:1506-1512, 2005.

[2] The Standish Group Report: Chaos. http://www.projectsmart.co.uk/docs/chaos-report.pdf, 1995.

[3] Leveson N. Medical Devices: The Therac-25. Appendix A in: Leveson N. Safeware: system safety and computers, Addison-Wesley, Reading, 1995.

[4] General Principles of Software Validation; Final Guidance for Industry and FDA Staff. January 11, 2002.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words:informatics, complexity, jules j berman, medical history

COMPLEXITY 5

2010-01-20T05:18:00.001-08:00

This is the fifth in a series of new posts on the subject of complexity in scientific research. The theme of this collection is that scientific progress, particularly in the realm of healthcare, has declined as a consequence of the high complexity in software and other technologies.

-- POST BEGINS HERE --

Gone are the days when a scientist could describe a simple, elegant experiment (on a mouse, a frog, or some other easily obtained chemical reagents) and another scientist would, in a matter of a few hours, repeat the process in his own laboratory. When several laboratories perform the same experiment, using equivalent resources, and producing similar results, it is a safe bet that the research is valid (1).

Today, much of research is conducted in a complex, data-intensive realm. Individual studies can cost millions of dollars, involve hundreds of researchers, and produce terabytes of data. When experiments reach a high level of cost and complexity, repetition of the same experiment, in a different laboratory, becomes impractical.

In the late 1990s, a variety of data-intensive methods were developed for molecular biology, all of which generated vast amounts of data, requiring complex and sophisticated algorithms to convert the raw data into measured quantities and to analyze the huge assortment of measurements. Once such method is gene expression microarrays. In these studies, RNA molecules in tissue samples are converted to DNA and incubated against an array of pre-selected DNA samples. DNA sequences in the sample and the microarray that match, will, under precise conditions, anneal to form double-stranded molecules. The number of matches can be semi-quantitated, and a profile of the relative abundance of every RNA species in the original sample can be produced and compared with the profiles of other specimens. Using these profiles, medical researchers have tried to identify profiles (of diseased tissues) that predict responsiveness to particular types of treatment. In particular, researchers have tried use cancer tissue profiles to predict the likelihood that a specific tumor will respond to a specific type of treatment. Since the late 1990s, an enormous number of studies have been funded to produce the tissue microarray profiles for many different diseases, in many different clinical stages, and to correlate these profiles with treatment response.

Because there are so many different variables in the selection of patients, the selection of tissues, the preparation of tissues for annealment, the selection of microarray reagents, the collection of data, the conversion of data to a quantifiable measure, and the methods of analyzing the data, it is impossible for different laboratories to faithfully repeat a microarray experiment. Michiels and co-workers have shown that most microarray studies could not classify patients better than chance (2). Still, the field of microarray profiling continues, as it should, because successful fields must overcome their limitations. Continued efforts may resolve the seemingly intractable problems discussed here, or may open up alternate areas of more fruitful research. Much money has been invested into microarray profiling, and many laboratories depend on the continued funding of this technology. Experience suggests that it takes at a few decades to thoroughly discredit a well-funded but ill-conceived idea.

Here is another case in point. The U.S. Veterans Administration Medical System operates about 175 hospitals. This is an immense undertaking, but the work is accomplished fairly well, using a rather simple algorithm. The VA hires a bunch of doctors, nurses and healthcare workers, gives them a set salary, and houses them in hospital buildings. When registered patients appear in their clinics, the VA pays for the supplies necessary to treat the patients. Each year, the Congress appropriates the money to keep the VA going the next year. One of the greatest benefits of the VA system is the lack of billing. Patient visits, medical procedures, diagnostics, pharmaceuticals, and other medical arcana are absorbed into budget. If you were to compare the level of complexity of the VA healthcare system with the level of complexity of 175 private hospitals, you would find the VA system to be a model of simplicity.

Then one day, somebody asked, "Should the VA pay for medical services rendered on veterans who have their own private insurers?" Having no affirmative answer, the VA undertook an effort to pry reimbursements from the private insurers of veterans treated at VA hospitals. Suddenly, billing and expense records became important the VA, an institution with no experience in fee-for-service care.

The VA planned a $427 million software system to track billing and other financial transactions. The pilot site was the Bay Pines VA, in Florida. After preliminary testing at Bay Pines, the system, known as the Core Financial and Logistics System, or CoreFLS, would be rolled out to all of the VA hospitals nationwide. Unfortunately, the system could not be implemented at Bay Pines. Neither the software nor the humans were up to the job. In 2005, VA decided to pull the plug on a $472-million system at because it did not work (3).

Four years later, in 2008, the Government Accounting Office reviewed the billing performance on just 18 of the 175 or so VA hospitals. It found that these 18 hospitals, in fiscal year 2007, failed to collect about $1.4 billion that could have been paid by private insurers. The report from the Government Accounting Office concluded, "Since 2001 we have reported that continuing weaknesses in VA billing processes and controls have impaired VA’s ability to maximize the collections received from third-party insurers. (4)"

Why, after years of effort, has the VA not succeeded in billing private insurers for VA care received by privately insured veterans? The reason can be distilled in a single word: complexity. Private insurance reimbursement has reached a level of complexity that exceeds the ability of bureaucratic organizations to cope. There are many insurers, each with their own policies and their own obstructionist bureaucracies. When the VA tries to collect from third party payers, they must deal with insurers across fifty states. The VA paid dearly to acquire a financial database that could handle the problem, but the software wasn't up to the job.

Hospital information systems are among the most complex and most expensive software systems. The cost of a hospital information system for a large medical center can easily exceed $200 million. It is widely assumed that hospital information systems have been of enormous benefit to patients, but reports suggest that 75% of installed systems are failures (5). If Hospital Information Systems worked well, why does the cost of healthcare continue to rise? Has information technology eliminated the fragmentation of medical care or reduced the the complexities of health payment plans? Evidence for the value of implementing complex health information technology in community hospitals is scant. Most of the credible reports on the benefits of Hospital Information Systems come from large institutions that have developed their own systems incrementally, over many years (6).

[1] Golden F. Science: Fudging Data for Fun and Profit. Time December 7, 1981. http://www.time.com/time/printout/0,8816,953258,00.html

[2] Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365:488-492, 2005.

[3] De La Garza P, Nohlgren S. VA yanks troubled computer system: the $472-million computer system being tested at Bay Pines just doesn't work, veterans officials say. St. Persburg Times July 27, 2004.

[4] GAO United States Government Accountability Office Testimony Before the Subcommittee on Health, Committee on Veterans' Affairs, House of Representatives. VA HEALTH CARE: Ineffective Medical Center Controls Resulted in Inappropriate Billing and Collection Practices. Statement of Kay L. Daly Director Financial Management and Assurance. GAO-10-152T October 15, 2009.

[5] Littlejohns P, Wyatt JC, Garvican L. Evaluating computerised health information systems: hard lessons still to be learnt British Medical Journal 326:860-863, April 19, 2003. http://bmj.com/cgi/content/full/326/7394/860. Comment. The authors report that about about three quarters of installed hospital information systems are considered failures.

[6] Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG. Impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 144:742-752, 2006.]chaudhry.pdf

-- TO BE CONTINUED --

© 2010 Jules Berman

key words:informatics, complexity, jules j berman, medical history

COMPLEXITY 4

2010-01-19T04:26:00.000-08:00

This is the fourth in a series of new posts on the subject of complexity in scientific research. The theme of this collection is that scientific progress, particularly in the realm of healthcare, has declined as a consequence of the high complexity in software and other technologies.

-- POST BEGINS HERE --

In the prior blog, I discussed the lack of tangible progress in medical research in the past few decades. The general perception that basic research advances are not yielding clinically useful medical breakthroughs has inspired the "translational research" rhetoric currently spewing from funding agencies. Is it possible that the current generation of medical researchers has made no progress whatsoever? Well, maybe there were a few bright spots. Here are some of the major breakthroughs in medicine occurring since 1960.

1. Zinc drastically reduces childhood deaths from diarrhea, a disease that kills 1.6 million children under the age of five, every year (1).

2. Helicobacter pylori causes gastritis, gastric ulcers, and some stomach cancers (2). A simple antibiotic treatment cures gastritis and reduces the incidence of stomach cancers (3). This work earned the two discoverers, Barry Marshall and Robin Warren, the 2005 Nobel prize

3. When babies sleep on their backs, instead of their stomachs, the incidence of SIDS (sudden infant death syndrome, or crib death) plummets (4).

4. Daily aspirin ingestion seems to reduce deaths from cardiovascular disease and colon cancer (5).

The most significant medical advances in the past few decades (and there haven't been many) have been simple measures. All of the great debacles in medicine have been complex. This is because scientific methods have reached a level of complexity that nobody can understand.

Gone are the days when a scientist could describe a simple, elegant experiment (on a mouse, a frog, or some other easily obtained chemical reagents) and another scientist would, in a matter of a few hours, repeat the process in his own laboratory. When several laboratories perform the same experiment, using equivalent resources, and producing similar results, it is a safe bet that the research is valid, but we seldom see that kind of validation.

Today, much of research is conducted in a complex, data-intensive realm. Individual studies can cost millions of dollars, involve hundreds of researchers, and produce terabytes of data. When experiments reach a high level of cost and complexity, repetition of the same experiment, in a different laboratory, becomes impractical.

[1] Walt V. Diarrhea: the great zinc breakthrough. Time August 17, 2009.

[2] Warren JR, Marshall BJ. Unidentified curved bacilli on gastric epithelium in active chronic gastritis. Lancet 1:1273-1275, 1983.

[3] Kidd M, Modlin IM. A century of Helicobacter pylori: paradigms lost-paradigms regained. Digestion 59:1-15, 1998.

[4] Vennemann MM, Fischer D, Jorch G, Bajanowski T. Prevention of sudden infant death syndrome (SIDS) due to an active health monitoring system 20 years prior to the public "back-to-sleep-campaigns." Arch Dis Child. Jan 6, 2006.

[5] Writing Group; Hennekens CH, Dyken ML, Fuster V. Aspirin as a therapeutic agent in cardiovascular disease: a statement for healthcare professionals from the American Heart Association. Circulation 96:2751-2753, 1997.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words:informatics, complexity, jules j berman, medical history

COMPLEXITY 3

2010-01-18T04:37:00.001-08:00

This is the third in a series of new posts on the subject of complexity in scientific research. The theme of this collection is that scientific progress, particularly in the realm of healthcare, has declined as a consequence of the high complexity in software and other technologies.

-- POST BEGINS HERE --

Scientists tell us that they are making great advances in the treatment of cancer. This is not so. The total U.S. cancer death rate has barely budged in the past 60 years. Though deaths from some types of cancer have dropped, these drops have been offset by the rise in deaths caused by other cancers. Of the cancers that have dropped the most: stomach cancer and cancer of the uterine cervix, improved mortality is due to a drop in cancer incidence, not due to any progress in treating advanced cancers. The reduced incidence of stomach cancer is generally credited to refrigeration and improved methods of food preservation. With better preserved food, the incidence of stomach cancer dropped. The drop in cervical cancer has been due to effective Pap smear screening for precancerous lesions (small lesions that precede the development of invasive cancer). When uterine precancers are excised, the cancer never develops. Further reduction in deaths from uterine cancer will probably result from population-wide inoculations with the new HPV vaccine; an effective measure that bypasses the need to treat advanced cancers.

Beginning about 1991, the U.S. has seen a small, but continuous drop in the cancer death rate. This drop is due almost entirely to the reduced incidence of lung cancer among men (due to smoking cessation). Even with this small drop, the cancer death rate is still about the same as it was in the middle of the twentieth century (i.e., there is still a net increase in the long-term U.S. cancer death rate, even with the recent drop). With the exception of curing a few types of rare tumors, cancer research has yielded none of the dramatic medical advances we saw in the 1950s, against such diseases as polio and tuberculosis.

Cancer death rates that have increased since 1950 include: esophageal cancer, liver cancer, pancreatic cancer, lung cancer, melanoma, kidney cancer, brain cancer, non-Hodgkins lymphoma, and multiple myeloma. The list includes some of the most common types of cancer. If cancer research were effective, we would have prevented the rise in incidence of these common cancers.

If you speak to cancer researchers, they will tell you that we have made great advances in understanding cancer genetics; the mutations in DNA that contribute to the development of cancers. Yes; we've gotten a lot of news about cancer, but it's mostly bad news. We now know, thanks to billions of dollars of funding, that cancer cells are remarkably complex, often containing thousands of genetic alterations. No two genetically complex cancers can be characterized by the same set of mutations, and no two tissue samples of any one cancer will be genetically identical. The complexity of cancer far outstrips our ability to characterize the alterations in a cancer cell. Consequently, it is highly unlikely that any single drug will correct all of the genetic changes in the cells of advanced (i.e., invasive and metastatic) common cancers. Newly acquired knowledge of cancer genetics have taught us that we cannot cure advanced common cancers with currently available techniques.

There are a few exceptions. Not all cancers are complex. Some cancers (particularly rare inherited cancers and rare cancers occurring in children, and a few types of rare sporadic cancers) are characterized by simple genetic alterations. These genetically simple tumors turn out to be the tumors we can cure or the tumors for which we can most likely develop a cure in the near future. A simple genetic error can be targeted by a new generation of cancer drugs. Unfortunately, the commonly occurring cancers of adults are all genetically complex. It is unlikely that we will be able to cure these tumors anytime soon. The best chance of curing common cancers may come from studying how rare (genetically simple) tumors respond to new types of treatment.

In cancer research, as in so many other modern areas of scientific research, it seems that complexity is a major barrier to scientific progress.

-- TO BE CONTINUED --

© 2010 Jules Berman

key words:informatics, complexity, jules j berman, medical history

COMPLEXITY 2

2010-01-17T04:29:00.001-08:00

This is the second in a series of new posts on the subject of complexity in scientific research. The theme of this collection is that scientific progress, particularly in the realm of healthcare, has declined as a consequence of the high complexity in software and other technologies.

-- POST BEGINS HERE --

If the rate of scientific accomplishment is dependent upon the number of scientists on the job, you would expect that that rate of scientific accomplishment would be accelerating, not decelerating. According to the National Science Foundation, 18,052 science and engineering doctoral degrees were awarded in the U.S., in 1970. By 1997, that number had risen to 26,847, nearly a 50% increase in the annual production of the highest level scientists (1). In 1953, according to the National Science Foundation, the total U.S. expenditures on research and development was $5.16 billion, expressed in current dollar values. In 1998, that number has risen to $227.173 billion, greater than a 40-fold increase in research and development spending (1). The growing work force of scientists failed to advance science very much, but it was not for lack of funds.

The U.S. Department of Health and Human Services has published an interesting document, entitled, "Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. (2) " The authors note that fewer and fewer new medicines and medical devices are reaching the Food and Drug Administration. Significant advances in genomics, proteomics and nanotechnology have not led to significant advances in the treatment of diseases. Extrapolating from the level of scientific progress in the past half century, there's not much reason to expect great improvements in the next 50 years. The last quarter of the 20th century has been described as the "era of Brownian motion in health care" (3).

1. National Science Board, Science & Engineering Indicators - 2000. Arlington, VA: National Science Foundation, 2000 (NSB-00-1).

2.Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. U.S. Department of Health and Human Services, Food and Drug Administration, 2004.

3. Crossing the Quality Chasm: A New Health System for the 21st Century. Quality of Health Care in America Committee, editors. Institute of Medicine, Washington, DC., 2001.

-- TO BE CONTINUED --

-© 2010 Jules Berman

key words: complexity, scientific progress, jules j berman, medical history, informatics, software

COMPLEXITY: PART 1

2010-01-16T09:53:00.000-08:00

I'm beginning a series of new blogs written on the subject of complexity in scientific research. The point of this collection of essays is to show that scientific progress has declined as a consequence of the high complexity in software and other technologies.

PROGRESS? WHAT PROGRESS?

When you watch a movie circa 1960, and you look at their streets and houses, and furniture, and clothing, is there any difference between then and now? Not much. Basically, the scientific advance that shapes the world today was discovered prior to 1960. The only visible difference between people then and people now is personal appearance. The twenty-first century citizen has abandoned keeping neat and trim, preferring an alluring fat slob look.

What did we have in 1960? We had home television (1947), transistors (1948), commercial jets (1949), computers (Univac, 1951), nuclear bombs (fission , fusion in 1952), solar cells (1954), fission reactors (1954), satellites orbiting the earth (Sputnik I, 1957), integrated circuits (1958), photocopying (1958), probes on the moon (Lunik II, 1959), practical business computers (1959), lasers (1960).

These engineering and scientific advancements pale in comparison to the advances in medicine that occurred by 1960. Prior to 1950, we had the basic principles of metabolism, including the chemistry and functions of vitamins; the activity of the hormone system (including the use of insulin to treat diabetes and dietary methods to prevent goiter), the methodology to develop antibiotics and to use them effectively to treat syphilis, gonorrhea, and the most common bacterial diseases. Sterile surgical technique was practiced, bringing a precipitous drop in maternal post-partum deaths. We could provide safe blood transfusions, using A,B,O compatibility testing (1900). X-ray imaging had improve medical diagnosis.

Disease prevention was a practical field of medical science, bringing methods to prevent a wide range of common diseases using a clean water supply and improved waste management; and safe methods to preserve food including canning, refrigeration and freezing. In 1941, Papanicolaou introduced the smear technique to screen for precancerous cervical lesions, resulting in a 70% drop in the death rate from uterine cervical cancer in populations that implemented screening. In 1947, we had strong epidemiologic evidence that cigarettes caused lung cancer.

When we entered 1950, Linus Pauling had essentially invented the field of molecular genetics by demonstrating a single amino acid mutation accounting for the the defective gene responsible for sickle cell anemia. In 1950 Chargoff discovered base complementarity in DNA. Also, in 1950, Arthur Vineburg routed an internal mammary artery, in place, to vascularize the heart. In 1951, fluoridation was introduced, greatly reducing dental disease. Then came isoniazid, the drug that virtually erased tuberculosis (1952). Also, in 1952, Harold Hopkins designed the fibroscope, heralding fiberoptic endoscopy. In 1953, Watson and Crick showed that DNA was composed of a double helix chain of complementary nucleotides encoding human genes. John Gibbon performed the first open heart surgery using a cardiopulmonary bypass machine (1953), and D.W. Gordon Murray used arterial grafts to replace the left anterior descending coronary artery (the coronary artery bypass graft). Oral contraceptives (birth control pills) were invented in 1954. That same year, Salk developed an effective killed vaccine for polio, followed just three years later with Sabin's live polio vaccine. Thus, in the 1950s, the two most dreadful scourges of developed countries, tuberculosis and polio, were virtually eradicated.

Don't believe those reports announcing longer life expectancies for Americans. The people who are living longer today are the people who were born in the twentieth century and benefited directly from the advances in medicine occurring prior to 1960. Nobody has any way of knowing whether children born in the twenty-first century will live longer lives than their twentieth century predecessors. But their chances for long lives do not look very good. Here are some of the medical reversals that have occurred since 1960.

1. The worldwide spread of AIDS, a virus-spread disease that could have been eradicated with a few simple precautions, but was not.

2. The emergence of multiple drug-resistant tuberculosis. The root cause of the rise of resistant TB is the incomplete treatment of identified patients.

3. The emergence of multiple antibiotic resistant strains of Staphylococcus aureus.

4. Global warming, loss of the ozone layer, and other consequences of atmospheric pollution.

5. Mass starvation.

6. Reduced access to potable water affecting the vast majority of humans.

7. Planetary scale deforestation and desertification.

8. Monoculture of a few favored crops replacing biodiversity.

9. Large scale emergence of invasive and destructive species of plants and animals.

10. Increases in the total number of U.S. deaths from cancer.

11. The re-emergence of resistant insect and other vectors carrying viral and parasitic diseases.

12. Astronomical costs of new medications for chronic diseases, unaffordable to all but a small percentage of the world population.

13. The rising worldwide incidence of obesity and sequelae disorders.

14. The rapid geographic spread of outbreaks of new strains of influenza and other evolving viruses, including HIV and hemorrhagic fever viruses.

-- TO BE CONTINUED --

-© 2010 Jules Berman

key words: complexity, scientific progress, jules j berman, medical history, informatics, software

Quarter century anniversary of Bhopal disaster

2009-12-03T14:38:00.000-08:00

On December 3, 1984, methyl isocyanate (MIC), a highly toxic gas, escaped from the Union Carbide chemical plant in Bhopal, India, quickly killing thousands of people, and injuring many others. Accurate numbers for deaths and injuries are not known, but the concensus seems to be about 15,000 deaths (including the number of people who died at the time of the accident and the weeks thereafter), and about 200,000 injuries.

The Bhopal disaster is described in detail in Nancy Leveson's excellent book A New Approach to System Safety Engineering

Here is what happened at Bhopal. A worker was cleaning one of the pipes, with water. Water from the cleaning operation flowed into an MIC tank, causing an explosion. Escape vents released hot MIC gases into the atmosphere. About 40 tons of toxin descended as a hot cloud on the population around Bhopal. Many people died on the spot. Some families had a chance to flee. Parents with gasping children had to decide quickly which child could be carried to safety and which child would be left behind. The Bhopal disaster is considered to be the worst industrial accident in history.

After the event, Union Carbide lawyers blamed the disaster on one individual; the worker who was cleaning the pipe. According to Union Carbide, because the disaster was the work of a saboteur, Union Carbide was not responsible.

Let's say, for the sake of argument, that a lone sabateur caused the Bhopal disaster, What would this saboteur need to know in order to pull off his deadly scheme.

First, he would need to know, that he alone would be selected to wash out some clogged pipes that connected to the MIC tank. He would need to know that the valves separating the pipes from the MIC tank were leaky. He would need to know that the cleaning operation would not be inspected by an experienced shift supervisor (a position that was eliminated to save money) who would have probably inserted a safety disk to compensate for the leaky valve. He would need to know that the refrigeration unit for the MIC tank was not working, and that the MIC was not stored at a sufficiently low temperature to ensure the stability of the MIC/water mixture. He would need to know that the procedure for logging the temperature in the MIC tank had been halted, thus preventing other workers from noticing the rising tank temperatures. He would need to know that the first tell-tale gas reaction odors would reach co-workers during tea break, and that crucial actions would wait until after tea was served. He would need to know that the flare tower, intended to safely burn off released gases, was inadequate for the job. He would need to know that the MIC would be vented from a high stack (105 feet), above the reach of the water jets intended as a safety curtain. He would need to know that the warning siren would not sound until two hours after the gas release (after most of the damage was done). He would need to know that emergency measures to protect the population would not be taken. Rather than quickly evacuating the population, plant spokesmen at first denied that an accident had occurred. Rather than advising people that injuries could be reduced by applying a wet towel on the face and shutting their eyes, spokesmen reassured the public that MIC was not particularly harmful.

Here we are, twenty-five years later. What is the news from Bhopal?

The former CEO of Union Carbide was issued an arrest warrant by the Indian government, which has not been served because the Indian government is officially unaware of his whereabouts. The former CEO is said to be residing comfortably and openly in the Hamptons.

The Bhopal site has not been cleaned up. Dow Chemical has since acquired Union Carbide and accepts no responsibility for the clean-up. Dow asserts that a 2001 payment of $470 million to the Indian government has resolved the issue. The Indian environment minister, when visiting the Bhopal site, lifted a clot of dirt and proclaimed, "see, I am alive," certifying that the area is now safe. Nonetheless, each month, an estimated 10-30 people die from the contaminated groundwater and residual toxic waste.

Sources:

Suketu Mehta. A cloud still hands over Bhopal. The New York Times, December 3, 2009.

Court issues arrest warrant for former CEO of Union Carbide in gas leak case. Associated Press (in The Guardian, UK) , July 31, 2009

Randeep Ramesh. Bhopal water still toxic 25 years after deadly gas leak, study finds. The Guardian, UK, December 1, 2009.

Leveson NG. A new approach to system safety engineering. (self-published), 2002

- © 2009 Jules J. Berman

Happy Thanksgiving

2009-11-26T13:34:00.000-08:00

Today, America is celebrating Thanksgiving, my favorite holiday. Thanksgiving is not a religious holiday, but it is a deeply spiritual holiday. It's a time when we give thanks for this marvelous and mysterious universe. Most of us, on Thanksgiving, seek the company of family and friends. I hope that everyone, even those people who are unable to be with their loved ones, can enjoy this special time.

Happy Thanksgiving!

Jules Berman

Cancer prevention better than cancer screening

2009-11-24T08:01:00.000-08:00

In the past several days, I've been blogging on the new guidelines issued last week for breast cancer screening (mammograms) and for cervical precancer screening (Pap smears). Basically, in both new sets of guidelines, the task forces raised the age at which routine screenings are started, because they felt that screening in the younger age groups found very few positive (malignant) cases. They reasoned that the small benefits of screening in the younger age groups are outweighed by the adverse affects of screening (false positives, unnecessary diagnostic tests and emotional trauma).

The public response was angry, countering that saving lives, even a small number of lives, is preferable to putting up with the non-life-threatening adverse effects of screening.

I'm sympathetic to both sides of the argument. Maybe there's another viewpoint that might put the debate into perspective.

While cancer screening tests have some value, the most effective way of reducing the cancer death rate is by cancer prevention. There are several drugs that can reduce the incidence of common cancers. Finasteride, for example, has been shown to reduce the incidence of prostate cancer by 30%. Though early studies indicated that finasteride mildly increased the incidence of highly aggressive prostate cancers, subsequent studies have shown that this is simply not the case. About 30,000 men die each year, in the U.S. from prostate cancer. Widespread finasteride use could lower the number of deaths by nearly 10,000 each year.

Finasteride is a drug that is used to reduce BPH (benign prostatic hyperplasia), and to reduce hair loss (under the name Propecia). It has a long history of use, and doctors understand the side-effects of Finasteride. The Merck patent on the use of Finasteride for the treatment of BPH has already expired, and the drug can be obtained as an inexpensive generic.

Ask yourself: "How many men do you know who take Finasteride to prevent prostate cancer?" I'm sure it's a very small number. Yet most women, over the age of 40, have mammographic examinations to detect breast cancer, and are prepared to fight anyone who tries to stop them.

There are a variety of agents that reduce the incidence of cancer. Most people are barely aware that the world-wide cancer death rate can be cut much more effectively with inexpensive anti-cancer agents than with expensive cancer screening tests. Both approaches are very important, but there seems to be a sociological blind-spot for prevention. I discuss this issue at some length in my recently published book, Precancer: The Beginning and the End of Cancer.

- © 2009 Jules Berman, Ph.D., M.D.

key words: cervical cancer, cin, dysplasia, precancer, precancerous lesions, hpv, Pap smear, Pap screening, cervical screening, cervical cancer screening, mammogram, mammographic screening, screening test, new recommendations, new guidelines, early cancer screening, cancer death rate, task force recommendations, ACOG, precancers, early detection, anticancer, anti-cancer, simvastatin, aspirin, calcium supplements

New guidelines for cervical (Pap Smear) screening

2009-11-20T13:23:00.000-08:00

This seems to be the week for announcing new non-intuitive medical guidelines. My last three blogs focused on the November 16 mammography guidelines that recommended halting routine screening mammograms for women under 50.

Today (November 20, 2009), the American Congress of Obstetricians and Gynecologists (ACOG) announced their new guidelines for cervical (Pap Smear) screening. The major change is that the first Pap smear screening is now delayed until women are 21 years old. The prior recommendation was for women to get their first screening three years after they became sexually active, or age 21, whichever came first.

ACOG's official release statement , dated today, can be read at their web site. It's a short, one-page explanation, but it contains highly misleading statements.

Here's one: "Cervical cancer rates have fallen more than 50% in the past 30 years in the US due to the widespread use of the Pap test. The incidence of cervical cancer fell from 14.8 per 100,000 women in 1975 to 6.5 per 100,000 women in 2006."

Virtually every study of Pap smear screening shows a drop of at least 70% in the cervical cancer death rate, in populations that institute screening. I have no idea where they got the 50% number. The more disturbing part of their statement is the comparison of the U.S. cervical cancer death rate in 1975 with the death rate in 2006. This makes no sense.

If you want to measure the drop in the cervical cancer death rate before and after a screening tool has been implemented, you need to go back to a date preceding the introduction of the screening tool. In 1941Papanicolaou and his coworkers published paper establishing the diagnostic value of examining cervical smears to screen for cervical precancer. It took a while to implement the test nationwide, but it was certainly in common use in the 1960s. By the time 1975 came around, the Pap smear had already influenced the cervical cancer death rate. You should not be looking at the interim between 1975 and 2006 to measure the drop in cancer.

You've got to compare a pre-Pap date and a post-Pap date. The National Cancer Institute has done this for us. Cancer death rates for selected sites is shown for 1950 and 2005 in Table I-3. SUMMARY OF CHANGES IN CANCER MORTALITY, 1950-2005 AND 5-YEAR RELATIVE SURVIVAL RATES, 1950-2004 Males and Females, By Primary Cancer Site.

The table indicates that the cervical cancer death rate dropped 81.4% in white women. Furthermore, the uterine cancer death rate dropped 68.8% in the same 55-year period. Pap smears also screen for dysplasias and cancer of the endometrium (uterine lining), though with less sensitivity than for cervical screening.

That was not the only problem with the ACOG announcement. Another quotation is, "Although the rate of HPV infection is high among sexually active adolescents, invasive cervical cancer is very rare in women under age 21."

This statement gives the impression that the Pap smear is a screen for cervical cancer. It is not. The Pap smear is a screen for cervical precancer (referred to as dysplasias and CIN in the ACOG statement), not for cervical cancer, and the persons responsible for the ACOG statement don't seem to understand this. Precancers are the lesions that precede the development of cancer , and it may take precancers a decade or more to develop into cancers. Though the Pap smear can detect cervical cancers, the whole idea behind the test is to find precancers before they become cancers, when they can be treated by a simple excisional biopsy. The fact that cancers rarely occur in women under the age of 21 means that the neoplastic lesions most likely to be found by Pap smear in women under 21 are cervical precancers (i.e., the lesions that we're trying to find)!

The ACOG release also states: "Screening for cervical cancer in adolescents only serves to increase their anxiety and has led to overuse of follow-up procedures for something that usually resolves on its own."

Pap smear screening in adolescents actually has a number of useful purposes. It draws young women into the gynecologist's office, where a broad range of gynecologic diseases, in addition to cervical precancer, can be detected and treated. In fact, many infections of the vagina and cervix can be diagnosed by Pap smear. Their assertion that the Pap smear leads to over-use of follow-up procedures is also questionable. If there is an overuse of procedures following Pap smears (and I wouldn't know whether there is or isn't), then the ACOG should produce guidelines on the proper use of follow-up procedures. Reducing Pap smear screening in the under 21 age group because doctors do the wrong follow-up after they receive the smear report, seems absurd to me.

In summary, I don't really know whether the ACOG recommendations are any good. All I know is that some of the reasons that they provide in their release statement don't make much sense.

- © 2009 Jules J. Berman, Ph.D., M.D.

key words: cervical cancer, cin, dysplasia, precancer, precancerous lesions, hpv, Pap smear, Pap screening, cervical screening, cervical cancer screening, adolescents, teen-agers, screening test, new recommendations, new guidelines, early cancer screening, cancer death rate, task force recommendations, ACOG, precancers, early detection

More on new mammographic screening recommendations

2009-11-20T08:13:00.000-08:00

I've had a chance to look at the evidence report issued by the United States Preventive Services Task Force that released the new recommendations for mammography screening earlier this week.

The evidence report is a public document available at:

http://www.ahrq.gov/clinic/uspstf09/breastcancer/brcanes.pdf

The report is entitled, Screening for Breast Cancer: Systematic Evidence Review Update for the U. S. Preventive Services Task Force, and was published this month by the AHRQ (the U.S. Agency for Healthcare Research and Quality). Anyone taking issue with the new recommendations should probably read the report before voicing their opinions.

For me, the biggest flaw in the report was the following line, where the task force wrote the limitations of the study:

"Limitations: Studies of older women, digital mammography, and magnetic resonance imaging are lacking."

I've been told that the latest digital mammograms produces superb images, that have diagnostic advantage over the images produced by the older instruments. If this is the case, then the labs that are currently using digital mammography may be doing a better job than the labs that are using the older equipment. More relevant to the task force report, the conclusion based on reviewing data using the old testing equipment may not be applicable for labs using the new equipment.

The problem with task force reviews, is that they're always working with data that lags behind the latest advances in the field. Basically, today's reality may be different from the reality that they reviewed.

It always seems to come back to two issues that I discussed in my blogs this week.

First, are all labs equal? Are there some labs that perform mammographic testing so well that a data review of their patients would show that mammographic screening in the 40-49 age group is justified, FOR THEM.

Second, as a screening test becomes more sensitive, and we start picking up small, early lesions and lesions of no biological consequence, how do we choose a clinically beneficial way of handling the test results?

The answer to the first question may involve using certified reference labs that perform tests at a high technical standard. If you want mammographic screening, why not have it done by highly competent professionals?

The answer to the second question comes down to how we approach the clinically unknown. If we have a test that produces lots of findings of undetermined clinical significance, then it seems ill-advised to throw up our hands and say that women shouldn't take the test because the results may be difficult to interpret. Wouldn't it make more sense to conduct some clinical research and learn something about these subtle lesions? We can't move forward by collecting anecdotal evidence; we need to examine data on hundreds of thousands of cases from the best mammographic labs. Then maybe we can start to design rational clinical trials where we look at how we can best deal with those lesions. I'm sure that among the small lesions found on mammography there will be lots of precancers. It would seem to me that mammographic screening in a young age group will give us the opportunity to develop effective and simple treatments for breast precancers (thus stopping the development of invasive cancer).

- © 2009 Jules J. Berman, Ph.D., M.D.

key words: mammographic, laboratory testing, screening test, mammography, breast examination, breast cancer, breast cancer screening, new recommendations, adverse effects of breast cancer screening, early cancer screening, cancer death rate, screening mammogram, screening mammography, new guidelines, new recommendations, task force recommendations, precancer, precancers

New mammogram testing recommendations: an opportunity to cure precancer

2009-11-19T05:08:00.000-08:00

In Yesterday's blog, I began a discussion of the new recommendation for mammographic breast cancer screening, announced by the United States Preventive Services Task Force (November 16).

The task force recommended that routine mammography screening begin at age 40, not age 50 (the previous recommendation).

The reason for the new recommendation relates to the low number of positive (malignant) cases in the 40-50 year age group and the high number of false-positives (nodules that are not invasive cancer) in the same age group.

I've been listening to a lot of discussion on TV and radio, and was surprised by the overwhelming (and strong) rejection of the new recommendation. Basically, it was just like any political issue: opponents rallying to reject the offered report, finding nothing of value and much to be reviled.

It seems to me that we stand to learn a lot from the task force's work, even if we don't follow their recommendation to the letter.

The problem with mammographic testing in young persons is that the test picks up small lesions that may be early invasive cancers, or they may be precancers (lesions that are not yet invasive cancers and that pose no immediate medical threat), or they may be lesions that mimic cancers but are actually benign disorders that have no medical consequence. When you look at younger and younger age groups (age groups not likely to have many invasive cancers), you pick up a disproportionate number of precancers and non-cancerous nodules.

The problem has been that these non-invasive lesions have been worked up by oncologists and surgeons with an array of surgical, diagnostic, and treatment interventions that have wasted money and caused great emotional distress in women who have not greatly benefited from the process.

Rather than drop testing, there are a number of options we could take, as a society, that might be better than the current way of doing things.

Radiologists could get together and develop diagnostic criteria for nodules that don't quite meet the criteria for malignancy. Radiologists and clinicians could then come up with recommendations for these nodules (e.g., repeat mammographic examination in 6 months, or 1 year, or whatever). Basically, the diagnosis and the recommended action would spare women from the mental, physical and economic consequences of an immediate cancer work-up.

Alternatively, the diagnosis of a "questionable" lesion could be used to qualify patients for inclusion in clinical trials for the treatment of precancers. Precancers are the non-invasive lesions that precede the development of invasive cancers. Precancers can be treated much more easily than cancers (this is the message developed in my recently published book, Precancer: The Beginning and the End of Cancer)Women with mammographic lesions consistent with precancer could be treated with experimental precancer treatments. If these treatments were found to be effective, we could greatly reduce, maybe eliminate, the breast cancer death rate.

The task force has made some important conclusions, based on their evaluation of the data. It would be a shame if we missed this opportunity to advance breast cancer treatment, simply because we don't like their final recommendation.

- © 2009 Jules J. Berman, Ph.D., M.D.

key words: mammographic, laboratory testing, screening test, mammography, breast examination, breast cancer, breast cancer screening, new recommendations, adverse effects of breast cancer screening, early cancer screening, cancer death rate, screening mammogram, screening mammography, new guidelines, new recommendations, task force recommendations, precancer, precancers

New mammogram recommendations

2009-11-18T09:05:00.000-08:00

I'm sure that every reader of this blog has been following the news about the new mammogram recommendations, but if you haven't, you might want to read Gina Kolata's article in the New York Times. Basically, the new recommendation is for women to begin mammographic breast cancer screening at age 50, not at age 40 (the previous recommended age).

Like everyone else, I've been trying to digest this news. The fuss is based on a limitation that arises with all screening tests: Whenever you have a low incidence of disease in a population (as you have for breast cancer in younger women), it's hard to come up with a good screening tool that will catch all of the positive cases (high sensitivity), and pass on all the negative cases (high specificity). As you get a higher and higher natural incidence of disease in a population (as we have for breast cancer in older women), screening outcomes look better. The extreme example would be a disease that occurs in nearly 100% of the population. If you had a remarkably dumb screening test that called everyone positive, it would seldom be wrong for a population in which just about everyone has the disease.

When your screening test is flawed (as most are), it's always tough to draw a line in the population between those who benefit from the test and those who are harmed by the test.

The problem with mammographic screening is especially difficult because mammography is a complex, interpreted test. I'll explain what this means further on in this blog, but the upshot is that some labs can do mammographic breast cancer screeing much better than other labs. The high-performing labs may produce results that would prove highly beneficial to women in the 40-50 age range. The low-performing labs skew the national data and lead statisticians to think that screening is bad for this group of women, when the truth may be that only "bad" screening is bad.

A complex test is a test where lots of things can go wrong in the preparation of the test output. Was the patient positioned properly? Was the mammogram machine working properly and was it well-calibrated? Did the lab use the best possible mammographic equipment? Were the prior tests on the same patient made available for review and comparison with the current test? Was a proper history taken, to ensure that that radiologist had all the information needed to render the best possible diagnosis for the patient?

An interpreted test is one in which the output (the mammogram) needs to be rendered into a diagnosis. All interpreted tests can be misinterpreted. Some laboratories do a much better job at interpretation than others. The best labs have radiologists who are highly trained to diagnose mammograms, and who look at many mammograms, routinely. The radiologists should review the patient's prior mammograms, when relevant, and should read the relevant sections of the patient's history and physical examination. When a radiologist has a tough case, he/she should have a way of getting help from another radiologist. A good lab has records of these kinds of consultations, and can prove that they seek consultation on a reasonable number of cases.

Good labs have a system of quality controls over every aspect of the mammographic tests, and have a way of reviewing outcomes, so that a false negative or a false positive finding from the lab can be discussed by all of the laboratory personnel. In other words, does the lab have a method of knowing when they have made a mistake, and does the lab have a way of learning from the mistake?

Most importantly, a lab must be able to prove that it is a good lab. It should have a way of conducting quality checks on the diagnoses that come from the lab, comparing the different radiologists in the lab, and comparing their lab against other labs.

This is just a generalization, but my experience has been that there are vast differences in quality among screening laboratories. In the realm of my field (pathology), it has been shown again and again that large, high-volume labs tend to do much better with complex tests than labs that do only occasional testing.

So, the question that I have about mammographic screening is: has anyone determined whether there are ANY labs for which screening in the 40-50 year range is beneficial?

- © 2009 Jules J. Berman, Ph.D., M.D.

key words: mammographic, laboratory testing, screening test, mammography, breast examination, breast cancer, breast cancer screening, new recommendations, adverse effects of breast cancer screening, early cancer screening, cancer death rate, screening mammogram, screening mammography, new guidelines, new recommendations, task force recommendations

CHRONOLOGY UPDATED

2009-11-02T09:01:00.001-08:00

I've recently made additions and modifications to my Chronology of Earthwebsite; a timeline of significant events, mostly scientific, in the history of earth; beginning with the big bang, followed 9 billion years later by the appearance of the solar system, and temporarily ending with all of us.

The chronology can be viewed at:

http://www.julesberman.info/chronos.htm

- Jules Berman

related words: world timeline, world time line, terran chronology, terrestrial chronology, chronology of science, timeline of science, science events, history of science, history of earth, science through history, science through the ages, science past and present, jules j berman

Precancers 5: Regulatory justification of precancer as a surrogate marker for cancer

2009-08-22T12:24:00.000-07:00

Precancers are the lesions from which cancers develop. Precancers can be easily treated, and when we eliminate precancers, we eliminate the cancers that would have developed from the precancers. This is the message in Precancer: The Beginning and the End of Cancer.

In the prior blog post, I discussed how precancers can be used as surrogate markers for cancers, in carcinogen assays. The example used was the increase in MGUS (the multiple myeloma precancer) in occupational exposure to pesticides. An increase in MGUS after human exposure is an indicator that pesticides can cause multiple myeloma.

Because precancers occur earlier than cancers, the use of precancers as surrogate markers for cancers, in animal studies and in human epidemiologic studies, can save enormous time and money in carcinogen assays.

Is there regulatory justification to use surrogate markers (instead of the ultimate disease end-point) in assays that test the effectiveness of interventions (to decrease the incidence of disease or to treat disease).

The Food and Drug Modernization Act of 1997 specifically allows and encourages the use of surrogate markers and end-points in drug evaluations (from the Act):

"(b) APPROVAL OF APPLICATION FOR A FAST TRACK PRODUCT.-
(1) IN GENERAL.-The Secretary may approve an application for approval of a fast track product under section 505(c) or section 351 of the Public Health Service Act upon a determination that the product has an effect on a clinical endpoint or on a surrogate endpoint that is reasonably likely to predict clinical benefit.
.
.
.
(d) AWARENESS EFFORTS.-The Secretary shall-
.
.
.
(2) establish a program to encourage the development
of surrogate endpoints that are reasonably likely to predict
clinical benefit for serious or life-threatening conditions for
which there exist significant unmet medical needs."

An early use of a surrogate marker for disease response was the use of MRI evaluation of brain images for betaseron effectiveness as a treatment of multiple sclerosis. Wherease previous FDA requirements would have expected a clinical trial showing that betaseron produced a clinical treatment response, the Modernization Act permitted the use of MRI brain scans (showing improvement in MS lesions) to serve as a surrogate end-point for clinical data. The drug was approved largely on the basis of surrogate end-points.

Similarly, precancers might serve as surrogate end-points for cancers. A drug that increases the number of precancerous lesions would be expected to increase the number of cancers. Likewise, a drug that decreases precancerous lesions would be expected to reduce the incidence of cancers.

-© 2009 Jules J. Berman

related words: precancers, precancer, pre-cancerous condition, precancerous condition, preneoplastic lesions, preneoplasia, ien, intra-epithelial neoplasia, intraepithelial neoplasia, intra-epithelial neoplasm, intraepithelial neoplasm, in situ carcinoma, carcinoma in situ, cis, dcis, din, pin, panin, cin, dysplasia, adenoma, preneoplastic, pre-cancer, pre-cancerous, precancerous, early cancer, cancer prevention, cancer detection

Precancers 4: Agents that cause precancers are carcinogens

2009-08-19T08:23:00.001-07:00

Precancers 3: All Myeloma Preceded by MGUS

2009-08-18T04:10:00.000-07:00

Precancers are the lesions from which cancers develop. Precancers can be easily treated, and when we eliminate precancers, we eliminate the cancers that would have developed from the precancers. This is the message in Precancer: The Beginning and the End of Cancer.

One of the most important questions in precancer research is whether all cancers arise from precancers, or whether some cancers can arise ab initio (from the beginning) as fully malignant clones, without first passing through a precancer phase.

There are many different types of cancer, and most cancers of humans have a putative precancer precursor. Multiple myeloma, and its precursor, MGUS (monoclonal gammopathy of undetermined significance) are discussed in some detail in my recently published book, Precancer: The Beginning and the End of Cancer. Briefly, multiple myeloma is a cancer of plasma cells. Plasma cells are the specialized blood cells that produce gamma globulin antobody molecules. Cancerous plasma cells produce a monoclonal spike of gamma globulins that can be detected by looking at samples of blood. MGUS, the precursor of multiple myeloma, produces a similar monoclonal spike in gamma globulins, but it is not associated with clinically detectable masses of cancer cells.

In a recent paper by Langren et al, the authors answered a simple, fundamental question in tumor biology: Are all cases of myeloma preceded by MGUS?

The paper was published in the journal Blood.

Landgren O, Kyle RA, Pfeiffer RM, et al. Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study. Blood 113:5412-5417, 2009.

The paper is available as an open access document at:

http://bloodjournal.hematologylibrary.org/cgi/content/full/113/22/5412

The authors used data and serum samples from enrollees in the large PLCO (prostate lung colon and ovary) trial currently underway through the National Cancer Institute. They looked specifically at enrollees in the trial who developed multiple myeloma during the trial. All enrollees in the PLCO trial have blood drawn during the multi-year trial. When they looked at prior blood samples for newly diagnosed cases of multiple myeloma, they found MGUS in every instance. Therefore, at least for the 71 examined cases in their study, MGUS always preceded the myeloma.

It is extremely important to have studies (such as this study for multiple myeloma) that indicate that precancer is an obligatory step in carcinogenesis. If only a portion of cancers developed from precancers, we would never be able to eradicate all cancers [by treating their precancers]. On the other hand, if every case of cancer is preceded by a precancer, then we can be certain that if we successfully treat all of the precancers, then no cancers will follow.

-© 2009 Jules J. Berman

related words: precancers, precancer, pre-cancerous condition, precancerous condition, preneoplastic lesions, preneoplasia, ien, intra-epithelial neoplasia, intraepithelial neoplasia, intra-epithelial neoplasm, intraepithelial neoplasm, in situ carcinoma, carcinoma in situ, cis, dcis, din, pin, panin, cin, dysplasia, adenoma, preneoplastic, pre-cancer, pre-cancerous, precancerous

PRECANCERS: 2 Inducing regression in precancers

2009-08-17T04:53:00.000-07:00

Precancers are the lesions from which cancers develop. Precancers can be easily treated, and when we eliminate precancers, we eliminate the cancers that would have developed from the precancers. This is the message of my new book, Precancer: The Beginning and the End of Cancer. In today's blog, I thought I'd provide one example, from the recent cancer research literature, of precancer treatment.

Colon cancer is one of the most common cancers of humans, accounting for about 50,000 deaths in the U.S. in 2009 (data from U.S. National Cancer Institute at:

http://www.cancer.gov/cancerinfo/types/colon-and-rectal

Almost all colon cancers arise from colon precancers, called colonic adenomas.

If we could eliminate colon adenomas, we could eliminate virtually all deaths from colon cancer. This is the premise behind colonoscopy (excise the adenomas and avoid the cancers).

A team of scientists from MIT, Mass Genl, Harvard and several other institutions have published a recent article in PNAS (Proceedings of the National Academy of Sciences), on the successful treatment of colon precancers, in a mouse model system.

The article is:

Elias Gounaris, Susan E. Erdman, Clifford Restaino, et al. Mast cells are an essential hematopoietic component for polyp development Proc Natl Acad Sci U S A. 2007 December 11; 104(50): 19977-19982.

The full-text article is available as an open access document:

http://www.pnas.org/content/104/50/19977.full.pdf+html

The investigators noticed that the stroma (the supporting connective tissue) in colon adenomas is infiltrated with mast cells (a blood cell involved in the inflammatory process). When the investigators reduced the mast cell population (in the tumor stroma) in precancerous mouse colon adenomas, many of the adenomas quickly regressed. In a related study, chimeric mice with a genetic deficiency of mast cell formation developed fewer colon adenomas than wild-type mice.

Precancers, unlike cancers, often regress spontaneously. It seems self-evident that it would be easier to treat a lesion that is unstable, and prone to die on its own, than to treat a lesion that has grown into a large, invasive mass, that has dissemminated throughout the body. This paper is one example of how easy it is to modify the growth of precancers and to induce a high rate of precancer regression.

Because treatment was aimed at the non-neoplastic cells in the stroma of the precancer, it is likely that the same treatment that was successful against colon adenomas may be successful against any precancer for which there is a population of mast cells that contribute to precancer progression. The authors discussed the observation that mast cells are found in a variety of human cancers, and, in a note attached to their paper, cited recent work indicating that mast cells have an important role in the development of pancreatic cancer and prostatic cancer.

It is important to begin treatment while neoplasms are stil in their precancer stage. AFTER an invasive cancer has emerged from a precancer, agents that target a sensitive step in precancer development will probably NOT be curative.

When we have candidate precancer treatments (and we do), we should start testing them in human clinical trials. When we learn how to eradicate precancers, we will have learned how to conquer cancer.

-© 2009 Jules J. Berman

related words: precancers, preneoplastic lesions, preneoplasia, ien, intra-epithelial neoplasia, intraepithelial neoplasia, intra-epithelial neoplasm, intraepithelial neoplasm, in situ carcinoma, carcinoma in situ, cis, dcis, din, pin, panin, cin, dysplasia, adenoma, preneoplastic, pre-cancer, pre-cancerous, precancerous

Precancers: 1

2009-08-16T07:49:00.000-07:00

After a long stretch of blog inactivity, I am starting to post again. I apologize for the absence, but I've been deeply absorbed writing several books. Now that the writing phase is finished, I hope to use the blog to post short essays on topics covered in those books.

Readers of this blog are familiar with my interest in precancers. Precancer: The Beginning and the End of Cancer, was just published (August 11, 2009) by Jones & Bartlett Publishers, Inc., and is now available at Amazon. It was written with the help of Dr. G. William Moore. Here's the publisher's blurb:

"Nearly every type of cancer passes through a precancer phase, during which it cannot metastasize or invade other tissues. While medicine is not always successful in treating or curing advanced stages of cancers, recent advances in our understanding of carcinogenesis have helped us to develop strategies to prevent, diagnose, and treat many cancers at the precancer stage. Research in this field is escalating rapidly as the evidence increasingly shows that the number of annual cancer deaths could be drastically reduced through the effective treatment and cure of precancer lesions. This book begins by explaining why it has been so difficult to cure cancers, followed by a review of precancer biology, with descriptions of the most common precancer lesions. The final chapters provide practical socio-political and medical goals for precancer treatment, including discussions of the economics and politics of treating precancers."

I've been interested in the precancers for over thirty-six years, and I think it's a shame that the precancers have not gotten more attention from cancer researchers and from the public. My impression is that many cancer researchers fail to distinguish between "precancers" and "early cancers". Precancers are very different from early cancers. One of the most important properties of precancers is their tendency to regress [spontaneously, or with simple treatments]. The book clarifies the biological properties that distinguish precancers from other early [and late] cancers, and describes how we can stop cancers from developing by treating precancers.

More on precancers in subsequent posts.

Jules Berman

related words: precancers, preneoplastic lesions, preneoplasia, ien, intra-epithelial neoplasia, intraepithelial neoplasia, intra-epithelial neoplasm, intraepithelial neoplasm, in situ carcinoma, carcinoma in situ, cis, dcis, din, pin, panin, cin, dysplasia, adenoma, preneoplastic, pre-cancer, pre-cancerous, precancerous

TUMOR SPECIATION

2009-02-17T06:41:00.000-08:00

On February 12, the world celebrated the 200th anniversary of Charles Darwin's birth. Darwin is best known for explaining speciation; how life on earth diversifies into distinct types of organisms. Each species shares a characteristic set of traits that separate the species from all other species on earth. One of the most curious aspects of speciation is member uniqueness: every species is composed of members that are different from every other member of the same species. If each species is composed of genetically diverse members, how can we think in terms of "sameness" among the members of a species?

Speciation is a general phenomenon, not confined to animals. For example, the field of cancer research deals with many different kinds (species) of tumors. The phenomenon of tumor speciation is basically the same as the problem of animal speciation. Tumors, like animals, occur as highly characteristic species, but every tumor is genetically unique, different from every other tumor that has ever occurred or that will ever occur.

Although there are many kinds of tumors that can arise in humans and other animals, pathologists (the people who render diagnoses on tissue specimens) are adept at assigning names to every tumor occurrence: Warthin's tumor of salivary gland, fibrolamellar carcinoma of liver, papillary carcinoma of thyroid, carcinoid tumor of appendix, and many others. Each kind of tumor has a characteristic appearance when it is viewed under a microscope. Pathologists can instantly render a specific diagnosis on most human tumors. Genetic analyses of cancers conducted over the past decade have shown us that cancer is a complex disease, with tumors accumulating thousands of different genetic alterations as they grow. This being the case, why do we encounter distinctive tumor species? Should we not expect a free-for-all of infinitely diverse, unique tumors? In addition, we now know that tumors continue to collect genetic changes over time. Should we not expect tumors to change their type over time, one day seeming to be a carcinoma (tumor of epithelium), and another day seeming to be a lymphoma (tumor of the immune system) or a sarcoma (tumor of the connective tissues)?

In fact, this never occurs. Just as a cat never becomes a dog, and a hamster never becomes a frog; a lymphoma (a tumor of lymph cells) never becomes an adenocarcinoma (a tumor of glandular epithelial cells), and a melanoma (a tumor of melanin producing cells of the skin) never becomes a glioma (a tumor of the central nervous system cells).

How do we reconcile the uniqueness of every tumor with the constraints of tumor speciation?

This problem is fundamentally the same problem as animal speciation. In the case of animals, speciation occurs because of the genetic diversity among the different members of a species. New species arise from mating among a subpopulation of a species that preserves genetic traits that are expressed within the subpopulation, and that are not expressed in the general population of the species. If the individual animals within a population were genetically identical, and could not express new traits in subpopulations, speciation, would not occur. When a new species appears, it has some properties of the parent species, but it also has traits that distinguish it from the parent species and every other species on earth.

In cancers, the malignant phenotype is acquired primarily through genetic alterations, but the type of a tumor is acquired through cell lineage determined by epigenetic alterations.

The human body has several hundred different kinds of cells, and these cells pass through many different developmental stages on their way to becoming the fully functional cells we find in adult bodies. Every neoplasm, despite its genetic uniqueness, has a set of properties determined by the epigenetic modifications that characterize its cell-type of origin. These epigenetic features will be shared by other tumors arising from the same type of cell. Each of the many different species of tumors occurring in humans and animals has a microscopic appearance and biological behavior that has traits of the type of cell from which it arose. In addition, each type of cancer has a set of neoplastic traits whose expression is constrained by the pre-existing cellular pathways inherited from the parent cell type.

This explains how it is possible that every tumor that has ever occurred is a unique biological entity; and yet, every tumor that has ever occurred can be classified as a member of a distinctive species that shares morphologic and biological features with every other tumor of the same kind.

I have heard it said that Charles Darwin's "On the Origin of Species," has negligible impact on our daily lives. This is not so. Aside from its enormous impact on every aspect of the natural sciences, it is the basis for much of modern medicine: genetics, molecular biology, and pharmacology. Just as our knowledge of animal speciation permits us to organize and make sense of life on this planet, our knowledge of tumor speciation permits us to organize tumors into classes that share a common epigenetic (developmental) lineage. Cells of a common lineage (class) will likely share the same genetically altered pathways that determine their malignant properties. Drugs that control these pathways in any given tumor, will likely control the same pathways in all tumors of a common class. When we know the class of a species of tumor, we have a way of developing cures that apply to every species in the class.

- © 2009 Jules J. Berman

Tumor speciation is just one of many topics covered in my book, Neoplasms, Principles of Development and Diversity. Additional information is available at the publisher's web site.

key words: neoplasm, classification, cancer, tumour

- © 2009 Jules J. Berman

Tumor speciation is just one of many topics covered in my book, Neoplasms, Principles of Development and Diversity (2009), available at the publisher's web site.

key words: neoplasm, classification, cancer, tumour

Biomedical Informatics

2009-02-13T11:52:00.001-08:00

I've been quite busy the past few weeks and haven't added any new blog posts. I have at least one more I'd like to do on bimodal cancer distributions, and then I'd like to do a series of blogs on neoplastic development.

Today, I noticed that Amazon.com has deeply discounted one of my books, Biomedical Informatics.

I'm mentioning it here because my books have no discount, or negligible discounts, on Amazon, most of the time. Occasionally, for reasons that I cannot explain, they mark a book for discount. Just as mysteriously, after a period of time, they remove the discount.

For the moment, Amazon has reduced the price of Biomedical Informatics by 37%.

- Jules Berman

Update of Neoplasm Classification is now available

2009-01-28T04:40:00.001-08:00

I'm interrupting my series of blogs on bimodal cancer age distributions to announce the release of the most recent version of the Developmental Lineage Classification and Taxonomy of Neoplasms.

The current classification contains 6083 neoplasm concepts (types of neoplasms) classified under 122,698 terms. It also contains a large number of unclassified neoplasm terms as addendum items. It is, by far and away, the world's largest neoplasm nomenclature.

The classification is available in XML, RDF and flat-file formats. Here is the preface text distributed with each formatted version:

"This file was prepared by Jules J. Berman. The first version of this file was created November 15, 2003. The current version was created on January 27, 2009.

Copyright © 2003-2009 Jules J. Berman

Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is available.

The neoclxml file is provided "as is", without warranty of any kind, expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. in no event shall the author or copyright holder be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.

An explanation of the classification can be found in the following two publications, which should be cited in any publication or work that may result from any use of this file.

Berman JJ. Tumor classification: molecular analysis meets Aristotle. BMC Cancer 4:8, 2004.

Berman JJ. Neoplasms: Principles of Development and Diversity. Jones and Bartlett Publishers, Sudbury, MA, 2009.

In the Neoplasm Classification, all classified names of neoplasms are coded with a "C" followed by a 7 digit number other than 0000000.

For example, "C9168000" = rectal signet ring adenocarcinoma

In addition to classified terms, there are three groups of unclassified terms that are provided special items that follow the list of classified terms in this file.

"C0000000"
"S" followed by 7 digits
"ST" followed by 7 digits

This list of unclassified terms coded as "C0000000" consists of general cancer terms that do not specify any particular neoplasm; overly specific terms that provide so-call pre-coordinated annotations related to terms contained elsewere in the Classification; and valid terms that have not been added (yet) to the list of classified neoplasm terms.

Examples of overly specific terms are:

squamous carcinoma of the nasal vestibule, gastric non-hodgkin lymphoma of mucosa-associated lymphoid tissue, primary primitive neuroectodermal tumor of the kidney

The terms that are coded "S" followed by 7 digits are inherited syndromes that have a neoplastic component (i.e., the occasional or frequent appearance of neoplasms in the syndrome).

The terms that are coded "ST" followed by 7 digits are staging terms used by oncologists.

The classification is meant for informatics projects that use computer parsing techniques. Programmers should simply insert statements that filter the unclassified terms included in the file."

Additional information may be available from the author's web site:
http://www.julesberman.info/devclass.htm

The Neoplasm Classification is available as a zipped XML file at:
http://www.julesberman.info/neoclxml.zip

The Neoplasm Classification is available as a zipped flat file at:
http://www.julesberman.info/neoself.zip

The Neoplasm Classification is available as a zipped RDF file at:
http://www.julesberman.info/neordf.zip

© 2009 Jules Berman

key words: medical nomenclature, classification, rdf, xml, ontology, data mining