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Record W1494710819 · doi:10.1155/2006/815307

A Burning Desire to Understand Genetic Mutations in Gastroenterology

2006· article· en· W1494710819 on OpenAlexaffvenueabout
Paul C. Adams, Peter Ainsworth

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2006
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsInternal medicineMedicinePsychologyGastroenterology

Abstract

fetched live from OpenAlex

Dr Peter Ainsworth is an Adjunct Professor in the Department of Biochemistry at the University of Western Ontario, London, Ontario. He is the Director of the Molecular Diagnostics Laboratory of the London Laboratory Services Group, which provides diagnostic genetic testing for Southwestern Ontario. The Human Genome Project has greatly expanded our understanding of the pathogenesis of gastrointestinal diseases. However, the function of many identified genes remains unknown. Molecular genetics is a relatively new scientific field, and thus, many of its fundamental concepts would not have been part of a gastroenterologist’s education. In the present review, an analogy is made between genetic mutations and digital music copying to increase our comprehension of this complex field. In 2003, the Human Genome Project announced their results from a 13-year multinational study which identified approximately 30,000 genes in human DNA. This achievement represents a landmark in modern medicine; however, the field of molecular genetics can be bewildering to the practicing clinician, who may only see an array of alphabet soup and overly complex scientific articles. Simply put, a genome represents all of the nuclear DNA in an organism, including the coding sequences carried in the genes. Genes carry information for making proteins that are involved in cell function. DNA is much like a double-stranded beaded necklace made up of four bases, strung out in pairs (guanine-cytosine [G-C] and adenine-thymine [A-T]) along two interwoven strands of a sugar-phosphate polymer backbone, to make up a double helix comprising the genome of an organism. Fully extended, the DNA in humans is approximately 2 m in length and contains approximately 3.5 × 109 base pairs. The use of digital computer technology to record and copy music is also a complex scientific field. Although the compact disc (CD) was only introduced in 1983 by Royal Philips Electronics Ltd (The Netherlands), the pulse code modulation technology used on CDs was developed in 1937 by British scientist Alec Reeves. The concept for the CD was developed in 1969 by a Dutch physicist, Klaas Compaan, and an American scientist, James T Russell. The first recordable CD, which led to the copying of CDs, was released in 1988. Today, millions of music and video discs have been sold and copied, and the entertainment industry is convinced that this has led to a major decline in their sales and profits. The lessons learned from music copying are used as an analogy in the present article for the study of genetic mutations associated with gastroenterological diseases. Dr Peter Ainsworth is an Adjunct Professor in the Department of Biochemistry at the University of Western Ontario, London, Ontario In this analogy, we will assume that our genetic code is the digital information on the original disc and during the process of copying, genetic mutations have developed. When the disc was introduced, it was assumed that the copying of the digital information would be ‘perfect’; however, it is now well known that many of the copied CDs do not contain exactly the same information as the original. This may be related to burning speed, compression of digital data, copy protection methods, quality of the copier and disc, as well as the algorithm for the data transfer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.009
Scholarly communication0.0050.017
Open science0.0020.004
Research integrity0.0090.029
Insufficient payload (model declined to judge)0.0070.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.241
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2006
Admission routes3
Has abstractyes

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