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Record W2152910533 · doi:10.1186/1710-1492-4-1-37

Epigenetics, Behaviour, and Health

2008· article· en· W2152910533 on OpenAlexafffundvenue
Moshe Szyf, Michael J. Meaney

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsDouglas CollegeMcGill University
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsEpigeneticsPsychologyBiologyGenetics

Abstract

fetched live from OpenAlex

: The long-term effects of behaviour and environmental exposures, particularly during childhood, on health outcomes are well documented. Particularly thought provoking is the notion that exposures to different social environments have a long-lasting impact on human physical health. However, the mechanisms mediating the effects of the environment are still unclear. In the last decade, the main focus of attention was the genome, and interindividual genetic polymorphisms were sought after as the principal basis for susceptibility to disease. However, it is becoming clear that recent dramatic increases in the incidence of certain human pathologies, such as asthma and type 2 diabetes, cannot be explained just on the basis of a genetic drift. It is therefore extremely important to unravel the molecular links between the "environmental" exposure, which is believed to be behind this emerging incidence in certain human pathologies, and the disease's molecular mechanisms. Although it is clear that most human pathologies involve long-term changes in gene function, these might be caused by mechanisms other than changes in the deoxyribonucleic acid (DNA) sequence. The genome is programmed by the epigenome, which is composed of chromatin and a covalent modification of DNA by methylation. It is postulated here that "epigenetic" mechanisms mediate the effects of behavioural and environmental exposures early in life, as well as lifelong environmental exposures and the susceptibility to disease later in life. In contrast to genetic sequence differences, epigenetic aberrations are potentially reversible, raising the hope for interventions that will be able to reverse deleterious epigenetic programming.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.334
Teacher spread0.297 · 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 teacher head, 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

Citations29
Published2008
Admission routes3
Has abstractyes

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