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Record W2927351944 · doi:10.1530/joe-19-0057

Developmental programming of the HPA axis and related behaviours: epigenetic mechanisms

2019· review· en· W2927351944 on OpenAlexafffund
Stephen G. Matthews, Patrick O. McGowan

Bibliographic record

VenueJournal of Endocrinology · 2019
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaU.S. Department of Defense
KeywordsEpigeneticsIntervention (counseling)DNA methylationDiseaseJuvenilePsychological interventionMechanism (biology)BiologyDevelopmental psychologyEpigenesisPsychologyBioinformaticsNeuroscienceMedicineGeneticsGeneInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

It has been approximately 30 years since the seminal discoveries of David Barker and his colleagues, and research is beginning to unravel the mechanisms that underlie developmental programming. The early environment of the embryo, foetus and newborn have been clearly linked to altered hypothalamic-pituitary-adrenal (HPA) function and related behaviours through the juvenile period and into adulthood. A number of recent studies have shown that these effects can pass across multiple generations. The HPA axis is highly responsive to the environment, impacts both central and peripheral systems and is critical to health in a wide variety of contexts. Mechanistic studies in animals are linking early exposures to adversity with changes in gene regulatory mechanisms, including modifications of DNA methylation and altered levels of miRNA. Similar associations are emerging from recent human studies. These findings suggest that epigenetic mechanisms represent a fundamental link between adverse early environments and developmental programming of later disease. The underlying biological mechanisms that connect the perinatal environment with modified long-term health outcomes represent an intensive area of research. Indeed, opportunities for early interventions must identify the relevant environmental factors and their molecular targets. This new knowledge will likely assist in the identification of individuals who are at risk of developing poor outcomes and for whom early intervention is most effective.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.349
Teacher spread0.287 · 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 designOther design
Domainnot available
GenreReview

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

Citations77
Published2019
Admission routes2
Has abstractyes

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