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Record W2564990978 · doi:10.3389/fendo.2016.00158

Dynamic Changes in DNA Methylation Occur during the First Year of Life in Preterm Infants

2016· article· en· W2564990978 on OpenAlexfundno aff
Chinthika Piyasena, Jessy Cartier, Nadine Provençal, Tobias Wiechmann, Batbayar Khulan, Raju Sunderesan, Gopi Menon, Jonathan R. Seckl, Rebecca M. Reynolds, Elisabeth B. Binder, Amanda J. Drake

Bibliographic record

VenueFrontiers in Endocrinology · 2016
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsnot available
FundersEuropean Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchDiabetes UKScottish Funding CouncilBritish Heart FoundationWellcome Trust
KeywordsDNA methylationCpG siteMedicineFKBP5Birth weightMethylationGestational agePediatricsPhysiologyPregnancyBiologyGeneticsInternal medicineGene

Abstract

fetched live from OpenAlex

BACKGROUND: Preterm birth associates with a substantially increased risk of later cardiovascular disease and neurodevelopmental disorders. Understanding underlying mechanisms will facilitate the development of screening and intervention strategies to reduce disease risk. Changes in DNA methylation have been proposed as one mechanism linking the early environment with later disease risk. We tested the hypothesis that preterm birth associates with altered DNA methylation in genes encoding insulin-like growth factor 2 (IGF2) and FK506-binding protein 5 (FKBP5), which appear particularly vulnerable to early life adversity. METHODS: loci. RESULTS: at birth. There were no persistent differences in DNA methylation at 1 year of age. CONCLUSION: in preterm infants in early life. Potential contributing factors include maternal smoking and social deprivation. However, these changes did not persist at 1 year of age and further longitudinal studies are required to determine any associations between altered DNA methylation in the perinatal period of individuals born preterm and their long-term health.

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 score0.015
Threshold uncertainty score0.273

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 head0.013
GPT teacher head0.267
Teacher spread0.253 · 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

Citations33
Published2016
Admission routes1
Has abstractyes

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