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Record W4392701125 · doi:10.1002/dev.22479

The role of epigenetic mechanisms in the long‐term effects of early‐life adversity and mother–infant relationship on physiology and behavior of offspring in laboratory rats and mice

2024· review· en· W4392701125 on OpenAlexaff
Olga Burenkova, Elena L. Grigorenko

Bibliographic record

VenueDevelopmental Psychobiology · 2024
Typereview
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsUniversity of Guelph
FundersNational Center for Research ResourcesNational Institute of Child Health and Human DevelopmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentMinistry of Science and Higher Education of the Russian FederationRussian Foundation for Basic Research
KeywordsOffspringAltricialEpigeneticsPhysiologyPsychologyDevelopmental psychologyNeuroscienceNeurotrophic factorsMedicineBiologyPregnancyInternal medicineZoology

Abstract

fetched live from OpenAlex

Maternal care during the early postnatal period of altricial mammals is a key factor in the survival and adaptation of offspring to environmental conditions. Natural variations in maternal care and experimental manipulations with maternal-child relationships modeling early-life adversity (ELA) in laboratory rats and mice have a strong long-term influence on the physiology and behavior of offspring in rats and mice. This literature review is devoted to the latest research on the role of epigenetic mechanisms in these effects of ELA and mother-infant relationship, with a focus on the regulation of hypothalamic-pituitary-adrenal axis and brain-derived neurotrophic factor. An important part of this review is dedicated to pharmacological interventions and epigenetic editing as tools for studying the causal role of epigenetic mechanisms in the development of physiological and behavioral profiles. A special section of the manuscript will discuss the translational potential of the discussed research.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.329
Teacher spread0.302 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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