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Record W2097619644 · doi:10.1186/1471-2393-13-s1-s9

Transgenerational programming of maternal behaviour by prenatal stress

2013· article· en· W2097619644 on OpenAlexafffund
Isabelle Ward, Fabíola CR Zucchi, Jerrah C. Robbins, Erin A. Falkenberg, David M. Olson, Karen Benzies, Gerlinde A. S. Metz

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2013
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsUniversity of CalgaryUniversity of AlbertaUniversity of Lethbridge
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsTransgenerational epigeneticsOffspringPrenatal stressMedicineSSS*GestationPregnancyReproductive medicinePhysiologyObstetricsInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

Peripartum events hold the potential to have dramatic effects in the programming of physiology and behaviour of offspring and possibly subsequent generations. Here we have characterized transgenerational changes in rat maternal behaviour as a function of gestational and prenatal stress. Pregnant dams of the parental generation were exposed to stress from days 12-18 (F0-S). Their daughters and grand-daughters were either stressed (F1-SS, F2-SSS) or non-stressed (F1-SN, F2-SNN). Maternal antepartum behaviours were analyzed at a time when pregnant dams usually show a high frequency of tail chasing behaviours. F1-SS, F2-SNN and F2-SSS groups showed a significant reduction in tail chasing behaviours when compared with controls. The effects of multigenerational stress (SSS) slightly exceeded those of transgenerational stress (SNN) and resulted in absence of tail chasing behaviour. These findings suggest that antepartum maternal behaviour in rats is programmed by transgenerational inheritance of stress responses. Thus, altered antepartum maternal behaviour may serve as an indicator of an activated stress response during gestation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

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)0.0010.000

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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.

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

Citations72
Published2013
Admission routes2
Has abstractyes

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