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Naturally Occurring Differences in Maternal Care are Associated with the Expression of Oxytocin and Vasopressin (V1a) Receptors: Gender Differences

2002· article· en· W1511351296 on OpenAlexaff
Darlene Francis, Larry J. Young, Michael J. Meaney, Thomas R. Insel

Bibliographic record

VenueJournal of Neuroendocrinology · 2002
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsStria terminalisOxytocinLickingVasopressinOxytocin receptorInternal medicineEndocrinologyAmygdalaOffspringReceptorNeuropeptideHypothalamusVasopressin receptorPsychologyMedicineBiologyPregnancyAntagonist

Abstract

fetched live from OpenAlex

Variations in maternal care have been associated with long-term changes in neurochemistry and behaviour in adult rats. Rats receiving high levels of licking and grooming as pups are less fearful and more maternal than rats receiving low levels of maternal licking and grooming. Central pathways for oxytocin and vasopressin have been implicated in the neurobiology of anxiety and social behaviours. We assessed whether variations in maternal care were associated with differences in oxytocin receptors (OTR) or vasopressin (V1a) receptors in the brains of adult offspring. In the central nucleus of the amygdala and bed nucleus of the stria terminalis, OTR binding was increased in adult females, but not adult males, that had received high levels of maternal licking and grooming as pups. Conversely, amygdala V1a receptor binding was increased in males, but not females, that had received high levels of maternal licking and grooming. These findings suggest that variations in maternal care may influence the expression of oxytocin and vasopressin receptors in a gender-specific manner.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.057
GPT teacher head0.286
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations416
Published2002
Admission routes1
Has abstractyes

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