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Record W1994797683 · doi:10.3389/fnins.2014.00420

Influence of sex and stress exposure across the lifespan on endophenotypes of depression: focus on behavior, glucocorticoids, and hippocampus

2015· review· en· W1994797683 on OpenAlexaff
Aarthi R. Gobinath, Rand Mahmoud, Liisa A.M. Galea

Bibliographic record

VenueFrontiers in Neuroscience · 2015
Typereview
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEndophenotypeDepression (economics)StressorPsychologyHippocampusChronic stressVulnerability (computing)Clinical psychologyNeuroplasticityDevelopmental psychologyNeuroscienceMedicineCognition

Abstract

fetched live from OpenAlex

Sex differences exist in vulnerability, symptoms, and treatment of many neuropsychiatric disorders. In this review, we discuss both preclinical and clinical research that investigates how sex influences depression endophenotypes at the behavioral, neuroendocrine, and neural levels across the lifespan. Chronic exposure to stress is a risk factor for depression and we discuss how stress during the prenatal, postnatal, and adolescent periods differentially affects males and females depending on the method of stress and metric examined. Given that the integrity of the hippocampus is compromised in depression, we specifically focus on sex differences in how hippocampal plasticity is affected by stress and depression across the lifespan. In addition, we examine how female physiology predisposes depression in adulthood, specifically in postpartum and perimenopausal periods. Finally, we discuss the underrepresentation of women in both preclinical and clinical research and how this limits our understanding of sex differences in vulnerability, presentation, and treatment of depression.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.321
Teacher spread0.285 · 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 designNot applicable
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

Citations108
Published2015
Admission routes1
Has abstractyes

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