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Pain Reduces Sexual Motivation in Female But Not Male Mice

2014· article· en· W1973105354 on OpenAlexafffund
Melissa A. Farmer, Alison Leja, Emily Foxen‐Craft, Lindsey Chan, Leigh MacIntyre, Tina Niaki, Mengsha Chen, Josiane C.S. Mapplebeck, Vanessa Tabry, Lucas Topham, Melissa Sukosd, Yitzchak M. Binik, James G. Pfaus, Jeffrey S. Mogil

Bibliographic record

VenueJournal of Neuroscience · 2014
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsConcordia UniversityMcGill University
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthLouise and Alan Edwards Foundation
KeywordsLibidoAnalgesicNociceptionPsychologyContext (archaeology)Sexual dysfunctionApomorphineMedicineDevelopmental psychologyInternal medicinePhysiologyEndocrinologyPsychiatryBiologyDopamine

Abstract

fetched live from OpenAlex

Chronic pain is often associated with sexual dysfunction, suggesting that pain can reduce libido. We find that inflammatory pain reduces sexual motivation, measured via mounting behavior and/or proximity in a paced mating paradigm, in female but not male laboratory mice. Pain was produced by injection of inflammogens zymosan A (0.5 mg/ml) or λ-carrageenan (2%) into genital or nongenital (hind paw, tail, cheek) regions. Sexual behavior was significantly reduced in female mice experiencing pain (in all combinations); male mice similarly treated displayed unimpeded sexual motivation. Pain-induced reductions in female sexual behavior were observed in the absence of sex differences in pain-related behavior, and could be rescued by the analgesic, pregabalin, and the libido-enhancing drugs, apomorphine and melanotan-II. These findings suggest that the well known context sensitivity of the human female libido can be explained by evolutionary rather than sociocultural factors, as female mice can be similarly affected.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.069
GPT teacher head0.344
Teacher spread0.275 · 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 designBench or experimental
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

Citations34
Published2014
Admission routes2
Has abstractyes

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