Biological Sex Influences the Contribution of Sign-Tracking and Anxiety-Like Behaviour toward Remifentanil Self-Administration
Bibliographic record
Abstract
Abstract Most people sample addictive drugs, but use becomes disordered in only a small minority. Two important factors that influence susceptibility to addiction are individual differences in personality traits and biological sex. The influence of traits on addiction-like behaviour is well characterized in preclinical models of cocaine self-administration, but less is understood in regards to opioids. How biological sex influences trait susceptibility to opioid self-administration is likewise less studied than psychostimulants. Thus, we sought to elucidate how biological sex and several addiction-relevant traits interact with the propensity to self-administer the opioid remifentanil. We first screened female ( n =19) and male ( n =19) rats for four addiction-relevant traits: impulsivity, novelty place-preference, anxiety-like behaviour, and attribution of incentive value to reward cues. Rats were then trained to self-administer remifentanil in a “conflict model” of drug self-administration. Rats had to endure a mild electric shock to access the response manipulandum that triggered an intravenous infusion of remifentanil. In male rats, high anxiety-like behaviour was positively correlated with the number of drug infusions if the shock level was low or completely absent. In females, sign-tracking was predictive of greater resistance to punishment during drug seeking; an effect that was mediated by anxiety-like behaviour. Females consumed more remifentanil under all conditions, and their drug seeking persisted in the face of significantly greater current than males. These findings demonstrate that the influence of behavioural traits over the propensity to self-administer opioids is dependent upon biological sex.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".