Fluoxetine reverses the behavioral despair induced by neurogenic stress in mice: role of <i>N</i>-methyl-<scp>d</scp>-aspartate and opioid receptors
Bibliographic record
Abstract
Opioid and N-methyl-d-aspartate (NMDA) receptors mediate different effects of fluoxetine. We investigated whether opioid and NMDA receptors are involved in the protective effect of fluoxetine against the behavioral despair induced by acute physical stress in male mice. We used the forced swimming test (FST), tail suspension test (TST), and open-field test (OFT) for behavioral evaluation. We used fluoxetine, naltrexone (opioid receptor antagonist), MK-801 (NMDA receptor antagonist), morphine (opioid receptor agonist), and NMDA (NMDA receptor agonist). Acute foot-shock stress (FSS) significantly induced behavioral despair (depressive-like) and anxiety-like behaviors in tests. Fluoxetine (5 mg/kg) reversed the depressant-like effect of FSS, but it did not alter the locomotion and anxiety-like behavior in animals. Acute administration of subeffective doses of naltrexone (0.3 mg/kg) or MK-801 (0.01 mg/kg) potentiated the antidepressant-like effect of fluoxetine, while subeffective doses of morphine (1 mg/kg) and NMDA (75 mg/kg) abolished this effect of fluoxetine. Also, co-administration of subeffective doses of naltrexone (0.05 mg/kg) and MK-801 (0.003 mg/kg) with fluoxetine (1 mg/kg) induced a significant decrease in the immobility time in FST and TST. Our results showed that opioid and NMDA receptors (alone or in combination) are involved in the antidepressant-like effect of fluoxetine against physical stress.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".