Dapoxetine Treatment Leads to Attenuation of Chronic Unpredictable Stress Induced Behavioral Deficits in Rats Model of Depression
Bibliographic record
Abstract
Stressful conditions possess a complex relationship with brain and body’s reaction to stress and beginning of depression. The hypofunctioning of Serotonin (5-Hydroxytryptamine; 5-HT) is known to be established in unpredictable chronic mild stress exposure. UCMS is broadly taken as the most promising and favorable model to study depression in various animals, imitating many human depressive symptoms. With the class of selective serotonin [5-hydroxytryptamine (5-HT)] reuptake inhibitors (SSRIs) is now considered as the most prescribed antidepressant that can reverse petrochemical and behavioral effects of stresses. The aim of the present study was to investigate whether repeated administration of dapoxetine at dose 1.0 mg/kg could reversed the behavioral deficits induced by UCMS in rat model of depression. Rats exposed to UCMS revealed a significant reduction in food intake as well as growth rate. Locomotive activity in home cage and anxiolytic behavior in light/dark activity box were greater in animals of unstressed group as compared to animals of stressed group. The mechanism involved in the inhibition of serotonin reuptake at pre-synaptic receptors by repeated dapoxetine administration is discussed. The knowledge accumulated may facilitate an innovative approach for extending the therapeutic use of dapoxetine and the interaction between stress and behavioral functions.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".