Electrophysiological effects of the co-administration of escitalopram and bupropion on rat serotonin and norepinephrine neurons
Bibliographic record
Abstract
Clinical studies indicate that addition of bupropion to selective serotonin (5-HT) reuptake inhibitors (SSRIs) provides incremental benefit over SSRI monotherapy in depression. This study was designed to investigate the effects of co-administration of bupropion with escitalopram on the firing rate of 5-HT and norepinephrine (NE) neurons in anesthetized rats. Escitalopram (10 mg/kg/day x 2 days), given via subcutaneously (s.c.) implanted minipumps, decreased the firing of 5-HT and NE neurons by 70% and 55%, respectively. The firing of 5-HT neurons, unlike that of NE neurons, recovered after the 14-day escitalopram regimen. Bupropion, injected once daily (30 mg/kg/day, s.c. x 2 days), did not increase 5-HT firing but decreased that of NE by 55%. After 14 days of repeated bupropion administration, 5-HT firing was increased by 50%, and NE firing was back to baseline. Co-administration of escitalopram and bupropion doubled 5-HT firing after 2 and 14 days, whereas NE neurons were inhibited by 60% after 2 days, but partially recovered after 14 days. The responsiveness of 5-HT(1A) autoreceptors was significantly attenuated in the combination-treated rats after 2 days, indicating an early desensitization. These results provide support for contributions from 5-HT and NE mechanisms for enhanced effectiveness of combination of SSRI and bupropion treatment.
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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) | 0.000 | 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 | 0.001 |
| 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".