Synergetic effects of quetiapine and venlafaxine in preventing the chronic restraint stress‐induced decrease in cell proliferation and BDNF expression in rat hippocampus
Bibliographic record
Abstract
Clinical studies show better response rates of patients with depression and schizophrenia to combinations of atypical antipsychotics and antidepressants, compared to responses to either type of drugs alone. Animal studies demonstrate that some antipsychotics and antidepressants increase neurogenesis and BDNF expression in the hippocampus, which is reduced in volume in patients with depression or schizophrenia. We hypothesized that the better therapeutic effects of combined treatment seen in schizophrenia and depression patients are related to the additive or synergistic effects of combined treatment on hippocampal neurogenesis and BDNF expression. To test this hypothesis, we investigated the effects of chronic administration of quetiapine, venlafaxine, and their combination, on hippocampal cell proliferation and BDNF expression in rats, when subjected to chronic restraint stress (CRS) during the last 2 weeks of a 3-week drug administration period. We found (1) CRS decreased hippocampal cell proliferation and BDNF expression; (2) chronic administration of quetiapine or venlafaxine dose-dependently prevented these decreases in hippocampal cell proliferation and BDNF expression caused by CRS (6 h/day for 14 days); (3) the combination of lower doses of quetiapine (5 mg/kg) and venlafaxine (2.5 mg/kg) increased hippocampal cell proliferation and prevented BDNF decrease in stressed rats, whereas each of the drugs exerted mild or no effects; (4) individual higher doses of quetiapine (10 mg/kg) or venlafaxine (5 mg/kg) exerted effects comparable to those produced by their combination. These results support our hypothesis and can lead to future studies to develop new therapeutic approaches for treatment-resistant depression and the negative symptoms of schizophrenia.
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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 | 0.000 |
| 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".