Quetiapine regulates neurogenesis in ischemic mice by inhibiting NF-<i>κ</i>B p65/p50 expression
Bibliographic record
Abstract
OBJECTIVES: Previously, we showed that quetiapine, an atypical antipsychotic drug, significantly attenuated neurodegeneration induced by global cerebral ischemia (GCI). The present work investigates the effects of quetiapine on neurogenesis. METHODS: Mice were treated with quetiapine (10 or 20 mg/kg/day; intraperitoneal injection) for 2 weeks and then subjected to GCI on day 15. Seven days after GCI, the mice were killed. Neuronal injury and neurogenesis were analysed using hematoxylin-eosin and 5-bromo-20-deoxyuridine stainings. Levels of nuclear factor kappaB (NF-kappaB) p65/p50 expressions were determined by immunohistochemistry and Western blot analysis. RESULTS: Global cerebral ischemia resulted in neuronal injury, neurogenesis and NF-kappaB p65/p50 expressions in hippocampus, especially in the dentate gyrus. Pre-administration of quetiapine significantly alleviated neuronal injury, while inhibiting neurogenesis and down-regulating NF-kappaB p65/p50 expression. DISCUSSION: NF-kappaB plays a key role in regulating neuron damage and neurogenesis. This work suggests that down-regulation of NF-kappaB expression may be one of the mechanisms by which quetiapine inhibits neurogenesis.
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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".