P2‐211: Quetiapine prevents memory impairment, up‐regulates Bcl‐2 expression and attenuates oxidative stress in an APP/PS1 double transgenic mouse model of Alzheimer's disease
Bibliographic record
Abstract
Previous studies have suggested that quetiapine, a new atypical antipsychotic drug, may have beneficial effects on cognitive impairment and be a neuroprotectant in treating neurodegenerative diseases. In the present study, we investigated the effects of quetiapine on memory impairment and its possible neuroprotective effects in an amyloid precursor protein (APP)/presenilin1 (PS1) double transgenic mouse model of Alzheimer's disease (AD). Non-transgenic and transgenic mice were treated with quetiapine (0, 2.5, or 5 mg/kg/day) in drinking water from the age of 2 months. After continuous treatment with quetiapine for 4 months, mice were tested for memory on a water maze task. After the behavioral test, mice were sacrificed for Western blot and biochemical measurements at the age of 6 months. The chronic administration of quetiapine prevented memory impairment in the transgenic mice. Furthermore, quetiapine up-regulated cerebral Bcl-2 protein, and attenuated cerebral nitrotyrosine, a protein marker of oxidative stress in the transgenic mice. These findings suggest that quetiapine can alleviate cognitive impairment in an APP/PS1 double transgenic mouse model of AD, and further indicate that quetiapine may have preventive and neuroprotective effects in the treatment of AD.
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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".