Quetiapine Attenuates Glial Activation and Proinflammatory Cytokines in APP/PS1 Transgenic Mice via Inhibition of Nuclear Factor- B Pathway
Bibliographic record
Abstract
BACKGROUND: In Alzheimer's disease, growing evidence has shown that uncontrolled glial activation and neuroinflammation may contribute independently to neurodegeneration. Antiinflammatory strategies might provide benefits for this devastating disease. The aims of the present study are to address the issue of whether glial activation and proinflammatory cytokine increases could be modulated by quetiapine in vivo and in vitro and to explore the underlying mechanism. METHODS: Four-month-old amyloid precursor protein (APP) and presenilin 1 (PS1) transgenic and nontransgenic mice were treated with quetiapine (5mg/kg/d) in drinking water for 8 months. Animal behaviors, total Aβ levels, and glial activation were evaluated by behavioral tests, enzyme-linked immunosorbent assay, immunohistochemistry, and Western blot accordingly. Inflammatory cytokines and the nuclear factor kappa B pathway were analyzed in vivo and in vitro. RESULTS: Quetiapine improves behavioral performance, marginally affects total Aβ40 and Aβ42 levels, attenuates glial activation, and reduces proinflammatory cytokines in APP/PS1 mice. Quetiapine suppresses Aβ1-42-induced activation of primary microglia by decresing proinflammatory cytokines. Quetiapine inhibits the activation of nuclear factor kappa B p65 pathway in both transgenic mice and primary microglia stimulated by Aβ1-42. CONCLUSIONS: The antiinflammatory effects of quetiapine in Alzheimer's disease may be involved in the nuclear factor kappa B pathway. Quetiapine may be an efficacious and promising treatment for Alzheimer's disease targeting on neuroinflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".