Quetiapine Inhibits Microglial Activation by Neutralizing Abnormal STIM1-Mediated Intercellular Calcium Homeostasis and Promotes Myelin Repair in a Cuprizone-Induced Mouse Model of Demyelination
Bibliographic record
Abstract
Microglial activation has been considered as a crucial process in the pathogenesis of neuroinflammation and psychiatric disorders. Several antipsychotic drugs (APDs) have been shown to display inhibitory effects on microglial activation in vitro, possibly through the suppression of elevated intracellular calcium (Ca(2+)) concentration. However, the exact underlying mechanisms still remain elusive. In this study, we aimed to investigate the inhibitory effects of quetiapine (Que), an atypical APD, on microglial activation. We utilized a chronic cuprizone (CPZ)-induced demyelination mouse model to determine the direct effect of Que on microglial activation. Our results showed that treatment with Que significantly reduced recruitment and activation of microglia/macrophage in the lesion of corpus callosum and promoted remyelination after CPZ withdrawal. Our in vitro studies also confirmed the direct effect of Que on lipopolysaccharide (LPS)-induced activation of microglial N9 cells, whereby Que significantly inhibited the release of nitric oxide (NO) and tumor necrosis factor α (TNF-α). Moreover, we demonstrated that pretreatment with Que, neutralized the up-regulation of STIM1 induced by LPS and declined both LPS and thapsigargin (Tg)-induced store-operated Ca(2+) entry (SOCE). Finally, we found that pretreatment with Que significantly reduced the translocation of nuclear factor kappa B (NF-κB) p65 subunit from cytoplasm to nuclei in LPS-activated primary microglial cells. Overall, our data suggested that Que may inhibit microglial activation by neutralization of the LPS-induced abnormal STIM1-mediated intercellular calcium homeostasis.
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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) | 0.001 | 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 | 0.001 |
| 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".