Quetiapine, an Atypical Antipsychotic, Is Protective against Autoimmune-Mediated Demyelination by Inhibiting Effector T Cell Proliferation
Bibliographic record
Abstract
Quetiapine (Que), a commonly used atypical antipsychotic drug (APD), can prevent myelin from breakdown without immune attack. Multiple sclerosis (MS), an autoimmune reactive inflammation demyelinating disease, is triggered by activated myelin-specific T lymphocytes (T cells). In this study, we investigated the potential efficacy of Que as an immune-modulating therapeutic agent for experimental autoimmune encephalomyelitis (EAE), a mouse model for MS. Que treatment was initiated on the onset of MOG(35-55) peptide induced EAE mice and the efficacy of Que on modulating the immune response was determined by Flow Cytometry through analyzing CD4(+)/CD8(+) populations and the proliferation of effector T cells (CD4(+)CD25(-)) in peripheral immune organs. Our results show that Que dramatically attenuates the severity of EAE symptoms. Que treatment decreases the extent of CD4(+)/CD8(+) T cell infiltration into the spinal cord and suppresses local glial activation, thereby diminishing the loss of mature oligodendrocytes and myelin breakdown in the spinal cord of EAE mice. Our results further demonstrate that Que treatment decreases the CD4(+)/CD8(+) T cell populations in lymph nodes and spleens of EAE mice and inhibits either MOG(35-55) or anti-CD3 induced proliferation as well as IL-2 production of effector T cells (CD4(+)CD25(-)) isolated from EAE mice spleen. Together, these findings suggest that Que displays an immune-modulating role during the course of EAE, and thus may be a promising candidate for treatment of MS.
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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".