Anti-Inflammatory Properties of Antipsychotics Via Microglia Modulations: Are Antipsychotics a ‘Fire Extinguisher’ in the Brain of Schizophrenia?
Bibliographic record
Abstract
Schizophrenia is one of the most severe psychiatric diseases noted for its chronic and often debilitating processes; affecting approximately 1% of the world's population, while its etiology and therapeutic strategies still remain elusive. In the 1950s, the discovery of antipsychotic effects of haloperidol and chlorpromazine shifted the paradigm of schizophrenia. These drugs proved to be antagonists of dopamine D2 receptor (D2R), thus dopamine system dysfunction came to be hypothesized in the pathophysiology of schizophrenia, and D2R antagonism against dopamine neurons has been considered as the primary therapeutic target for schizophrenia. In addition, abnormalities of glutamatergic neurons have been indicated in the pathophysiology of schizophrenia. On the other hand, recent neuroimaging studies have shown that not only dementia but also schizophrenic patients have a significant volume reduction of some specific regions in the brain, which indicates that schizophrenia may involve some neurodegenerative process. Microglia, major sources of various inflammatory cytokines and free radicals such as superoxide and nitric oxide (NO) in the CNS, play a crucial role in a variety of neurodegenerative diseases such as dementia. Recent postmortem and positron emission computed tomography (PET) studies have indicated that activated microglia may be present in schizophrenic patients. Recent in vitro studies have suggested the anti-inflammatory effects of antipsychotics on microglial activation. In this article, we review the anti-inflammatory effects of antipsychotics on microglia, and propose a novel therapeutic hypothesis of schizophrenia from the perspective of microglial modulation.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".