Neuroinflammation and glial cell activation in mental disorders
Bibliographic record
Abstract
Mental disorders (MDs) are highly prevalent and potentially debilitating complex disorders which causes remain elusive. Looking into deeper aspects of etiology or pathophysiology underlying these diseases would be highly beneficial, as the scarce knowledge in mechanistic and molecular pathways certainly represents an important limitation. Association between MDs and inflammation/neuroinflammation has been widely discussed and accepted by many, as high levels of pro-inflammatory cytokines were reported in patients with several MDs, such as schizophrenia (SCZ), bipolar disorder (BD) and major depression disorder (MDD), among others. Correlation of pro-inflammatory markers with symptoms intensity was also reported. However, the mechanisms underlying the inflammatory dysfunctions observed in MDs are not fully understood yet. In this context, microglial dysfunction has recently emerged as a possible pivotal player, as during the neuroinflammatory response, microglia can be over-activated, and excessive production of pro-inflammatory cytokines, which can modify the kynurenine and glutamate signaling, is reported. Moreover, microglial activation also results in increased astrocyte activity and consequent glutamate release, which are both toxic to the Central Nervous System (CNS). Also, as a result of increased microglial activation in MDs, products of the kynurenine pathway were shown to be changed, influencing then the dopaminergic, serotonergic, and glutamatergic signaling pathways. Therefore, in the present review, we aim to discuss how neuroinflammation impacts on glutamate and kynurenine signaling pathways, and how they can consequently influence the monoaminergic signaling. The consequent association with MDs main symptoms is also discussed. As such, this work aims to contribute to the field by providing insights into these alternative pathways and by shedding light on potential targets that could improve the strategies for pharmacological intervention and/or treatment protocols to combat the main pharmacologically unmatched symptoms of MDs, as the SCZ.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".