Dual immune modulation of microglia in viral encephalitis: current understanding and future perspectives
Bibliographic record
Abstract
Viral encephalitis, characterized by inflammation of the brain parenchyma, poses a significant threat to public health due to its high rates of morbidity and mortality. Microglia, the central nervous system's resident immune cells, are crucial in the pathophysiology and development of this condition. These microglia exhibit a dual function, being involved in both neuroprotection and neurotoxicity during viral encephalitis. To address this complex interplay, targeted therapeutic strategies that modulate microglia activation state have emerged as a promising approach. These strategies aim to either inhibit excessive microglia activation or promote their neuroprotective functions. By targeting microglia, these therapies hold the potential to improve outcomes for patients with viral encephalitis. This review synthesizes current evidence revealing that microglial responses during viral encephalitis exhibit context-dependent heterogeneity that extends beyond traditional M1/M2 paradigms. Critically, our review reveals a significant translational gap, with no current clinical trials investigating microglial-targeted therapies for viral encephalitis despite promising preclinical evidence. This review provides a comprehensive framework for understanding microglial complexity in viral encephalitis and establishes research priorities for advancing these insights toward clinical application.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".