Complex Roles of Microglial Cells in Ischemic Stroke Pathobiology: New Insights and Future Directions
Bibliographic record
Abstract
Ischemic stroke constitutes the major cause of death and disability in the industrialized world. The interest in microglia arose from the evidence outlining the role of neuroinflammation in ischemic stroke pathobiology. Microglia constitute the powerhouse of innate immunity in the brain. Microglial cells are highly ramified, and use these ramifications as sentinels to detect changes in brain homeostasis. Once a danger signal is recognized, cells become activated and mount specialized responses that range from eliminating cell debris to secreting inflammatory signals and trophic factors. Originally, it was suggested that microglia play essentially a detrimental role in ischemic stroke. However, recent reports are providing evidence that the role of these cells is more complex than what was originally thought. Although these cells play detrimental role in the acute phase, they are required for tissue regeneration in the post-acute phases. This complex role of microglia in ischemic stroke pathobiology constitutes a major challenge for the development of efficient immunomodulatory therapies. This review aims at providing an overview regarding the role of resident microglia and peripherally recruited macrophages in ischemic pathobiology. Furthermore, the review will highlight future directions towards the development of novel fine-tuning immunomodulatory therapeutic interventions.
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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 | 0.002 | 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".