The Role of Monocytes in Ischemic Stroke Pathobiology: New Avenues to Explore
Bibliographic record
Abstract
Ischemic stroke accounts for the majority of stroke cases and constitutes a major cause of death and disability in the industrialized world. Inflammation has been reported to constitute a major component of ischemic stroke pathobiology. In the acute phase of ischemic stroke, microglia, the resident macrophages of the brain, are activated, followed by several infiltration waves of different circulating immune cells into the brain. Among these circulating immune cells, monocytes have been shown to play a particularly important role. Following their infiltration, monocytes differentiate into potent phagocytic cells, the monocyte-derived macrophages (MDMs), in the ischemic brain. Initially, the presence of these cells was considered as marker of an exacerbated inflammatory response that contributes to brain damage. However, the recent reports are suggesting a more complex and multiphasic roles of these cells in ischemic stroke pathobiology. Monocytes constitute a heterogeneous group of cells, which comprises two major subsets in rodent and three major subsets in human. In both species, two equivalent subsets exist, the pro-inflammatory subset and the anti-inflammatory subset. Recent data have demonstrated that ischemic stroke differentially regulate monocyte subsets, which directly affect ischemic stroke pathobiology and may have direct implications in ischemic stroke therapies. Here, we review the recent findings that addressed the role of different monocyte subsets in ischemic stroke pathobiology, and the implications on therapies.
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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 | 0.002 |
| 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 | 0.002 |
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".