Microglia and macrophage phenotypes in intracerebral haemorrhage injury: therapeutic opportunities
Bibliographic record
Abstract
The prognosis of intracerebral haemorrhage continues to be devastating despite much research into this condition. A prominent feature of intracerebral haemorrhage is neuroinflammation, particularly the excessive representation of pro-inflammatory CNS-intrinsic microglia and monocyte-derived macrophages that infiltrate from the circulation. The pro-inflammatory microglia/macrophages produce injury-enhancing factors, including inflammatory cytokines, matrix metalloproteinases and reactive oxygen species. Conversely, the regulatory microglia/macrophages with potential reparative and anti-inflammatory roles are outcompeted in the early stages after intracerebral haemorrhage, and their beneficial roles appear to be overwhelmed by pro-inflammatory microglia/macrophages. In this review, we describe the activation of microglia/macrophages following intracerebral haemorrhage in animal models and clinical subjects, and consider their multiple mechanisms of cellular injury after haemorrhage. We review strategies and medications aimed at suppressing the pro-inflammatory activities of microglia/macrophages, and those directed at elevating the regulatory properties of these myeloid cells after intracerebral haemorrhage. We consider the translational potential of these medications from preclinical models to clinical use after intracerebral haemorrhage injury, and suggest that several approaches still lack the experimental support necessary for use in humans. Nonetheless, the preclinical data support the use of deactivator or inhibitor of pro-inflammatory microglia/macrophages, whilst enhancing the regulatory phenotype, as part of the therapeutic approach to improve the prognosis of intracerebral haemorrhage.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".