Current and Future Therapeutic Strategies to Target Inflammation in Stroke
Bibliographic record
Abstract
Experimentally and clinically, stroke is followed by both acute and prolonged inflammatory responses characterized by the production of inflammatory cytokines and leukocyte infiltration into the brain. A debate on whether inflammation after stroke is neurotoxic or participates in brain repair remains unresolved. However, the need to pharmacologically control inflammatory amplification has been commonly acknowledged. The principal challenge of devising successful anti-inflammatory strategies for stroke is to understand molecular and temporal interplay of inflammatory and cell-death-inducing processes triggered by cerebral ischemia in both parenchymal and vascular brain cells. This article will review a number of experimental and clinically tested approaches to reduce brain inflammation and damage after stroke (e.g., anti-neutrophil, anti-ICAM-1, anti-cytokine strategies) and will suggest potential pathways where novel therapeutic targets may emerge, including transcriptional regulators of inflammatory gene expression (e.g., NF-kappaB, proteasome) and signaling pathways (e.g., ICE-cascade, MAPK/MKK/ERK cascade) linked to both inflammation and neuronal cell death. Finally, we will discuss applications of functional genomics technologies in the discovery of stroke diagnostics and therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".