Bibliographic record
Abstract
Inflammation is a defense reaction against diverse insults that serves to remove noxious agents and to limit their detrimental effects. There is increasing evidence that post-ischemic inflammation plays an important role in brain ischemia. However, whether inflammatory processes are deleterious or beneficial to recovery is presently a matter of debate and controversy. Experimentally and clinically, stroke is followed by an acute and a prolonged inflammatory response characterized by the production of inflammatory cytokines, leukocyte and monocyte infiltration in the brain, and the activation of resident glial cells. These events may contribute to ischemic brain injury. Several groups report conflicting results regarding the role of inflammation and effects of anti-inflammatory treatments in cerebral ischemia. Experimental studies employing knockout mice for different cytokines and chemokines provide only partial answers. This highlights the importance of clarifying the role of the immune response in pathological changes at the site of ischemic lesions in the brain. Here, we describe dual effects of the brain's inflammatory response and new evidence for a neuroprotective role of proliferating microglial cells in ischemia. In addition, we discuss a potential role of post-ischemic inflammation in brain regeneration and modulation of synaptic plasticity.
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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) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".