Delayed Administration of Interleukin-1 Receptor Antagonist Reduces Ischemic Brain Damage and Inflammation in Comorbid Rats
Bibliographic record
Abstract
Many neuroprotective agents have been effective in experimental stroke, yet few have translated into clinical application. One reason for this may be failure to consider clinical comorbidities/risk factors in experimental models. We have shown that a naturally occurring interleukin-1 receptor antagonist (IL-1Ra) is protective against ischemic brain damage in healthy animals. However, protective effects of IL-1Ra have not been determined in comorbid animals. Thus, we tested whether IL-1Ra protects against brain injury induced by experimental ischemia in aged JCR-LA (corpulent) rats, which have clinically relevant risk factors. Male, aged, lean, and corpulent rats exposed to transient (90 minutes) occlusion of the middle cerebral artery (tMCAO) were administered two doses of IL-1Ra (25 mg/kg, subcutaneously) during reperfusion. Brain injury and neuroinflammatory changes were assessed 24 hours after tMCAO. Our results show that IL-1Ra administered at reperfusion significantly reduced infarct volume measured by magnetic resonance imaging (50%, primary outcome) and blood-brain barrier disruption in these comorbid animals. Interleukin-1Ra also reduced microglial activation, neutrophil infiltration, and cytokines levels in the brain. These data are the first to indicate that IL-1Ra protects against ischemic brain injury when administered via a clinically relevant route and time window in animals with multiple risk factors for stroke.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".