Interleukin-1 receptor antagonist is beneficial after subarachnoid haemorrhage in rat by blocking haem-driven inflammatory pathology
Bibliographic record
Abstract
Subarachnoid haemorrhage (SAH) is a major contributor to the burden of stroke on society. Treatment options are limited and animal models of SAH do not always mimic key pathophysiological hallmarks of the disease, thus hindering development of new therapeutics. Inflammation is strongly associated with brain injury after SAH in animals and patients, and inhibition of the pro-inflammatory cytokine interleukin-1 (IL-1) represents a possible therapeutic target. Here we report that a rupture of the middle cerebral artery in the rat produces heterogeneous infarct patterns similar to those observed in human SAH. Administration of the IL-1 receptor antagonist (IL-1Ra) reduced blood-brain barrier breakdown, and the extent of breakdown correlated with brain injury. After SAH, haem oxygenase-1 (HO-1) was strongly expressed around the bleed site and in the cortex and striatum, indicating the presence of free haem, a breakdown product of haemoglobin. HO-1 expression was also found in the same regions as microglial/macrophage expression of IL-1α. The direct effect of haem on IL-1α expression was confirmed in vitro using organotypic slice culture (OSC). Haem-induced cell death was dependent on IL-1 signalling, with IL-1Ra completely blocking cellular injury. Furthermore, stimulation of mouse primary mixed glial cells with haem induced the release of IL-1α, but not IL-1β. Thus, we suggest that haem, released from lysed red blood cells (RBCs) in the subarachnoid space, acts as a danger-associated molecular pattern (DAMP) driving IL-1-dependent inflammation. These data provide new insights into inflammation after SAH-induced brain injury and suggest IL-1Ra as a candidate therapeutic for the disease.
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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 | 0.000 | 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) | 0.003 | 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".