Relative Importance of Proteinase-Activated Receptor-1 Versus Matrix Metalloproteinases in Intracerebral Hemorrhage-Mediated Neurotoxicity in Mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To reduce bleeding and damage to central nervous system tissue in intracerebral hemorrhage, the coagulant effect of thrombin is essential. However, thrombin itself can kill neurons in intracerebral hemorrhage as can the matrix metalloproteinases (MMPs), which are also elevated in this condition, in part due to thrombin-mediated activation of MMPs. It is thus important to understand and block the neurotoxic effects of thrombin without inhibiting its therapeutic outcomes. In this study, we have investigated the relative roles of proteinase activated receptor-1, a thrombin receptor, and MMPs in brain injury induced by thrombin or blood. METHODS: Mice were subjected to stereotactic intracerebral injections of saline, thrombin, and autologous blood, with or without hirudin, a thrombin inhibitor, or GM6001, an MMP inhibitor. Twenty-four hours later, tissue sections were obtained to evaluate the area of brain damage and extent of dying neurons. Data from wild-type mice were compared with results obtained with proteinase activated receptor-1 null mice. RESULTS: In blood-induced damage to the brain parenchyma, both hirudin and GM6001 significantly reduced injury to a comparable extent (>40%) implicating both thrombin and MMPs in neurotoxicity. In proteinase activated receptor-1 null mice, blood-induced brain damage was reduced by 22.6% relative to wild-type animals; by comparison, the blood-induced brain damage was reduced by 48.3% using GM6001. CONCLUSIONS: The neurotoxicity of blood in intracerebral hemorrhage involves both proteinase activated receptor-1 and MMP activation, with the latter appearing more prominent in causing death.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".