Contributions of multiple proteases to neurotoxicity in a mouse model of intracerebral haemorrhage
Bibliographic record
Abstract
Proteases such as matrix metalloproteinases (MMPs) and thrombin are implicated in intracerebral haemorrhage (ICH) but their interactions amongst one another and interdependency remain to be defined. The latter is important since proteases acting through different mechanisms to inflict neurotoxicity would require separate targeting compared with proteases acting through the same cascade. We reported recently that MMP-9 and thrombin combined to promote neurotoxicity in ICH; however, as there was still substantial injury when both MMP-9 and thrombin were inhibited, we sought other factors that also contribute to ICH pathology. MMP-3, another member of the MMP family, has been correlated with poor prognosis in ICH in humans and it has been shown to increase rapidly after ICH in animals. Moreover, MMP-3 can convert the MMP-9 zymogen to its active form. Thus, we have examined whether MMP-3 is neurotoxic and addressed whether its potential effect in ICH is dependent on, or additional to, damage inflicted by MMP-9 and thrombin. We report that cultured neurons are killed by MMP-3 and that neuronal death is most marked when all three proteases, MMP-3, MMP-9 and thrombin, are combined. In vivo, the injection of autologous blood into the right striatum to produce ICH injury resulted in MMP-3 expression within 3 h. The blood-induced lesion and neuronal death was significantly reduced in MMP-3 or MMP-9 null mice compared with wild-type counterparts, and MMP-3 and -9 double null mice had even less brain damage. Significantly, pathological destruction after ICH was least in MMP-3 and -9 double null mice treated with a thrombin antagonist, hirudin. These results provide insights into molecules that inflict neurotoxicity in ICH and demonstrate that multiple proteases would need to be targeted simultaneously to successfully reduce ICH neurotoxicity.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".