Combination of Thrombin and Matrix Metalloproteinase-9 Exacerbates Neurotoxicity in Cell Culture and Intracerebral Hemorrhage in Mice
Bibliographic record
Abstract
The rapid loss of neurons is a major pathological outcome of intracerebral hemorrhage (ICH). Several mechanisms may produce the neurotoxicity observed in ICH, and these include proteolytic enzymes such as thrombin and matrix metalloproteinase-9 (MMP-9). We tested the hypothesis that thrombin and MMP-9 combine to injure neurons in culture and that they interact to promote the acute neurotoxicity that occurs in ICH in vivo. We report that human fetal neurons die when exposed to thrombin or MMP-9 in isolation and that a combination of these two enzymes increased neurotoxicity. The toxicity of thrombin involved protease-activated receptor-1 and the conversion of proMMP-9 to active MMP-9. In ICH, which was induced in mice by the intracerebral injection of autologous blood, significant areas of brain damage, neuronal death, microglia/macrophage activation, and neutrophil accumulation occurred by 24 h of injury. Importantly, these neuropathological features were reduced in MMP-9 null mice compared with wild-type controls, and the concordant antagonism of thrombin using hirudin also alleviated the injury found in MMP-9 null mice. Our collective results demonstrate that thrombin and MMP-9 collaborate to promote neuronal death in culture and in ICH. To improve the prognosis of ICH, the neurotoxic actions of thrombin and MMP-9 must be inhibited early and simultaneously after injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".