Bibliographic record
Abstract
PURPOSE: The objective of this study is to review the role of matrix metalloproteinases in intracerebral hemorrhage, which is associated with hypertension, head trauma and premature birth. MATERIALS AND METHODS: A PubMed search of literature pertaining to this study was conducted in April 2008 using specific keyword search terms pertaining to intracerebral hemorrhage and matrix metalloproteinases. Some papers are not cited here as it is not possible to be all inclusive or due to the space limit from the journal. DISCUSSION: The prognosis following ICH is more detrimental than that of ischemic strokes. Matrix metalloproteases have been implicated in the pathogenesis of brain damage following ICH. The goal of this review is to bring together recent diverse data concerning the roles of matrix metalloproteinases after intracerebral hemorrhage, which includes the role of matrix metalloproteinases in central nervous system, matrix metalloproteinases in animal models and humans of intracerebral hemorrhage, the relationship between matrix metalloproteinases and neuroinflammation, neuronal death, blood-brain barrier disruption and interaction with other molecules, as well as treatment of intracerebral hemorrhage with anti-matrix metalloproteinases agents. Besides deleterious roles in the acute period of intracerebral hemorrhage, some matrix metalloproteinases function in the later stages following intracerebral hemorrhage may have beneficial remodeling activity. CONCLUSION: At present, the experimental data support the use of pharmacologic anti-matrix metalloproteinases strategies in the acute periods following intracerebral hemorrhage to alleviate 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".