Unbalanced Metalloproteinase-9 and Tissue Inhibitors of Metalloproteinases Ratios Predict Hemorrhagic Transformation of Lesion in Ischemic Stroke Patients Treated with Thrombolysis: Results from the MAGIC Study
Bibliographic record
Abstract
BACKGROUND: Experimentally, metalloproteinases (MMPs) play a detrimental role related to the severity of ischemic brain lesions. Both MMPs activity and function in tissues reflect the balance between MMPs and tissue inhibitors of metalloproteinases (TIMPs). We aimed to evaluate the role of MMPs/TIMPs balance in the setting of rtPA-treated stroke patients. METHODS: Blood was taken before and 24-h after rtPA from 327 patients (mean age 68 years, median NIHSS 11) with acute ischemic stroke. Delta median values of each MMP/TIMP ratio [(post rtPA MMP/TIMP-baseline MMP/TIMP)/(baseline MMP/TIMP)] were analyzed related to symptomatic intracranial hemorrhage (sICH) according to NINDS criteria, relevant hemorrhagic transformation (HT) defined as confluent petechiae within the infarcted area or any parenchymal hemorrhage, stroke subtypes (according to Oxfordshire Community Stroke Project) and 3-month death. The net effect of each MMP/TIMP ratio was estimated by a logistic regression model including major clinical determinants of outcomes. RESULTS: Adjusting for major clinical determinants, only increase in MMP9/TIMP1 and MMP9/TIMP2 ratios remained significantly associated with sICH (odds ratio [95% confidence interval], 1.67 [1.17-2.38], p = 0.005; 1.74 [1.21-2.49], p = 0.003, respectively). Only relative increase in MMP9/TIMP1 ratio proved significantly associated with relevant HT (odds ratio [95% confidence interval], 1.74 [1.17-2.57], p = 0.006) with a trend toward significance for MMP9/TIMP2 ratio (p = 0.007). DISCUSSION: Our data add substantial clinical evidence about the role of MMPs/TIMPs balance in rtPA-treated stroke patients. These results may serve to generate hypotheses on MMPs inhibitors to be administered together with rtPA in order to counteract its deleterious effect.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".