Rosiglitazone Alone Or in Combination with Tissue Plasminogen Activator Improves Ischemic Brain Injury in an Embolic Model in Rats
Bibliographic record
Abstract
In this study, we examined whether rosiglitazone, a peroxisome proliferator-activated receptor gamma (PPARgamma) agonist, is neuroprotective in focal ischemic brain injury, and whether rosiglitazone can enhance the protective action of tissue plasminogen activator (tPA), an agent used clinically for thrombolytic therapy. Rats were subjected to ischemic brain injury by embolizing preformed clots into the middle cerebral artery (MCA). Treatment with rosiglitazone reduced infarction and improved functional recovery; it also enhanced the neuroprotective action of tPA and lengthened the time window for initiating tPA treatment. Occlusion of MCA resulted in a loss of collagen type IV, a major structural protein of the microvascular basal lamina, and tPA treatment worsened this loss. Rosiglitazone treatment prevented the reduction of collagen type IV in the ischemic injured brain by inhibiting the activation of matrix metallopeptidase-9 (MMP-9). In addition, rosiglitazone treatment reduced inflammatory reactions in the ischemic injured brain. Rosiglitazone either alone or in combination with tPA is an effective agent in the reduction of ischemic brain injury. The reduction of microvascular damage and inflammation contributes to the beneficial actions of rosiglitazone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".