Effects of Triflusal and Aspirin in a Rat Model of Cerebral Ischemia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neuroinflammation plays a critical role in the pathogenesis of cerebral ischemia. Triflusal, a selective cyclooxygenase-2, and its active metabolite 3-hydroxy-4-trifluoromethylbenzoic acid may inhibit apoptosis and inflammation after cerebral ischemia. An in vivo model of cerebral ischemia was used to investigate the effects of triflusal and aspirin treatment on infarct volume, and inflammation after cerebral ischemia in the rat. METHODS: Male Wistar rats were subjected to a permanent right-sided middle cerebral artery occlusion. Rats received oral administration of either triflusal or aspirin. After 3 days after surgery, immunostaining was used to detect neuroinflammatory cells and molecules, and infarct volumes were measured. RESULTS: Both triflusal and aspirin at a dose of 30 mg/kg but not 10 mg/kg significantly reduced infarct volume compared with vehicle treatment. Middle cerebral artery occlusion resulted in increased astrocyte and heat shock protein-27 (Hsp27) immunostaining in the ipsilateral cortex. Triflusal (30 mg/kg) or aspirin treatment (30 mg/kg) did not reduce the levels of GFAP or Hsp27 immunostaining. Triflusal (30 mg/kg) also significantly decreased the protein levels of IL-Ibeta but not nuclear factor kappa B or tumor necrosis factor-alpha in the cortex ipsilateral to the middle cerebral artery occlusion. CONCLUSIONS: The results suggest that triflusal and aspirin appear to be equally neuroprotective against middle cerebral artery occlusion-induced cerebral ischemia. Therefore, strong rationale exists to continue the neuroprotective examination of triflusal in brain injury.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".