Partial Intra‐Aortic Occlusion Improves Perfusion Deficits and Infarct Size Following Focal Cerebral Ischemia
Bibliographic record
Abstract
Reperfusion with intravenous tissue plasminogen activator (tPA) has been the goal of therapy for acute ischemic stroke; however, tPA is contraindicated in many patients, has low recanalization rates in major occlusions, and carries a substantial risk of symptomatic intracerebral hemorrhage. In the present study, we hypothesized that partial intra-aortic occlusion of the abdominal aorta would increase salvage of ischemic penumbra and reduce infarct volume after focal embolic stroke in rats. We examined the effects of aortic occlusion on infarct volume, expression and activation of matrix metalloprotease-9, and hemorrhagic transformation with or without treatment with tPA. We then examined the effects of aortic occlusion on perfusion deficits following embolic occlusion. Results showed that partial aortic occlusion significantly reduces brain infarction volume with or without treatment with tPA after focal ischemia, but does not increase risk for hemorrhagic transformation or matrix metalloprotease-9 expression and activation. Partial intra-aortic occlusion also reduces perfusion deficits after focal cerebral ischemia as compared to control. The present study shows that partial intra-aortic occlusion significantly decreases infarction volume and perfusion deficits following ischemic injury in an embolic model of cerebral ischemia. Moreover, combination treatment with tPA and partial intra-aortic occlusion further reduces infarction volume without any increase in hemorrhagic transformation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".