Abstract 98: Post-ischemic Intra-arterial Infusion of Liposome-encapsulated Hemoglobin Can Reduce Ischemic Reperfusion Injury
Bibliographic record
Abstract
Background: We sometimes encounter severe brain edema and hemorrhagic transformation due to ischemic reperfusion (I/R) injury after thrombolysis and/or thromboectomy. The mechanism of I/R injury is thought to depend on blood-brain barrier disruption mediated by matrix metalloproteinase-9 (MMP-9) mainly produced by circulating neutrophils. We examined whether post-ischemic intra-arterial infusion of liposome-encapsulated hemoglobin (LEH), an efficient oxygen carrier without blood cells including neutrophils, can reduce I/R injury through reducing the effect of neutrophil MMP-9 in the rat transient middle cerebral artery occlusion (MCAO) model. Methods: Male Sprague-Dawley rats were subjected to transient MCAO for 2 hours and then were divided into three groups: 1) LEH group infused with LEH (10ml/kg/h) through the recanalized internal carotid artery for 2 hours, 2) vehicle group infused with saline, and 3) control group subjected to recanalization only. After 24-hour reperfusion, all rats were tested for neurological score and then sacrificed to examine infarct and edema volumes, MMP-9 expression, MMP-9 activity and reactive oxygen species (ROS) production. Results: Compared with the control group, the LEH group showed significantly better neurological score (p<0.05), smaller infarct and edema size (p<0.01, p<0.05 respectively). MMP-9 expression, activity and ROS production in the LEH group were lower than those in the control group (p<0.001, p<0.01 and p<0.05, respectively). There was no significant difference between the results in the vehicle group and those in the control group. Conclusion: The results in the present study suggest that post-ischemic intra-arterial infusion of LEH can reduce I/R injury through reducing the effect of neutrophil MMP-9. LEH may be a promising candidate to prevent I/R 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".