Intravenous Administration of Scutellarin Nanoparticles Augments the Protective Effect against Cerebral Ischemia–Reperfusion Injury in Rats
Bibliographic record
Abstract
This study investigates the protective effect of poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) loaded with scutellarin (SCU), a flavone isolated from the traditional Chinese medicineErigeron breviscapus (Vant.) Hand.-Mazz., in reducing cerebral ischemia/reperfusion (I/R) injury in vivo. The focal cerebral I/R injury model was established by occluding the middle cerebral artery for 1 h in male Sprague-Dawley (SD) rats. Our SCU-PLGA NPs exhibited an extended in vitro release profile and prolonged blood circulation in rats with cerebral ischemia. More importantly, when administered intravenously once a day for 3 days, SCU-PLGA NPs increased the SCU level in the ischemic brain, compared to free SCU, resulting in a significant reduction of the cerebral infarct volume after cerebral I/R. Furthermore, SCU-PLGA NPs reversed the histopathological changes caused by cerebral I/R injury, as well as attenuated cell apoptosis in the brain tissue, as confirmed by hematoxylin and eosin, and TUNEL staining. Our findings have revealed that our injectable SCU-PLGA NPs provide promising protective effects against cerebral I/R injury, which could be used in combination with the existing conventional thrombolytic therapies to improve stroke management.
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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".