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Intravenous Administration of Scutellarin Nanoparticles Augments the Protective Effect against Cerebral Ischemia–Reperfusion Injury in Rats

2022· article· en· W4224218548 on OpenAlexfundno aff
Chang Yang, Qing Zhao, Shanshan Yang, Libin Wang, Xing-Yuan Xu, Lisu Li, Wafa’ T. Al-Jamal

Bibliographic record

VenueMolecular Pharmaceutics · 2022
Typearticle
Languageen
FieldMedicine
TopicFlavonoids in Medical Research
Canadian institutionsnot available
FundersNatural Science Foundation of Guizhou ProvinceChina Scholarship CouncilQueen's University BelfastQueen's UniversityNational Natural Science Foundation of ChinaHealth Commission of Guizhou Province
KeywordsScutellarinIschemiaPLGAPharmacologyMedicineTUNEL assayIn vivoReperfusion injuryH&E stainAnesthesiaChemistryPathologyInternal medicineIn vitroStainingImmunohistochemistryBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.360
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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