P.064 Enhancing the neuroprotective properties of edaravone using glutathione nanogel as a promising carrier for brain drug delivery in transient global ischemia in a rodent model
Bibliographic record
Abstract
Background: Edaravone (EDV) is an antioxidant that scavengs ROS, which is known to associate with pathophysiology of ischemic stroke. Low stability and bioavailability are major EDV drawbacks. Decorating nanogel surface with glutathione to target brain tissue was performed to optimize drug delivery. Methods: Nano vehicle characterization was assessed with FT-IR and HNMR. Images from the surface of nano vehicle was captured by AFM and TEM instruments. After development of mPEG-b-PLGA EDV nano particles, their effect on biochemical factors including malondialdehyde and protein carbonyl level was measured on Wistar rats under global ischemia. The level of GSH and FRAP were also measured. Results: The Size (199 nm, hydrodynamic diameter) and zeta potential (-25 mV) of optimum formulation was assessed and the calibration curve in deionized water was created at 244 nm. In-vitro drug release profile depicted a sustained release process. EDV and glutathione presence in one vehicle simultaneously, resulted in elevated spatial memory and learning along with cognitive function. In addition, significantly lower MDA and PCO, and higher level of neural GSH and FRAP were observed. Conclusions: The developed mPEG-b-PLGA EDV nanogel can be a suited vehicle for brain drug delivery of EDV, while managing to minimize the biochemical and pathophysiological alterations in ischemic-like disorder.
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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".