Nanodrug delivery platform for glucocorticoid use in skeletal muscle injury
Bibliographic record
Abstract
Glucocorticoids are utilized for their anti-inflammatory properties in the skeletal muscle and arthritis. However, the major drawback of use of glucocorticoids is that it leads to senescence and toxicity. Therefore, based on the idea that decreasing particle size allows for increased surface area and bioavailability of the drug, in the present study, we hypothesized that nanodelivery of dexamethasone will offer increased efficacy and decreased toxicity. The dexamethasone-loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles were prepared using nanoprecipitation method. The morphological characteristics of the nanoparticles were studied under scanning electron microscope. The particle size of nanoparticles was 217.5 ± 19.99 nm with polydispersity index of 0.14 ± 0.07. The nanoparticles encapsulation efficiency was 34.57% ± 1.99% with in vitro drug release profile exhibiting a sustained release pattern over 10 days. We identified improved skeletal muscle myoblast performance with improved closure of the wound along with increased cell viability at 10 nmol/L nano-dexamethasone-PLGA. However, dexamethasone solution (1 μmol/L) was injurious to cells because the migration efficiency was decreased. In addition, the use of dexamethasone nanoparticles decreased lipopolysaccharide-induced lactate dehydrogenase release compared with dexamethasone solution. Taken together, the present study clearly demonstrates that delivery of PLGA-dexamethasone nanoparticles to the skeletal muscle cells is beneficial for treating inflammation and skeletal muscle function.
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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) | 0.001 | 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".