Nanoparticle drug delivery characterization for fluticasone propionate and in vitro testing
Bibliographic record
Abstract
Glucocorticoids, such as fluticasone propionate (FP), are used for the treatment of inflammation and alleviation of nasal symptoms and allergies, and as an antipruritic. However, both short- and long-term therapeutic use of glucocorticoids can lead to muscle weakness and atrophy. In the present study, we evaluated the feasibility of the nanodelivery of FP with poly(dl-lactide-co-glycolide) (PLGA) and tested in vitro function. FP-loaded PLGA nanoparticles were prepared via nanoprecipitation and morphological characteristics were studied via scanning electron microscopy. FP-loaded nanoparticles demonstrated an encapsulation efficiency of 68.6% ± 0.5% with a drug loading capacity of 4.6% ± 0.04%, were 128.8 ± 0.6 nm in diameter with a polydispersity index of 0.07 ± 0.008, and displayed a zeta potential of -19.4 ± 0.7. A sustained in vitro drug release pattern was observed for up to 7 days. The use of fluticasone nanoparticle decreased lipopolysaccharide (LPS)-induced lactate dehydrogenase release compared with LPS alone in C2C12 treated cells. FP also decreased expression of LPS-induced inflammatory genes in C2C12 treated cells as compared with LPS alone. Taken together, the present study demonstrates in vitro feasibility of PLGA-FP nanoparticle delivery to the skeletal muscle cells, which may be beneficial for treating inflammation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".