Development and evaluation of Sahasthara Thai medicine remedy in a film-forming spray for topical anti-inflammatory therapy
Bibliographic record
Abstract
Background and purpose: The study aimed to develop a localized topical anti-inflammatory treatment using a Thai medicinal herbal remedy called "Sahasthara," known for its anti-inflammatory properties, to create a film-forming spray (FFS). Experimental approach: This research evaluated and developed an FFS formulated with Sahasthara ethanolic extract (SHTe). Subsequently, the optimized formulation was investigated for in vitro anti-inflammatory activity, cell culture toxicity assessment, pharmacological effects, and stability studies. Findings/Results: An optimized formulation (F12) was identified, consisting of 1% w/w SHTe and PVP K90, glycerol, PEG 400, sesame oil, a eutectic blend, and ethanol. This clear, smooth surface, yellowish film releases 42.37%, 38.67%, and 68.93% at 8 h, corresponding to a flux of 20.94, 1.92, and 26.32 µg/cm2/h of piperine, plumbagin, and β-asarone, respectively. F12 was determined to have a viscosity, drying time, and spray angle of 20 cps, 4.57 min, and 66.0 degrees. In-vitro anti-inflammatory activity demonstrated nitric oxide (NO) inhibition with an IC50 of 9.18 µg/mL. No apparent toxicity was observed in a skin cell line. This formulation was developed to be physically stable after undergoing freeze-thaw cycles. Although thermodynamic stability studies under accelerated conditions revealed a minor decrease in piperine and β-asarone within the film, the results indicate no statistically significant changes in its anti-inflammatory activity. Conclusion and implications: SHTe FFS offers optimal spray ability, a high in-vitro drug release profile, potent inhibition of anti-inflammatory markers, and stability under accelerated conditions. These findings suggest that SHTe FFS can serve as an innovative topical anti-inflammatory treatment.
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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".