A Novel Development of a Curcuma aromatica Salisb Extract-Loaded Hydrogel Patch for Acne and Skin Inflammation: Physicochemical Properties and In Vitro Anti-Inflammatory and Anti-Acne Activities
Bibliographic record
Abstract
Background/Objectives: Inflammatory skin disorders such as dermatitis, psoriasis, and acne that affect patients’ quality of life and health require safe and effective active delivery systems. In Thai traditional medicine, Curcuma aromatica has long been used to treat skin disorders and for cosmetic skin care. However, the research is scarce. This study aimed to develop and evaluate a novel anti-inflammatory and anti-acne hydrogel patch containing C. aromatica extract. Methods: The hydrogel patch formulations were prepared, and their mechanical properties, in vitro release, skin permeation, in vitro anti-inflammatory and anti-acne activities, and physicochemical properties were studied. Results: The C. aromatica hydrogel patch (CA2 formulation) made from carrageenan, locust bean gum, PVP-K30, and C. aromatica extract displayed excellent physical appearance and mechanical properties; it was smooth, durable, and flexible. Curcumin, an active ingredient, was released from the C. aromatica hydrogel patch within the first 30 min (19.07 ± 1.14%), reaching its peak at 12 h (50.40 ± 3.94%), with sustained permeation of 38.18 ± 0.45% at 24 h. Data from the drug release and permeation study better fit Higuchi’s kinetic model. Additionally, the CA2 hydrogel patch demonstrated anti-inflammatory activity, with an IC50 value of 19.85 ± 0.82 μg/mL, and was also effective against Cutibacterium acne, with an inhibition zone of 12.70 ± 2.10 mm. Conclusions: The developed C. aromatica hydrogel patch not only showed great physicochemical properties but also had anti-inflammatory and anti-acne activities; it prolonged curcumin release, enabling delivery of the drug to treat skin inflammation disorders. The CA hydrogel patch is suitable for use as an anti-acne facial mask and for inflamed skin areas; however, it should be further evaluated in clinical trials.
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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".