In vitro biological activities of a combination of Ha-rak remedy, Piper betle, and Garcinia mangostana for the treatment of atopic dermatitis
Bibliographic record
Abstract
Background and purpose: Ha-rak (HR), an equal-proportion combination of roots from Capparis micracantha DC., Clerodendrum petasites S. Moore., Ficus racemosa L., Harrisonia perforata (Blanco) Merr., and Tiliacora triandra (Colebr.) Diels, Piper betle L. (PB), and Garcinia mangostana L. (GM) are commonly used in traditional Thai medicine to treat skin diseases, including atopic dermatitis (AD). Combining three medicines in adjusted proportions can improve efficacy, reduce toxicity, and reduce medication. This study aimed to evaluate the anti-allergic, anti-inflammatory, antimicrobial, and cytotoxic activities of the ethanolic extracts of different combinations to analyze the relationship among elements, medicinal tastes, and biological activities. Experimental approach: The biological activities (anti-allergic, anti-inflammatory, and anti-microbial activities and cell viability) of ethanolic extracts of plants and their combinations in various proportions were evaluated, as well as the chemical content of the developed remedies using the HPLC technique. Findings/Results: HMB-123 was the most significantly effective combination for AD. HMB-123 reduced β-hexosaminidase release from RBL-2H3 cells to a greater extent than chlorpheniramine. HMB-123 significantly inhibited nitric oxide and TNF-α production in LPS-stimulated RAW 264.7 cells. HMB-123 demonstrated antibacterial activity against all tested bacteria and antifungal activity against Candida albicans . For single extract, PB exhibited the highest anti-fungal activity, while GM exhibited the highest anti-bacterial activity. Conclusion and implications: The combined extracts showed potential as an optimized remedy for AD. HMB-123 demonstrated the highest anti-allergic, anti-inflammatory, and antimicrobial activities, making it a promising development candidate for AD treatment. To confirm safety and efficacy, further pre-clinical and clinical testing is necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".