Quality Control and Evaluation of Anti-inflammatory and Antioxidant Activities of Ethanolic Extracts from Ha-Rak remedy, Piper betle Linn., Garcinia mangostana Linn., and Their Combined Remedies
Bibliographic record
Abstract
Introduction: A Thai herbal remedy, Ha-Rak (HR), Piper betle Linn. (PB) leaves, and Garcinia mangostana Linn. (GM) pericarps tend to relieve inflammatory related atopic dermatitis (AD). Additionally, antioxidants have important role in the AD prevention. Objectives: To develop combined formulas of HR, PB and GM extracts and investigate them on anti-inflammatory and antioxidant activities, the total flavonoid content (TFC) and the total phenolic content (TPC). Methods: The quality of each dried plant material was evaluated according to Thai Herbal Pharmacopoeia (THP) criteria. Each was extracted by maceration with 95% ethanol, and then combined with a remedy. The inflammatory properties of each extract were assessed using the production inhibition of TNF- α from RAW 264.7 cells. The antioxidant properties were assessed using DPPH and ABTS radical scavenging. The TFC and TPC were also analyzed. Results: The quality of all plant materials passed Thai herbal pharmacopeia standards. HR showed the highest anti inflammatory activity. The combination HMB-321, which is composed of HR:GM:PB in a ratio of 3:2:1, showed the highest anti-inflammatory activity among other combined formulas. Moreover, HMB-321 showed moderate antioxidant activities and capacities. PB showed the highest antioxidant activity but less anti-inflammatory activity. HMB-123, whose proportion of HR:GM:PB was 1:2:3, showed the highest antioxidant activity among the combination of extracts but showed less anti-inflammatory activity. Conclusions: The combination HMB-321 could be a candidate remedy for the prevention and treatment of AD. Further study should be directed to in vivo testing and clinical trials for anti-inflammatory and antioxidant activities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".