<i>In Vitro</i> Wound Healing Activities of Three Most Commonly Used Thai Medicinal Plants and Their Three Markers
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Bibliographic record
Abstract
Skin ensures that a constant internal environment can be maintained in an ever‐changing external environment. When a wound occurs on the skin, the inflammatory and proliferative phases are initiated in response to injury. Thai traditional medicine (TTM), using medicinal plants and ancient knowledge, has been used to treat wounds. Eight Thai medicinal plants, most commonly used to treat wounds, were evaluated for their in vitro biological activities such as antioxidation by NBT assay, anti‐inflammation by production inhibition of NO, promoting fibroblast cell proliferation, and wound closure activities. Plant materials were extracted with 95% ethanol or distilled water and then concentrated and dried. Statistical analysis of data was done using one‐way ANOVA at p value of 0.05. The ethanolic extracts of Garcinia mangostana L., Glycyrrhiza glabra L., and Nigella sativa L. could inhibit the production of superoxide anion with the IC 50 values of 13.97 ± 0.38, 28.62 ± 1.91, and 71.54 ± 3.22 μ g/ml and nitric oxide with the IC 50 values of 23.97 ± 0.91, 46.35 ± 0.43, and 78.48 ± 4.46 μ g/ml, respectively. These extracts could promote cell proliferation and accelerate wound recovery at the rate of 2.02 ± 0.03, 2.12 ± 0.03, and 2.65 ± 0.05% per hour, respectively. Three established markers from these three plants were selected according to the selection criteria. Alpha‐mangostin, glycyrrhizin, and thymoquinone were found to be the active markers for wound closure activities. The ethanolic extracts of G . mangostana , G . glabra , and N . sativa could scavenge superoxide anion and inhibit the production of nitric oxide; therefore these extracts could assist in surpassing the inflammatory phase and protected the cells surrounding the wound area. Most importantly, these extracts also increased the proliferation and accelerated wound closure, indicating that these plant extracts could be promoting wound healing processes and support the use of TTM.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it