<i>In Vitro</i> and <i>In Vivo</i> Anti‐Inflammatory Activities of Benjakul: A Potential Medicinal Product from Thai Traditional Medicine
Bibliographic record
Abstract
Benjakul (BJK) is a Thai traditional remedy consisting of five plants: Piper chaba Hunt., Piper sarmentosum Roxb., Piper interruptum Opiz., Plumbago indica Linn., and Zingiber officinale Roscoe. It is used as a first‐line drug to balance patient’s symptoms before other treatments. BJK ethanolic extract has been reported to show anti‐inflammatory activity through various mediators, e.g., nitric oxide, TNF‐ α , IL‐1 β , and IL‐6. Therefore, BJK could serve as a potential novel anti‐inflammatory herbal medicine. However, studies on prostaglandin E2 (PGE2), one of the key mediators in acute inflammation, and anti‐inflammation in animal models ( in vivo ) have not been done. This study investigated the anti‐inflammatory activity of BJK extract and some of its chemical compounds against PGE2 production in murine macrophage (RAW 264.7) cell line and two in vivo models of anti‐inflammatory studies. Ethanolic extract of BJK (BJK[E]) showed high inhibitory activity against PGE2 production with an IC 50 value of 5.82 ± 0.10 μ g/mL but its water extract (BJK[W]) was inactive. Two chemicals from BJK[E], i.e., plumbagin and myristicin, which served as biological markers, showed strong activity with IC 50 values of 0.08 ± 0.01 and 1.80 ± 0.06 μ g/mL, respectively. BJK[E] was administered both topically and orally to rats inhibited with inflammation induced by ethyl phenylpropiolate (rat ear edema model) and carrageenan (hind paw edema model). Moreover, the biological activity of BJK extract did not reduce after six‐month storage under accelerated condition (40°C, 75% RH). This indicated its stability and a 24‐month shelf‐life under normal condition. These results supported not only the use of BJK in Thai traditional medicine but also the possibility of further development of phytopharmaceutical products from BJK.
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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) | 0.003 | 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".