In vitro and in vivo evaluation of anti-inflammatory activities of ethanol extract from Lom-Am-Ma-Pruek remedy for pain relief
Bibliographic record
Abstract
Background and purpose: anti-inflammatory activity of LAMP ethanol extract. Experimental approach: The anti-inflammatory activity of LAMP and its plant ingredients were investigated on lipopolysaccharide-stimulated NO, PGE2, and TNF-α release from RAW264.7 cells. Furthermore, the stability of LAMP under biological and chemical accelerated conditions was evaluated using the Griess reaction assay and HPLC. Lastly, rat models with ethyl phenylpropionate (EPP)-induced ear edema and carrageenan-induced paw edema were utilized to assess anti-inflammatory activity. Findings/Results: LAMP possessed potent inhibitory effects on NO, PGE2, and TNF-α production with IC50 values of 24.90 ± 0.86, 4.77 ± 0.03, and 35.01 ± 2.61 µg/mL, respectively. In addition, LAMP extract demonstrated stable biological activity, anti-inflammatory effects, and phytochemical content stability under stress conditions. Additionally, 0.5%, 1%, and 2% w/v LAMP significantly inhibited EPP-induced rat ear edema over time equivalent to 5% w/v phenylbutazone. LAMP at 180, 375, and 750 mg/kg also considerably reduced carrageenan-induced rat paw edema 2 h after carrageenan administration compared to phenylbutazone at 250 mg/kg. Conclusion and implications: LAMP has anti-inflammatory activity by inhibiting PGE2 formation. These findings are consistent with the efficacy and traditional use of the LAMP remedy in treating inflammatory diseases.
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 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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".