In Vitro Anti-Inflammatory Properties of Selected Green Leafy Vegetables
Bibliographic record
Abstract
The study investigated the anti-inflammatory activity of the hydro methanolic extract of six leafy vegetables, namely Cassia auriculata, Passiflora edulis, Sesbania grandiflora, Olax zeylanica, Gymnema lactiferum, and Centella asiatica. The anti-inflammatory activity of methanolic extracts of leafy vegetables was evaluated using four in vitro-based assays: hemolysis inhibition, proteinase inhibition, protein denaturation inhibition, and lipoxygenase inhibition. Results showed that the percent inhibition of hemolysis from these leaf extracts (25–100 µg/mL dry weight basis (DW)) was within the range from 5.4% to 14.9%, and the leaves of P. edulis and O. zeylanica showed a significantly higher (p < 0.05) inhibition levels. Percent inhibition of protein denaturation of these leafy types was within the range of 36.0–61.0%, and the leaf extract of C. auriculata has exhibited a significantly higher (p < 0.05) inhibition level. Proteinase inhibitory activity of these leaf extracts was within the range of 20.2–25.9%. The lipoxygenase inhibition was within the range of 3.7–36.0%, and the leaf extract of G. lactiferum showed an improved ability to inhibit lipoxygenase activity. In conclusion, results revealed that all the studied leaves possess anti-inflammatory properties at different levels, and this could be due to the differences in the composition and concentration of bioactive compounds.
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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) | 0.001 | 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".