In Vitro Anti-Inflammatory Properties of Selected Green Leafy Vegetables
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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