Polyphenols and Rosmarinic acid Contents, Antioxidant and Anti- Inflammatory Activities of Different Solvent Fractions from Nga- Mon (Perilla frutescens) Leaf
Bibliographic record
Abstract
Perilla is a rich source of polyphenols, which exhibits antioxidant, anti-inflammatory activities, and a variety of biological effects. The effect of differential solvents on the polyphenols, flavonoids, rosmarinic acid (RA), antiinflammatory and antioxidant activities of perilla leaf require investigation. In this study, perilla leaf was extracted with 70% ethanol and sequentially fractionated according to the solvent’s polarity with hexane, dichloromethane, ethyl acetate, and water. Samples were subjected to the bioactive compound measurements. The antioxidant and antiinflammation nature of perilla was analyzed based on the scavenging effects on DPPH•, ABTS•+, O2•- and nitric oxide (NO), as well as FRAP assay, and determination of the inhibition effects on NO, inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) production in the cell-based study. The results indicate that among all different solvents used for sequential fractionation, ethyl acetate (EtOAc) was most effective in the separation of anti-oxidative and antiinflammatory compounds in the perilla leaf extract. These properties can partly be due to the presence of polyphenols, flavonoids, and also RA. It can be demonstrated here that, the perilla leaf EtOAc fraction could be used as a natural active pharmaceutical ingredient for dietary supplements and nutraceuticals.
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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 | 0.001 | 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) | 0.001 | 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".