Antioxidative and Antihypertensive Activities of Selected Malaysian ulam (salad), Vegetables and Herbs
Bibliographic record
Abstract
This study was conducted to investigate antioxidative and antihypertensive activities of selected Malaysian ulam (salad), vegetables and herbs. The aqueous extract of selected ulam (salad), vegetables and herbs were analysed for total phenolic content (TPC), antioxidant activities (2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay, 2,2-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid (ABTS) radical cation scavenging assay and ferric reducing antioxidant power assay (FRAP) and antihypertension activity (angiotensin converting enzyme (ACE) inhibitory activity assay). TPC analysis showed that Polygonum minus contains significantly (p<0.05) highest phenolic compound at 48.23 ± 0.17 mg GAE/g as compared to other plants. DPPH analysis showed that P. minus had significantly (p<0.05) highest percentage of radical scavenging activity at 79.09 ± 0.10% as compared to other plants. ABTS analysis showed that Sauropus androgynus had significantly (p<0.05) highest percentage of radical cation scavenging activity at 95.10 ± 0.26% as compared to other plants. FRAP analysis showed that P. minus had significantly (p<0.05) highest ferric reducing power at 63.61 ± 0.73 mmol Fe2+/g as compared to other samples. Murraya koenigii had the highest percentage of ACE inhibitory activity (91.20 ± 4.15%). Correlation analysis showed positive and significant (p<0.01) correlation between TPC and FRAP (r = 0.956), TPC and ABTS (r = 0.635), TPC and DPPH (r = 0.630) and TPC and ACE inhibitors (r = 0.645). This shows that Malaysian tropical plants especially P. minus are potential source of natural antioxidant and antihypertensive agents.
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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 | 0.000 |
| 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".