DNA scission and LDL cholesterol oxidation inhibition and antioxidant activities of Bael ( Aegle marmelos) flower extracts
Bibliographic record
Abstract
Phenolic compounds and other antioxidants have been implicated in protection against non-communicable diseases (NCDs) in which oxidative stress is a main contributor. The extracts of Bael ( Aegle marmelos) flower were examined for their phenolic content, free radical scavenging efficacy and inhibition of low density lipoprotein (LDL) cholesterol oxidation and DNA scission activities. The extracts of Bael flowers were prepared using different solvent systems and their total phenolic content (TPC) and total flavonoid content (TFC) determined. Selected extracts which showed high TPC were subsequently used to determine their efficacy in scavenging hydroxyl, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals, using electron paramagnetic resonance (EPR) spectroscopy. The corresponding peroxyl radical scavenging activity was measured using oxygen radical absorbance capacity (ORAC) assay. The potency of the extracts in inhibiting hydroxyl and peroxyl radical-induced supercoiled DNA scission and inhibition of LDL cholesterol oxidation was also evaluated. The chemical identity of phenolic compounds present in the extracts was tentatively unraveled using HPLC-MS. Phenolic extracts of Bael flowers effectively inhibited hydroxyl, and peroxyl radicals. Phenolic extracts demonstrated notable inhibitory activity against hydroxyl and peroxyl radical-induced DNA scission and LDL oxidation. Vanillic, p -coumaric, chlorogenic, caffeic, and gentisic acids were identified as major phenolic acids, along with flavonoids, mainly catechin, and quercetin. The knowledge gained here may help better use of Bael flower extracts as functional herbal beverage ingredients in the prevention of NCDs.
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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) | 0.002 | 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".