Profiles of free and bound phenolics extracted from Citrus fruits and their roles in biological systems: content, and antioxidant, anti-diabetic and anti-hypertensive properties
Bibliographic record
Abstract
This study of selected plants of the Rutaceae family was carried out to investigate their phenolic content, antioxidant activity, and the in vitro inhibitory potential of extracted phenolics towards enzymes relevant for hyperglycemia and hypertension. The phenolic content, antioxidant activity and phenolic extract-mediated inhibitory activities for α-glucosidase and α-amylase were evaluated by spectrophotometry. The content of individual phenolics and the angiotensin I-converting enzyme (ACE) inhibitory activity of the phenolic extracts were evaluated by LC/MS-MS and RP-HPLC methods, respectively. A higher percentage of free phenolic content was seen for all the selected plants of the Rutaceae family (85.43-92.82% of the total phenolic content) than of the bound form (7.18-14.57% of total phenolic content). The major predominant bound phenolic in lemon and red blood orange was hesperidin. The major predominant bound phenolic in pummelo, shamouti and clementine was ferulic acid. The highest ACE and α-glucosidase inhibitory activity of the extracted phenolics from lemon was associated with free phenolic extracts obtained at 30 °C with values of 100% inhibition. Red blood orange free phenolic extract (30 °C) elicited the highest α-amylase inhibition activity (32.3%). In contrast, extracted bound phenolics after acid and base hydrolysis from all selected plants from the Citrus species were shown to induce activation of the ACE and α-amylase enzymes.
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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) | 0.001 | 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".