Do Flavonoids from Durum Wheat Contribute to Its Bioactive Properties? A Prospective Study
Bibliographic record
Abstract
A clear gap with respect to the potential biological properties of wheat flavonoids exists in the available literature. This information is crucial for breeding programs aiming to produce new varieties presenting improved health benefits. Accordingly, advanced breeding lines of whole durum wheat were evaluated in this contribution. The highest recovery of phenolics was achieved using aqueous acetone (50:50, v/v), as verified by multi-response optimization, thus showing that phenolics could be largely underestimated by employing an inappropriate extraction. The concentration of derivatives of apigenin, the main phenolics present, ranged from 63.5 to 80.7%, as evaluated by LC–ESI-QTOF-MS. Phenolics from the breeding line 98 exhibited the highest ability in scavenging peroxyl radicals, reducing power as well as in terms of inhibition of pancreatic lipase activity, a key enzyme regulating the absorption of triacylglycerols. In contrast, none of the samples exhibited a significant anti-diabetic potential. Despite their high concentration compared to that of phenolic acids, results of this work do not support a significant antioxidant and pancreatic lipase inhibitory effect of durum wheat flavonoids. Therefore, breeding programs and animal and/or human trials related to the effect of durum wheat flavonoids on oxidative stress and absorption of triacylglycerols are discouraged at this point.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".