In vitro inhibition of starch digestive enzymes by ultrasound‐assisted extracted polyphenols from <i>Ascophyllum nodosum</i> seaweeds
Bibliographic record
Abstract
Abstract Seaweeds are gaining importance due to their antidiabetic characteristics. This study investigated the inhibitory effects of aqueous Ascophyllum nodosum extracts, obtained by ultrasound‐assisted extraction with different sonication powers (70–90 W/cm 2 ) and subjected to resin purification, against α‐amylase and α‐glucosidase enzymes. Different inhibition methodologies were carried out, preincubating the extract either with the enzyme or the substrate. Chemical characterization, in terms of proximate analysis, antioxidant capacity (2,2‐diphenyl‐1‐picryl‐hydrazyl‐hydrate [DPPH] and FRAP), and polyphenols characteristics (reversed‐phase high‐performance liquid chromatography [RP‐HPLC] and 1 H‐NMR) were carried out to explain inhibitory activities of extracts. Sonication power did not influence the proximal composition nor antiradical activity of extracts, but increasing sonication power increased inhibition capacity (>15%) against both starch digestive enzymes. The extract purification largely improved the inhibition efficiency decreasing the IC 50 of α‐amylase and α‐glucosidase by 3.0 and 6.1 times, respectively. Seaweed extracts showed greater inhibition effect when they were preincubated with the enzyme instead of the substrate. RP‐HPLC together with 1 H‐NMR spectra allowed relating the presence of uronic acids–polyphenols complexes and quinones in the extracts with the different inhibitory capacities of samples. Practical Application The study confirms that ultrasound‐assisted extracts from Ascophyllum nodosum can be used to inhibit digestive enzymes. This opens the alternative to be used in foods for modulating glycemic index.
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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 | 0.000 | 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".