Antidiabetic properties of polysaccharide- and polyphenolic-enriched fractions from the brown seaweed<i>Ascophyllum nodosum</i>This article is one of a selection of papers published in this special issue (part 2 of 2) on the Safety and Efficacy of Natural Health Products.
Bibliographic record
Abstract
We screened seaweed species from Atlantic Canada for antidiabetic activity by testing extracts for alpha-glucosidase inhibitory effect and glucose uptake stimulatory activity. An aqueous ethanolic extract of Ascophyllum nodosum was found to be active in both assays, inhibiting rat intestinal alpha-glucosidase (IC50 = 77 microg/mL) and stimulating basal glucose uptake into 3T3-L1 adipocytes during a 20-minute incubation by about 3-fold (at 400 microg/mL extract). Bioassay-guided fractionation of the A. nodosum extract showed that alpha-glucosidase inhibition was associated with polyphenolic components in the extract. These polyphenolics, along with other constituents appeared to be responsible for the stimulatory activity on glucose uptake. However, attempts to further concentrate this activity through fractionation techniques were unsuccessful. A crude polyphenol extract (PPE), an enriched polyphenolic fraction (PPE-F1) and a polysaccharide extract (PSE) were prepared from commercial A. nodosum powder and administered to streptozotocin-diabetic mice for up to 4-weeks by daily gavage at 200 mg/kg body mass. PPE and PPE-F1 improved fasting serum glucose level in diabetic mice; however, the effect was only statistically significant at day 14. In addition, PPE-F1 was shown to blunt the rise in blood glucose after an oral sucrose tolerance test in diabetic mice. Mice treated with PPE and PPE-F1 had decreased blood total cholesterol and glycated serum protein levels compared with untreated diabetic mice, whereas PPE also normalized the reduction in liver glycogen level that occurred in diabetic animals. All 3 A. nodosum preparations improved blood antioxidant capacity.
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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".