Antiglycation activity of <i>Vaccinium</i> spp. (Ericaceae) from the Sam Vander Kloet collection for the treatment of type II diabetes<sup>1</sup>This article is part of a Special Issue entitled “A tribute to Sam Vander Kloet FLS: Pure and applied research from blueberries to heathland ecology”.
Bibliographic record
Abstract
In this report, the inhibition of advanced glycation endproducts (AGEs) by extracts of leaves from a collection of six, mainly tropical, Vaccinium L. spp. (Ericaceae) was examined. Indigenous Peoples have used Vaccinium species to treat symptoms of type I and II diabetes. Sustained hyperglycaemia, often associated with diabetes, facilitates crosslinking of sugars with proteins, producing AGEs. AGEs are a therapeutic target since they are responsible for many diabetes symptoms and contribute to ageing and the development of atherosclerosis, kidney, vascular, and neurological diseases. Vaccinium barandanum S. Vidal, Vaccinium consanguineum Klotzsch, Vaccinium gaultheriifolium (Griff.) Hook. f. ex C.B. Clarke, Vaccinium poasanum Donn. Sm., Vaccinium tonkinense Dop, and Disterigma rimbachii (A.C. Sm.) Luteyn (outgroup) were collected from Sam Vander Kloet’s common garden collection. Ethanolic extracts of leaves of these Vaccinium spp. were potent inhibitors of AGEs. Vaccinium and outgroup species extracts tested in an AGE inhibition assay demonstrated concentration dependent inhibition, with a half maximal inhibitory concentration (IC50) ranging from 4.2 to 16.2 µg·mL–1. Phenolic content ranged from 258 to 626 (µg quercetin equivalents·mg extract–1). Activity and phenolic content show that these tropical accessions have a higher phenolic content (p < 0.001, t test) and AGE inhibition (p < 0.03, t test) than six temperate species from our collections in eastern North America. Significant relationships were found between IC50 and latitude of geographic origin.
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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".