Inhibitory effect of the cree traditional medicine wiishichimanaanh (<i>Vaccinium vitis‐idaea</i>) on advanced glycation endproduct formation: identification of active principles
Bibliographic record
Abstract
Like many aboriginal populations, First Nations communities such as the Cree of Eeyou Istchee are facing continuously increasing rates of diabetes and related complications. Advanced glycation endproducts (AGEs), which readily form and accumulate with sustained hyperglycemia, contribute to the development of diabetic complications and, as such, are considered a potential therapeutic target. In the present study, the inhibition of AGE formation by ethanolic extracts of the Cree medicinal plant Vaccinium vitis-idaea L. was assessed by fluorometric detection of fluorescent AGEs and immunodetection of N(epsilon)-(carboxymethyl)lysine adducts of albumin. Extracts from V. vitis-idaea berries demonstrated a concentration-dependent inhibition of AGE formation in both measures. High performance liquid chromatography mass spectrometry (HPLC/MS) identified nine main phenolic constituents. Four were selected for further testing, of which catechin, quercetin-3-O-galactoside and cyanidin-3-O-glucoside but not para-coumaric acid displayed antiglycation activities. These results demonstrate that the flavonoid components of the berry extract are potent antiglycation agents and provide pharmacological validation for the traditional use of V. vitis-idaea as an antidiabetic remedy.
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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".