Evaluation of the antidiabetic potential of selected medicinal plant extracts from the Canadian boreal forest used to treat symptoms of diabetes: part II
Bibliographic record
Abstract
Among the Cree of northern Quebec, the disproportionately high rate of diabetic complications is largely due to the cultural inadequacy of modern therapies for type 2 diabetes. To establish culturally adapted antidiabetic treatments, our team identified several candidate plant species used by the Cree to treat symptoms of diabetes. An initial study focused on 8 species and revealed that most possess significant in vitro antidiabetic activity. The purpose of the present study was to assess a further 9 species identified through the ethnobotanical survey. Crude plant extracts were screened for (i) potentiation of basal and insulin-stimulated glucose uptake by skeletal muscle cells (C2C12) and adipocytes (3T3-L1); (ii) potentiation of glucose-stimulated insulin secretion by pancreatic beta cells (betaTC); (iii) potentiation of adipogenesis in 3T3-L1 cells; (iv) protection against glucose toxicity and glucose deprivation in PC12-AC neuronal precursor cells; and (v) diphenylpicrylhydrazyl (DPPH) oxygen free radical scavenging. Four species potentiated basal glucose uptake in muscle cells or adipocytes, one species being as potent as metformin. Adipogenesis was accelerated by 4 species with a potency roughly half that of rosiglitazone. Five species protected PC12-AC cells against glucose toxicity and 4 protected against glucose deprivation. Five species exhibited antioxidant activity comparable to ascorbic acid. However, no species increased insulin secretion. The present study revealed that Gaultheria hispidula, Rhododendron tomentosum, and Vaccinium vitis-idaea exhibit a promising profile of antidiabetic potential and are good candidates for more in-depth evaluation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".