Selected plant species from the Cree pharmacopoeia of northern Quebec possess anti-diabetic potential
Bibliographic record
Abstract
Type II diabetes is a major health problem worldwide. Some populations, such as aboriginal peoples, are particularly at risk for this disease. In the Cree Nation of Quebec, Canada, prevalence in adults is approaching 20%, and the consequences are compounded by low compliance with modern medicine. In 2003, we conducted an ethnobotanical study of Cree medicinal plants used for the treatment of symptoms of diabetes. This served as the basis for a project designed to identify efficacious complementary treatment options more readily accepted by this population. The present study assesses the in vitro anti-diabetic potential of extracts from the 8 most promising plants to emerge from the ethnobotanical study. Cell-based bioassays were employed to screen for (i) potentiation of glucose uptake by skeletal muscle cells (C2C12) and adipocytes (3T3-L1); (ii) potentiation of glucose-stimulated insulin secretion (GSIS) and insulin production by pancreatic beta cells (INS 832/13); (iii) potentiation of triglyceride accumulation in differentiating 3T3-L1 cells; (iv) protection against glucose toxicity and glucose deprivation in pre-sympathetic neurons (PC12-AC). Additionally, anti-oxidant activity was measured biochemically by the diphenylpicrylhydrazyl (DPPH) reduction assay. All plant extracts potentiated basal or insulin-stimulated glucose uptake to some degree in muscle cells or adipocytes. Adipocyte differentiation was accelerated by 4 extracts. Five extracts conferred protection in PC12 cells. Three extracts displayed free radical scavenging activity similar to known anti-oxidants. None of the plant extracts enhanced GSIS or insulin content in INS 832/13 beta cells. It is concluded that the Cree pharmacopoeia contains several plants with significant anti-diabetic potential.
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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 | 0.001 | 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) | 0.006 | 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".