Mechanisms of Action of Indigenous Antidiabetic Plants from the Boreal Forest of Northeastern Canada
Bibliographic record
Abstract
Indigenous populations in Canada possess a wealth of native traditional knowledge. However, their rates of Type 2 diabetes mellitus (T2DM), a disease that was unheard of in their midst 50 years ago, are the highest in the country. In an effort to cut the impact of T2DM epidemic on Indigenous health, the Canadian Institutes of Health Research funded the “CIHR Team in Aboriginal Antidiabetic Medicines (CIHR-TAAM).” The goal was to explore Boreal forest medicinal plants stemming from Indigenous Traditional Medicine to be included in T2DM care. Six out of nine communities of the Cree of Eeyou Istchee (CEI) participated in ethnobotanical studies that resulted in the identification of 17 potential antidiabetic plant species. These species were screened for antidiabetic activities using a platform of in vitro bioassays and in vivo models of T2DM. This paper summarizes results on the 10 most promising plant species, their active constituents, and the mechanisms behind their antidiabetic activities. In addition, potential herb-drug interactions were examined at the level of drug-metabolizing enzymes, notably the cytochrome P450 family. This review serves as a canvas onto which is discussed the value of Indigenous medicinal plants, future avenues of research, and the ethical approach required in this field.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".