Comprehensive Evidence-Based Assessment and Prioritization of Potential Antidiabetic Medicinal Plants: A Case Study from Canadian Eastern James Bay Cree Traditional Medicine
Bibliographic record
Abstract
Canadian Aboriginals, like others globally, suffer from disproportionately high rates of diabetes. A comprehensive evidence-based approach was therefore developed to study potential antidiabetic medicinal plants stemming from Canadian Aboriginal Traditional Medicine to provide culturally adapted complementary and alternative treatment options. Key elements of pathophysiology of diabetes and of related contemporary drug therapy are presented to highlight relevant cellular and molecular targets for medicinal plants. Potential antidiabetic plants were identified using a novel ethnobotanical method based on a set of diabetes symptoms. The most promising species were screened for primary (glucose-lowering) and secondary (toxicity, drug interactions, complications) antidiabetic activity by using a comprehensive platform of in vitro cell-based and cell-free bioassays. The most active species were studied further for their mechanism of action and their active principles identified though bioassay-guided fractionation. Biological activity of key species was confirmed in animal models of diabetes. These in vitro and in vivo findings are the basis for evidence-based prioritization of antidiabetic plants. In parallel, plants were also prioritized by Cree Elders and healers according to their Traditional Medicine paradigm. This case study highlights the convergence of modern science and Traditional Medicine while providing a model that can be adapted to other Aboriginal realities worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".