A Multivariate Approach to Ethnopharmacology: Antidiabetic Plants of Eeyou Istchee
Bibliographic record
Abstract
An ethnopharmacological metanalysis was conducted with a large database available on antidiabetic activities of plant foods and medicines from the northern boreal forest, which are traditionally used by the indigenous Cree of James Bay, Quebec, Canada. The objective was to determine which bioassays are closely associated with the traditional knowledge of the Cree and which pharmacological metrics and phytochemical signals best define these plants and their groups. Data from 17 plant species, ethnobotanically ranked by syndromic importance value for treatment of 15 diabetic symptoms, was used along with 49 bioassay endpoints reported across numerous pharmacological studies and a metabolomics dataset. Standardized activities were separated into primary, secondary and safety categories and summed to produce a Pharmacological Importance Value (PIV) in each of the three categories for each species. To address the question of which pharmacological metrics and phytochemical signals best define the CEI anti-diabetes plants, multivariate analyses were undertaken to determine groupings of plant families and plant parts. The analysis identified Larix larcina as the highest PIV species in primary assays, Salix planifolia in secondary assays, and Kalmia angustifolia in safety assays, as well as a ranking of other less active species by PIV. Multivariate analysis showed that activity in safety PIV monitored mainly with cytochrome P450 inhibition patterns best reflected patterns of traditional medicine importance in Cree traditional knowledge, whereas potent primary bioactivities were seen in individual plants determined to be most important to the Cree for anti-diabetes purposes. In the secondary anti-diabetes assays, pharmacological variability was better described by plant biology, mostly in terms of the plant part used. Key signal in the metabolomics loadings plots for activity were phenolics especially quercetin derivatives. Traditional Indigenous knowledge in this analysis was shown to be able to guide the identification of plant pharmacological qualities in scientific terms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".