Novel Approach to Identify Potential Bioactive Plant Metabolites: Pharmacological and Metabolomics Analyses of Ethanol and Hot Water Extracts of Several Canadian Medicinal Plants of the Cree of Eeyou Istchee
Bibliographic record
Abstract
We evaluated and compared the antidiabetic potential and molecular mechanisms of 17 Cree plants' ethanol extracts (EE) and hot water extracts (HWE) on glucose homeostasis in vitro and used metabolomics to seek links with the content of specific phytochemicals. Several EE of medical plants stimulated muscle glucose uptake and inhibited hepatic G6Pase activity. Some HWE partially or completely lost these antidiabetic activities in comparison to EE. Only R. groenlandicum retained similar potential between EE and HWE in both assays. In C2C12 muscle cells, EE of R. groenlandicum, A. incana and S. purpurea stimulated glucose uptake by activating AMP-activated protein kinase (AMPK) pathway and increasing glucose transporter type 4 (GLUT4) expression. In comparison to EE, HWE of R. groenlandicum exhibited similar activities; HWE of A. incana completely lost its effect on all parameters; interestingly, HWE of S. purpurea activated insulin pathway instead of AMPK pathway to increase glucose uptake. In the liver, for a subset of 5 plants, HWE and EE activated AMPK pathway whereas the EE and HWE of S. purpurea and K. angustifolia also activated insulin pathways. Quercetin-3-O-galactoside and quercetin 3-O-α-L-arabinopyranoside, were successfully identified by discriminant analysis as biomarkers of HWE plant extracts that stimulate glucose uptake in vitro. More importantly, the latter compound was not identified by previous bioassay-guided fractionation.
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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 | 0.000 | 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".