<i>Rhododendron groenlandicum</i>(Labrador tea), an antidiabetic plant from the traditional pharmacopoeia of the Canadian Eastern James Bay Cree, improves renal integrity in the diet-induced obese mouse model
Bibliographic record
Abstract
Content Our team has identified Labrador tea [Rhododendron groenlandicum L. (Ericaceae)] as a potential antidiabetic plant from the traditional pharmacopoeia of the Eastern James Bay Cree. In a previous in vivo study, the plant extract was tested in a high-fat diet (HFD)-induced obese model using C57BL/6 mice and it improved glycaemia, insulinaemia and glucose tolerance. Objective In the present study, we assessed the plant's potential renoprotective effects. Materials and methods Rhododendron groenlandicum was administered at 250 mg/kg/d to mice fed HFD for 8 weeks to induce obesity and mild diabetes. Histological (periodic acid-Schiff (PAS), Masson and Oil Red O staining), immunohistochemical (IHC) and biochemical parameters were assessed to evaluate the renoprotective potential of R. groenlandicum treatment for an additional 8 weeks. Results Microalbuminuria and renal fibrosis were developed in HFD-fed mice. Meanwhile, there was a tendency for R. groenlandicum to improve microalbuminuria, with the values of albumin-creatinine ratio (ACR) reducing from 0.69 to 0.53. Renal fibrosis value was originally 4.85 arbitrary units (AU) in HFD-fed mice, dropped to 3.27 AU after receiving R. groenlandicum treatment. Rhododendron groenlandicum reduced renal steatosis by nearly one-half, whereas the expression of Bcl-2-modifying factor (BMF) diminished from 13.96 AU to 9.43 AU. Discussion and conclusions Taken altogether, the results suggest that R. groenlandicum treatment can improve renal function impaired by HFD.
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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 | 0.000 |
| 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) | 0.002 | 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".