<i>In Vitro</i>Evaluations of Cytotoxicity of Eight Antidiabetic Medicinal Plants and Their Effect on GLUT4 Translocation
Bibliographic record
Abstract
Despite the enormous achievements in conventional medicine, herbal-based medicines are still a common practice for the treatment of diabetes. Trigonella foenum-graecum, Atriplex halimus, Olea europaea, Urtica dioica, Allium sativum, Allium cepa, Nigella sativa, and Cinnamomum cassia are strongly recommended in the Greco-Arab and Islamic medicine for the treatment and prevention of diabetes. Cytotoxicity (MTT and LDH assays) of the plant extracts was assessed using cells from the liver hepatocellular carcinoma cell line (HepG2) and cells from the rat L6 muscle cell line. The effects of the plant extracts (50% ethanol in water) on glucose transporter-4 (GLUT4) translocation to the plasma membrane was tested in an ELISA test on L6-GLUT4myc cells. Results obtained indicate that Cinnamomon cassia is cytotoxic at concentrations higher than 100 μ g/mL, whereas all other tested extracts exhibited cytotoxic effects at concentrations higher than 500 μ g/mL. Exposing L6-GLUT4myc muscle cell to extracts from Trigonella foenum-graecum, Urtica dioica, Atriplex halimus, and Cinnamomum verum led to a significant gain in GLUT4 on their plasma membranes at noncytotoxic concentrations as measured with MTT assay and the LDH leakage assay. These findings indicate that the observed anti-diabetic properties of these plants are mediated, at least partially, through regulating GLUT4 translocation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".