Multiple molecular targets underlie the antidiabetic effect of <i>Nigella sativa</i> seed extract in skeletal muscle, adipocyte and liver cells
Bibliographic record
Abstract
AIM: Nigella sativa (N. sativa) is a plant widely used in traditional medicine of North African countries. During the last decade, several studies have shown that extracts from the seeds of N. sativa have antidiabetic effects. METHODS: Our group has recently demonstrated that N. sativa seed ethanol extract (NSE) induces an important insulin-like stimulation of glucose uptake in C2C12 skeletal muscle cells and 3T3-L1 adipocytes following an 18 h treatment. The purpose of the present study was to elucidate the pathways mediating this insulin-like effect and the mechanisms through which these pathways are activated. RESULTS: Results from western immunoblot experiments indicate that in C2C12 cells as well as in H4IIE hepatocytes, but not in 3T3-L1 cells, NSE increases activity of Akt, a key mediator of the effects of insulin, and activity of AMP-activated protein kinase (AMPK), a master metabolic regulating enzyme. To test whether the activation of AMPK resulted from a disruption of mitochondrial function, the effects of NSE on oxygen consumption were assessed in isolated liver mitochondria. NSE was found to exhibit potent uncoupling activity. CONCLUSION: Finally, to provide an explanation for the effects of NSE in adipocytes, PPARgamma stimulating activity was tested using a reporter gene assay. Results indicate that NSE behaves as an agonist of PPARgamma. The data supports the ethnobotanical use of N. sativa seed oil as a treatment for diabetes, and suggests potential uses of this product, or compounds derived thereof, against obesity and the metabolic syndrome.
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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 | 0.000 | 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".