Antidiabetic Properties of a Spice Plant Nigella sativa
Bibliographic record
Abstract
Seeds of Nigella sativa (black cumin/kalonji) used in pickles as spice, have also been traditionally used in treatment of many diseases including diabetes and hypertension. Among many activities exhibited by N. sativa and its constituents in animal experiments, antidiabetic property is most important. Thymoquinone (TQ), a volatile oil, is one of its active constituents but antidiabetic activity has also been shown by its aqueous extract and defatted extract. N. sativa may be beneficial in diabetic individuals and those with glucose intolerance as it reduces appetite, glucose absorption in intestine, hepatic gluconeogenesis, blood glucose level, cholesterol, triglycerides, body weight and simulates glucose induced secretion of insulin from beta-cells in pancreas; improves glucose tolerance as efficiently as metformin; yet it has not shown significant adverse effects and has very low toxicity. In streptozotocin (STZ) induced diabetic rats it causes gradual partial regeneration of pancreatic beta-cells, increases the lowered serum insulin concentrations and decreases the elevated serum glucose. N. sativa has antioxidant activity and protective role of TQ against development of type I diabetes may be via NO inhibitory pathway. It also exerts an insulin-sensitizing action in hepatocytes. Seeds of N. sativa have been safely consumed by human patients in many clinical trials which however were not aimed to assess its antidiabetic activity. In future clinical studies may show potential of N. sativa , its constituents or their s ynthetic analogues, in prevention and control of diabetes. J Endocrinol Metab. 2011;1(1):1-8 doi: https://doi.org/10.4021/jem12e
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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) | 0.001 | 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".