Anti-diabetic effect of a combination of black seed (Nigella sativa) and cumin (Cuminum cyminum), a two-step study from bench to bed
Bibliographic record
Abstract
Background: Type 2 Diabetes mellitus (T2DM) is a prevalent chronic condition causing one-fifth million deaths globally. Standard treatments for diabetes are not sufficiently effective, highlighting the need to find adjunctive treatments to improve glycemic control. Nigella sativa (NS) and Cuminum cyminum (CC) are two herbal medicines known for their antioxidant, anti-inflammatory, and antidiabetic properties. Objective: This study aims to assess the impact of supplementation with a combination of NS and CC on glycemic regulation among individuals with T2DM. The research was conducted in two phases, pre-clinical and clinical. Methods: In the in-vivo phase, thirty-two male mice were allocated into four groups, each comprising eight mice. These groups were categorized as follows: control, diabetic-type 2, diabetic-intervention-NS, and diabetic-intervention-NS+CC. Following the induction of diabetes using Streptozotocin, the mice in NS and NS-CC groups received 400 mg/kg/d or 360 mg/kg/d NS oil plus 40 mg/kg/d CC oil respectively for 30 days. An insulin tolerance test (ITT), an intraperitoneal glucose tolerance test (IPGTT), and blood glucose were conducted subsequently. In the clinical study, eighty patients who have been clinically diagnosed with type 2 diabetes were randomly assigned to two distinct groups to receive a daily dosage of 2000 mg NS-CC soft gel or sunflower oil soft gel for 12 weeks. The examination of fasting blood glucose (FBG) and hemoglobin A1C (HbA1C) was performed both before and after the completion of the intervention. Results: Preclinical findings exhibited a notable decrease in glucose levels in NS (decrease from 271.1±21.71 to 167.6±8.14 mg/dl) and NS-CC (decrease from 275.4±25.57 to 136±13.07 mg/dl). Also, ITT and IPGTT levels showed significantly more reduction in NS and NS-CC compared to the non-treated diabetic group. Treating with NS-CC, but not NS alone decreased ITT to a normal level with no significant difference with the non-diabetic group. In phase 2, supplementation with NS-CC led to a significantly greater reduction of the mean of fasting blood sugar (FBS) (P=0.001) concentration and HbA1C (P<0.001) compared to placebo. Conclusions: NS-CC oil may reduce insulin resistance and blood glucose levels in murine models and individuals with T2DM. Keywords: Nigella sativa, Cuminum cyminum, Type 2 diabetes mellitus, Glycemic control, Diabetic mice
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".