Genistein and <i>Momordica charantia</i> L. prevent oxidative stress and upregulate proglucagon and insulin receptor mRNA in diabetic rats
Bibliographic record
Abstract
Type 2 diabetes occurs as a result of insulin resistance and dysfunction in insulin signaling. Controlling hyperglycemia and activation of insulin signaling are important in the management of type 2 diabetes. This study aimed to evaluate the effect of genistein and Momordica charantia L. fruit (MCF) on oxidative stress, markers of inflammation, and their role in proglucagon and insulin receptor messenger RNA (mRNA) expression by real-time PCR in diabetic rats. Thirty-five albino rats were divided into 7 groups (n = 5). Group I (non-diabetic) and group II (diabetic control) were treated with distilled water, and groups III and IV received 250 mg/kg and 500 mg/kg lyophilized MCF, respectively. Groups V and VI received 10 mg/kg and 20 mg/kg genistein, respectively, while group VII received 500 mg/kg metformin. The administration lasted for 28 days. MCF and genistein significantly reduced interleukin (IL)-1β and tumor necrosis factor alpha (TNF-α) levels, which were elevated in the serum of diabetic rats. Treatment with MCF and genistein significantly increased the expression of proglucagon mRNA in the small intestine and insulin receptor mRNA in the liver of diabetic rats. In conclusion, MCF and genistein ameliorate type 2 diabetes complications by preventing the loss of insulin-positive cells, inhibiting IL-1β and TNF-α, and upregulating proglucagon and insulin receptor mRNA expression. Novelty: MCF and genistein have an inhibitory effect on diabetic induced IL-1β and TNF-α production. MCF and genistein upregulate proglucagon and insulin receptor mRNA expression.
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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".