Combined treatment with interleukin-1 and tumor necrosis factor-alpha antagonists improve type 2 diabetes in rats
Bibliographic record
Abstract
In the present study, combined treatment with etanercept and anakinra were tested in the streptozotocin-induced diabetic rats. Forty male Wistar albino rats were divided into 5 groups: healthy control (HC), diabetic control (DC), diabetic + anakinra (DAT), diabetic + etanercept (DET), and diabetic + etanercept + anakinra (DEAT). HC and DC groups received subcutaneous (s.c.) injection with a saline solution, while DAT and DET groups received anakinra (10 mg/kg per day, s.c.) or etanercept (10 mg/kg, twice a week, s.c.), and DEAT rats received both anakinra and etanercept treatments for 21 days after diabetes has developed. Anakinra and etanercept treatments significantly increased insulin and homeostatic model assessment β-cell function levels and decreased glucose levels compared to the DC group as single (DAT and DET) and combined treatments (DEAT). The thiobarbituric acid reactive substances level was significantly decreased in DAT group. The combine use of etanercept and anakinra can improve insulin and blood glucose in type 2 diabetic rats. The combined treatment of anakinra and etanercept together was more effective than single treatment and might have a potential new treatment strategy and to reduce the mortality and morbidity resulting from diabetes.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".