Time-dependent therapeutic roles of nitazoxanide on high-fat diet/streptozotocin-induced diabetes in rats: effects on hepatic peroxisome proliferator-activated receptor-gamma receptors
Bibliographic record
Abstract
Targeting peroxisome proliferator-activated receptor-gamma (PPAR-γ) is an approved strategy in facing insulin resistance (IR) for diabetes mellitus (DM) type 2. The PPAR-γ modulators display improvements in the insulin-sensitizing and adverse effects of the traditional thiazolidinediones. Nitazoxanide (NTZ) is proposed as a PPAR-γ receptor ligand with agonistic post-transcriptional effects. Currently, NTZ antidiabetic activities versus pioglitazone (PIO) in a high-fat diet/streptozotocin rat model of type 2 diabetes was explored. Diabetic adult male Wistar rats were treated orally with either PIO (2.7 mg·kg−1·day−1) or NTZ (200 mg·kg−1·day−1) for 14, 21, and 28 days. Body masses, fasting blood glucose, IR, lipid profiles, and liver and kidney functions of rats were assayed. Hepatic glucose metabolism and PPAR-γ protein expression levels as well as hepatic, pancreatic, muscular, and renal histopathology were evaluated. Significant time-dependent euglycemic and insulin-sensitizing effects with preservation of liver and kidney functions were offered by NTZ. Higher hepatic levels of glucose-6-phosphatase and glucose-6-phosphate dehydrogenase enzymes and PPAR-γ protein expressions were acquired by NTZ and PIO, respectively. NTZ could be considered an oral therapeutic strategy for DM type 2. Further systematic NTZ/PPAR-γ receptor subtype molecular activations are recommended. Simultaneous use of NTZ with other approved antidiabetics should be explored.
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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 | 0.000 |
| 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 | 0.001 |
| 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".