New insight on the role of liraglutide in alleviating dexamethasone-induced pancreatic cytotoxicity via improving redox status, autophagy flux, and PI3K/Akt/Nrf2 signaling
Bibliographic record
Abstract
Chronic glucocorticoids therapy is commonly complicated by steroid diabetes, although the underlying mechanisms are still elusive. Liraglutide, a glucagon-like peptide-1, was initially found to induce glycemic control and recently it was found to have many pleotropic effects; however, its role in pancreas remains unknown. The present study aims to estimate the protective role of liraglutide on dexamethasone-induced pancreatic cytotoxicity and hyperglycemia, highlighting the possible underlying biochemical, molecular, and cellular mechanisms. Twenty-eight male Wistar rats were involved in this study and were randomly divided into four groups. Group III and IV were treated with 1 mg/kg dexamethasone daily for 10 days. Group II and IV were treated with liraglutide in a dose of 0.8 mg/kg per day for 2 weeks. Pancreatic caspase-9, nuclear factor erythroid 2-related factor 2 (Nrf2), phospho-protein kinase-B (pAkt), and sequestrome 1 (p62) levels were assessed by immunoassay. Moreover, phosphoinositide 3-kinase (PI3K) expression by real-time PCR, microtubule-associated protein light chain 3 (LC3B) expression by immunohistochemistry, glycemic status, β-cell function by homoeostasis model assessment (HOMA) β index, and pancreatic redox status were assessed. Liraglutide improved blood glucose level, β-cell function, pancreatic caspase-9 level, redox status, and autophagy. Additionally, it increased pancreatic PI3K, pAkt, and Nrf2 levels. Moreover, preservation of pancreatic histological and the ultrastructural morphological features of β- and α-cells were observed. In conclusion, liraglutide protected against dexamethasone-induced pancreatic injury and hyperglycemia and decelerated the progression towards steroid diabetes via activating PI3K/Akt/Nrf2 signaling and autophagy flux pathways.
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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 | 0.001 |
| 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".