Grape Seed Proanthocyanidins Protect Pancreatic β Cells Against Ferroptosis via the Nrf2 Pathway in Type 2 Diabetes
Bibliographic record
Abstract
Pancreatic β cell damage is the primary contributor to type 2 diabetes mellitus (T2DM); however, the underlying mechanism remains nebulous. This study explored the role of ferroptosis in pancreatic β cell damage and the protective effects of grape seed proanthocyanidin extract (GSPE). In T2DM model rats, the blood glucose, water intake, urine volume, HbA1c, and homeostasis model assessment-insulin resistance were significantly increased, while the body weight and the insulin level were significantly decreased, indicating the successful establishment of the T2DM model. MIN6 mouse insulinoma β cells were cultured in high glucose and sodium palmitate conditions to obtain a glycolipid damage model, which was administered with GSPE, ferrostatin-1 (Fer-1), or nuclear factor erythroid 2-related factor 2 (Nrf2) small interfering (si) RNA. GSPE and Fer-1 treatment significantly improved pancreatic β-cell dysfunction and protected against cell death. Both treatments increased the superoxide dismutase and glutathione activity, reduced the malondialdehyde and reactive oxygen species levels, and improved iron metabolism. Furthermore, the treatments reversed the expression of ferroptosis markers cysteine/glutamate transporter (XCT) and glutathione peroxidase 4 (GPX4) caused by glycolipid toxicity. GSPE treatments activated the expression of Nrf2 and related proteins. These effects were reversed when co-transfected with si-Nrf2. GSPE inhibits ferroptosis by activating the Nrf2 signaling pathway, thus reducing β-cell damage and dysfunction in T2DM. Therefore, GSPE is a potential treatment strategy against T2DM.
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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".