Resveratrol attenuates hIAPP amyloid formation and restores the insulin secretion ability in hIAPP-INS1 cell line via enhancing autophagy
Bibliographic record
Abstract
It has been proved that human islet amyloid polypeptide (hIAPP), the main constituent of islet amyloid deposition, is one of the important factors that can induce type 2 diabetes or graft failure after islet transplantation. As there is no research on whether resveratrol degrading the amyloid deposition by its special chemical structure or enhancing autophagy had been published, we decided to detect the function of resveratrol in degrading the amyloid deposition in pancreatic beta cells. We established stable hIAPP-INS1 cell line via transfecting INS1 cells by lentivirus that overexpresses hIAPP. Our research demonstrates that amyloid deposition existed in hIAPP-INS1 cell by the thioflavin S fluorescent staining, meanwhile the function of insulin secretion of hIAPP-INS1 cells was decreased significantly (p < 0.01). After treatment with resveratrol (20 μM) for 24 h, amyloid deposition in hIAPP-INS1 cells was decreased significantly, and the insulin secretion was restored significantly (p < 0.01). Once inhibited the autophagy of hIAPP-INS1 cells by 3-methyladenine for 24 h, resveratrol does not effectively remove hIAPP deposits again, and cannot improve the function of insulin secretion. These results provide a novel thought that resveratrol can degrade the amyloid deposition in type 2 diabetes and the graft after islet transplantation.
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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) | 0.001 | 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".