Cyanidin-3-O-Glucoside improves the viability of human islet cells treated with amylin or Aβ1-42 in vitro
Bibliographic record
Abstract
Islet transplantation is being considered as an alternative treatment for type 1 diabetes. Despite recent progress, transplant recipients continue to experience progressive loss of insulin independence. Cyanidin-3-O-Glucoside (C3G) has shown to be protective against damage that may lead to post-transplant islet loss. In this study, human islets cultured with or without C3G were treated with human amylin, Aβ1-42, H2O2, or rapamycin to mimic stresses encountered in the post-transplant environment. Samples of these islets were collected and assayed to determine C3G's effect on cell viability and function, reactive oxygen species (ROS), oxidative stress, amyloid formation, and the presence of inflammatory as well as autophagic markers. C3G treatment of human islets exposed to either amylin or Aβ1-42 increased cell viability (p<0.01) and inhibited amyloid formation (p<0.01). A reduction in ROS and an increase in HO-1 gene expression as well as in vitro islet function were also observed in C3G-treated islets exposed to amylin or Aβ1-42, although not significantly. Additionally, treatment with C3G resulted in a significant reduction in the protein expression of inflammatory markers IL-1β and NLRP3 (p<0.01) as well as an increase in LC3 autophagic marker (p<0.05) in human islets treated with amylin, Aβ1-42, rapamycin, or H2O2. Thus, C3G appears to have a multi-faceted protective effect on human islets in vitro, possibly through its anti-oxidant property and alteration of inflammatory as well as autophagic 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 | 0.000 |
| 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".