Cisplatin exposure dysregulates pancreatic islet function in male mice
Bibliographic record
Abstract
Abstract Cancer survivors have an increased risk of developing new-onset Type 2 diabetes compared to the general population. Moreover, patients treated with cisplatin, a commonly used chemotherapeutic agent, are more likely to develop metabolic syndrome and Type 2 diabetes compared to age- and sex-matched controls. Insulin-secreting beta cells—located within pancreatic islets—are critical for maintaining glucose homeostasis, and dysregulated insulin secretion is central to Type 2 diabetes pathophysiology. Surprisingly, the impact of cisplatin treatment on pancreatic islets has not been reported. In this study, we aimed to determine if murine islet function is adversely affected by direct or systemic exposure to cisplatin. In vitro cisplatin exposure to male mouse islets profoundly dysregulated insulin release, reduced oxygen consumption, and altered the expression of genes related to insulin production, oxidative stress, and the Bcl-2 family. In vivo cisplatin exposure led to sustained hypoinsulinemia and hypoglycemia in male mice. Pancreas tissues from cisplatin-exposed male mice showed increased proinsulin accumulation and expression of DNA-damage markers in beta cells, but no change in average islet size or % insulin + area per islet. Our data suggest both direct and systemic cisplatin exposure cause acute defects in insulin secretion and may have lasting effects on islet health in mice.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".