Multiparameter Screening Reveals a Role for Na+ Channels in Cytokine-Induced β-Cell Death
Bibliographic record
Abstract
Pancreatic β-cell death plays a role in both type 1 and type 2 diabetes, but clinical treatments that specifically target β-cell survival have not yet been developed. We have recently developed live-cell imaging-based, high-throughput screening methods capable of identifying factors that modulate pancreatic β-cell death, with the hope of finding drugs that can intervene in this process. In the present study, we used a high-content screen and the Prestwick Chemical Library of small molecules to identify drugs that block cell death resulting from exposure to a cocktail of cytotoxic cytokines (25 ng/mL TNF-α, 10 ng/mL IL-1β, and 10 ng/mL IFN-γ). Data analysis with self-organizing maps revealed that 19 drugs had profiles similar to that of the no cytokine condition, indicating protection. Carbamazepine, an antiepileptic Na(+) channel inhibitor, was particularly interesting because Na(+) channels are not generally considered targets for antiapoptotic therapy in diabetes and because the function of these channels in β-cells has not been well studied. We analyzed the expression and characteristics of Na(+) currents in mature β-cells from MIP-GFP mice. We confirmed the dose-dependent protective effects of carbamazepine and another use-dependent Na(+) channel blocker in cytokine-treated mouse islet cells. Carbamazepine down-regulated the proapoptotic and endoplasmic reticulum stress signaling induced by cytokines. Together, these studies point to Na(+) channels as a novel therapeutic target in diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".