IFN-α Induces Heterogenous ROS Production in Human β-Cells
Bibliographic record
Abstract
ABSTRACT Type 1 diabetes (T1D) is a multifactorial disease involving genetic and environmental factors, including viral infection. We investigated the impact of interferon alpha (IFN-α), a cytokine produced during the immune response to viral infection or the presence of un-edited endogenous double-stranded RNAs, on human β-cell physiology. Intravital microscopy on transplanted human islets using a β-cell-selective reactive oxygen species (ROS) biosensor (RIP1-GRX1-roGFP2), revealed a subset of human β-cells that acutely produce ROS in response to IFN-α. Comparison to Integrated Islet Distribution Program (IIDP) phenotypic data revealed that healthier donors had more ROS accumulating cells. I n vitro IFN-α treatment of human islets similarly elicited a heterogenous increase in superoxide production that originated in the mitochondria. To determine the unique molecular signature predisposing cells to IFN-α stimulated ROS production, we flow sorted human islets treated with IFN-α. RNA sequencing identified genes involved in inflammatory and immune response in the ROS-producing cells. Comparison with single cell RNA-Seq datasets available through the Human Pancreas Analysis Program (HPAP) showed that genes upregulated in ROS-producing cells are enriched in control β-cells rather than T1D donors. Combined, these data suggest that IFN-α stimulates mitochondrial ROS production in healthy human β-cells, potentially predicting a more efficient antiviral response.
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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".