Inflammatory β-Cell Stress and Immune Surveillance in Type 1 Diabetes
Bibliographic record
Abstract
Recent years have seen increased recognition for the role of β-cell stress as a contributing factor to the autoimmune destruction process that ultimately results in symptomatic type 1 diabetes (T1D). Preclinical studies have discovered a variety of stress responses in the β-cell that occur at presymptomatic stages and contribute to disease progression, but unifying explanations of how these mechanisms operate to promote disease progression remain incomplete. We propose that stressed β-cells transition into β-cells expressing inflammatory molecules that provoke an immune response to restore homeostasis by coordinating islet repair and the removal of stressed cells. However, when immune surveillance fails, stressed β-cells accumulate and contribute to autoimmunity. Therapies directed toward stressed β-cells to either curb their inflammatory signaling or to eliminate them (essentially doing the job of the failed immune surveillance) are moving from animal models into the clinic with promising initial results, although the understanding of how the immune response is coordinated by stressed β-cells is not clear. In this article, we discuss β-cell stress responses implicated in T1D pathogenesis based on evidence from humans and highlight existing knowledge gaps in their mechanisms. Future work in this field is poised to target T1D by simultaneously targeting stressed β-cells and the failed immune response to halt the progression of autoimmunity and prevent β-cell destruction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".