Islet stress, degradation and autoimmunity
Bibliographic record
Abstract
β-cell destruction in type 1 diabetes (T1D) results from the effect of inflammation and autoimmunity. In response to inflammatory signals, islet cells engage adaptive mechanisms to restore and maintain cellular homeostasis. Among these mechanisms, the unfolded protein response (UPR) leads to a reduction of the general protein translation rate, increased production of endoplasmic reticulum chaperones and the initiation of degradation by activation of the ER associated degradation pathway (ERAD) in which newly synthetized proteins are ubiquitinylated and processed through the proteasome. This adaptive phase is also believed to play a critical role in the development of autoimmunity by the generation of neoantigens. While we have previously investigated the effect of stress on transcription, translation and post-translational events as possible source for neoantigens, the participation of the degradation machinery, yet crucial in the generation of antigenic peptides, remains to be investigated in the context of T1D pathology. In this review, we will describe the relation between the unfolded protein response and the Ubiquitin Proteasome System (UPS) and address the role of the cellular degradation machinery in the generation of antigens. Learning from tumour immunology, we propose how these processes may unmask β-cells by triggering the generation of aberrant peptides recognized by the immune cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".