Exploring senescence as a modifier of β cell extracellular vesicles in type 1 diabetes
Bibliographic record
Abstract
Type 1 Diabetes (T1D) is a chronic metabolic disease resulting from insulin deficiency due to autoimmune loss of pancreatic β cells. In addition to β cell destruction, it is now accepted that β cell stress and dysfunction, such as senescence, plays a crucial role in the development of the disease. Accumulation of senescent β cells occurs during development of T1D in humans and contributes to the progression of T1D in the nonobese diabetic (NOD) mouse model. Senescent β cells are thought to exacerbate the inflammatory response within the islets by production and secretion of senescence-associated secretory phenotype (SASP). Extracellular vesicles (EVs) from β cells have been shown to carry protein and microRNAs (miRNAs), influencing cellular signaling and may contribute to the development of T1D but it remains to be addressed how senescence impacts β cell EV cargo. In this minireview, we discuss emerging evidence that EV cargo proteins and miRNAs associated with senescence could contribute to the development of T1D and could suggest potential biomarkers and therapeutic targets for the regulation of SASP and elimination of senescent β cells in T1D. Future investigation exploring the intricate relationship between β cell senescence, EVs and miRNAs could pave the way for the development of novel diagnostic techniques and therapeutic interventions.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".