Editorial: Cellular senescence in diabetes: from markers to mechanisms and therapies
Bibliographic record
Abstract
Editorial on the Research Topic Cellular senescence in diabetes: from markers to mechanisms and therapies Cellular senescence is a durable cell cycle arrest and response to stress that increases with age.It leads to the development of hallmark attributes such as persistent DNA damage response, metabolic adaptation, apoptosis resistance, and a secretory phenotype (the Senescence-Associated Secretory Phenotype, SASP).Recent work has shown that senescence is involved in a variety of endocrine and metabolic disorders such as obesity, fatty liver, cardiovascular disease, and diabetes, including Type 1 Diabetes, Type 2 Diabetes, and monogenic diabetes.However, due to the highly heterogeneous nature of senescence and context specificity (such as cell-type and developmental stage), there are major gaps in our understanding.Some of these gaps are: 1) lack of understanding about the mechanisms leading to activation of senescence and senescent cell accumulation during the progression of metabolic diseases, 2) specific markers that accurately distinguish senescent cells from their non-senescent counterparts still need to be determined, and 3) therapeutics that can safely and effectively counteract or mitigate senescent cell accumulation in diabetes and metabolic diseases are being studied.This Research Topic aims to examine the current status of knowledge on b-cell senescence, some of the mechanisms by which it develops, its relevance in health and disease, and its potential as a therapeutic target.The present Research Topic assembles two original research papers that explore the role of b-cell senescence in the generational transmission of metabolic dysfunction and the interaction of senescence with altered nutrient sensing, another hallmark of aging.Additionally, two review papers and one perspective consolidate the latest knowledge on b-cell senescence, mechanisms, and markers, examining strategies for targeting this population and proposing an interesting perspective on the potential role for senescence during pancreatic islet development.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".