Recent Developments in Islet Biology: A Review With Patient Perspectives
Bibliographic record
Abstract
Navigating the coronavirus disease-2019 (COVID-19, now COVID) pandemic has required resilience and creativity worldwide. Despite early challenges to productivity, more than 2,000 peer-reviewed articles on islet biology were published in 2021. Herein, we highlight noteworthy advances in islet research between January 2021 and April 2022, focussing on 5 areas. First, we discuss new insights into the role of glucokinase, mitogen-activated protein kinase-kinase/extracellular signal-regulated kinase and mitochondrial function on insulin secretion from the pancreatic β cell, provided by new genetically modified mouse models and live imaging. We then discuss a new connection between lipid handling and improved insulin secretion in the context of glucotoxicity, focussing on fatty acid-binding protein 4 and fetuin-A. Advances in high-throughput "omic" analysis evolved to where one can generate more finely tuned genetic and molecular profiles within broad classifications of type 1 diabetes and type 2 diabetes. Next, we highlight breakthroughs in diabetes treatment using stem cell-derived β cells and innovative strategies to improve islet survival posttransplantation. Last, we update our understanding of the impact of severe acute respiratory syndrome-coronavirus-2 infection on pancreatic islet function and discuss current evidence regarding proposed links between COVID and new-onset diabetes. We address these breakthroughs in 2 settings: one for a scientific audience and the other for the public, particularly those living with or affected by diabetes. Bridging biomedical research in diabetes to the community living with or affected by diabetes, our partners living with type 1 diabetes or type 2 diabetes also provide their perspectives on these latest advances in islet biology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".