Islet Biology During COVID-19: Progress and Perspectives
Bibliographic record
Abstract
The coronavirus-2019 (COVID-19) pandemic has had significant impact on research directions and productivity in the past 2 years. Despite these challenges, since 2020, more than 2,500 peer-reviewed articles have been published on pancreatic islet biology. These include updates on the roles of isocitrate dehydrogenase, pyruvate kinase and incretin hormones in insulin secretion, as well as the discovery of inceptor and signalling by circulating RNAs. The year 2020 also brought advancements in in vivo and in vitro models, including a new transgenic mouse for assessing beta-cell proliferation, a "pancreas-on-a-chip" to study glucose-stimulated insulin secretion and successful genetic editing of primary human islet cells. Islet biologists evaluated the functionality of stem-cell-derived islet-like cells coated with semipermeable biomaterials to prevent autoimmune attack, revealing the importance of cell maturation after transplantation. Prompted by observations that COVID-19 symptoms can worsen for people with obesity or diabetes, researchers examined how islets are directly affected by severe acute respiratory syndrome coronavirus 2. Herein, we highlight novel functional insights, technologies and therapeutic approaches that emerged between March 2020 and July 2021, written for both scientific and lay audiences. We also include a response to these advancements from patient stakeholders, to help lend a broader perspective to developments and challenges in islet research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".