New‐onset type 1 diabetes and severe acute respiratory syndrome coronavirus 2 infection
Bibliographic record
Abstract
Abstract Type 1 diabetes (T1D) is a condition characterized by an absolute deficiency of insulin. Loss of insulin‐producing pancreatic islet β cells is one of the many causes of T1D. Viral infections have long been associated with new‐onset T1D and the balance between virulence and host immunity determines whether the viral infection would lead to T1D. Herein, we detail the dynamic interaction of pancreatic β cells with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and the host immune system with respect to new‐onset T1D. Importantly, β cells express the crucial entry receptors and multiple studies confirmed that β cells are infected by SARS‐CoV‐2. Innate immune system effectors, such as natural killer cells, can eliminate such infected β cells. Although CD4+CD25+FoxP3+ regulatory T (TREG) cells provide immune tolerance to prevent the destruction of the islet β‐cell population by autoantigen‐specific CD8+ T cells, it can be speculated that SARS‐CoV‐2 infection may compromise self‐tolerance by depleting TREG‐cell numbers or diminishing TREG‐cell functions by repressing Forkhead box P3 (FoxP3) expression. However, the expansion of β cells by self‐duplication, and regeneration from progenitor cells, could effectively replace lost β cells. Appearance of islet autoantibodies following SARS‐CoV‐2 infection was reported in a few cases, which could imply a breakdown of immune tolerance in the pancreatic islets. However, many of the cases with newly diagnosed autoimmune response following SARS‐CoV‐2 infection also presented with significantly high HbA1c (glycated hemoglobin) levels that indicated progression of an already set diabetes, rather than new‐onset T1D. Here we review the potential underlying mechanisms behind loss of functional β‐cell mass as a result of SARS‐CoV‐2 infection that can trigger new‐onset T1D.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".