SARS-CoV-2 infects cells of the human exocrine and endocrine pancreas and interferes with beta-cell function
Bibliographic record
Abstract
Abstract Preexisting diabetes increases the risk of a severe course of the pandemic coronavirus disease 2019 (COVID-19). Vice versa, exacerbations of a preexisting diabetes as well as new-onset diabetes have been reported upon SARS-CoV-2 infection. Thus, there is an imperative need to clarify whether human pancreatic endocrine cells organized within an islet of Langerhans are permissive for and affected by SARS-CoV-2 infection, and to elucidate the mechanisms underlying the development of diabetes upon COVID-19. Here, we (i) defined ACE2 and TMPRSS2 expression patterns in human pancreatic endocrine and exocrine cell types, (ii) employed human pancreatic islet cultures to demonstrate susceptibility to SARS-CoV-2 infection and to viral replication in β-cells, (iii) showed that SARS-CoV-2 attenuates glucose-stimulated insulin secretion, and (iv) tested remdesivir as eventually effective to prevent β-cell failure. In addition, we (v) visualized viral particles replicating in endocrine pancreatic cells and define their subcellular localization patterns via transmission electron microscopy, and finally (vi) present examples of cell type specific pancreatic infection patterns of COVID-19 deceased patients. Overall, our data demonstrate that SARS-CoV-2 can infect both the exocrine and endocrine compartments of the pancreas and can perturb β-cell integrity, which might lead to an increased risk for diabetes.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".