A diabetic <i>milieu</i> increases cellular susceptibility to SARS-CoV-2 infections in engineered human kidney organoids and diabetic patients
Bibliographic record
Abstract
Summary SARS-CoV-2 infections lead to a high risk of hospitalization and mortality in diabetic patients. Why diabetic individuals are more prone to develop severe COVID-19 remains unclear. Here, we established a novel human kidney organoid model that mimics early hallmarks of diabetic nephropathy. High oscillatory glucose exposure resulted in metabolic changes, expansion of extracellular membrane components, gene expression changes determined by scRNAseq, and marked upregulation of angiotensin-converting enzyme 2 (ACE2). Upon SARS-CoV-2 infection, hyperglycemic conditions lead to markedly higher viral loads in kidney organoids compared to normoglycemia. Genetic deletion of ACE2, but not of the candidate receptor BSG/CD147, in kidney organoids demonstrated the essential role of ACE2 in SARS-CoV-2 infections and completely prevented SARS-CoV-2 infection in the diabetogenic microenvironment. These data introduce a novel organoid model for diabetic kidney disease and show that diabetic-induced ACE2 licenses the diabetic kidney to enhanced SARS-CoV-2 replication.
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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 | 0.001 |
| 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".