No evidence for persistent enteroviral B infection of pancreatic islets in type 1 diabetic and pre-diabetic patients from RNA-Seq data
Bibliographic record
Abstract
Persistent enterovirus B infection has been proposed as an important contributor to the etiology of type 1 diabetes. We leveraged extensive bulk RNA-sequencing data from alpha, beta, and exocrine cells, as well as single cell islet RNA-Seq data from the Human Pancreas Analysis Program (HPAP), to evaluate the presence of enterovirus B sequences in the pancreas of type 1 diabetic and pre-diabetic (non-diabetic but auto-antibody positive) patients. We examined all available HPAP data for either assay type, including non-diabetic, type 1, and type 2 diabetic donors. To assess the presence of viral reads, we analyzed all reads not mapping to the human genome with the taxonomic classification system Kraken2 and its full viral database, augmented to encompass representatives for all (28) enterovirus B serotypes for which a complete genome is available. As a secondary approach, we input the same sequence reads into the STAR aligner using these 28 enterovirus B genomes as the reference. No enterovirus B sequences were detected by either approach in any of the 243 bulk RNA libraries nor in any of the 79 single cell RNA libraries. While we cannot rule out the possibility of a very low-grade persistent enterovirus B infection in the donors analyzed, our data do not support the notion of chronic viral infection by these viruses as a major driver of type 1 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".