Higher abundance of enterovirus A species in the gut of children with islet autoimmunity
Bibliographic record
Abstract
Enteroviruses (EVs) are prime candidate environmental triggers of islet autoimmunity (IA), with potential as vaccine targets for type 1 diabetes prevention. However, the use of targeted virus detection methods and the selective focus on EVs by most studies increases the risk for substantial investigation bias and an overestimated association between EV and type 1 diabetes. Here we performed comprehensive virome-capture sequencing to examine all known vertebrate-infecting viruses without bias in 182 specimens (faeces and plasma) collected before or at seroconversion from 45 case children with IA and 48 matched controls. From >2.6 billion reads, 28 genera of viruses were detected and 62% of children (58/93) were positive for ≥1 vertebrate-infecting virus. We identified 129 viruses as differentially abundant between the gut of cases and controls, including 5 EV-A types significantly more abundant in the cases. Our findings further support EV's hypothesised contribution to IA and corroborate the proposal that viral load may be an important parameter in disease pathogenesis. Furthermore, our data indicate a previously unrecognised association of IA with higher EV-A abundance in the gut of children and provide a catalog of viruses to be interrogated further to determine a causal link between virus infection and 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".