Autoantibodies neutralizing type I IFNs in 40% of patients with WNV encephalitis in seven new cohorts
Bibliographic record
Abstract
Abstract Mosquito-borne West Nile virus (WNV) infection is a growing global health problem. About 0.5% of infected individuals develop encephalitis. We previously showed that 40% of patients in six cohorts had WNV encephalitis because of circulating auto-antibodies (auto-Abs) neutralizing type I IFNs. In seven new cohorts, we found that the prevalence of auto-Abs was highest (40% [17-44%]) in patients with encephalitis, and very low in a small sample of individuals with asymptomatic or mild infection. In the 13 European, Middle-Eastern and American cohorts available, odds ratios for WNV encephalitis in individuals with these auto-Abs relative to those without them in a large sample of the general population untested for WNV infection range from ∼20 (OR=17.7; 95% CI: 13.8-22.8, p <10 −16 ) for auto-Abs neutralizing only 100 pg/mL IFN-α2 and/or IFN-ω to >2000 (OR=2218.4; 95% CI: 125.1-39337.7, p <10 −16 ) for auto-Abs neutralizing high concentrations of IFN-α2 and high or low concentrations of IFN-ω. Pre-existing autoantibodies neutralizing type I IFNs are therefore causal for WNV encephalitis in about 40% of patients. Summary In 13 cohorts of individuals with WNV infection, the risk of WNV encephalitis is increased 20 to >2,000 times by circulating auto-Abs neutralizing type I IFNs, depending on the concentration and combination of type I IFNs neutralized and patient age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".