Auto-Abs neutralizing type I IFNs in patients with severe Powassan, Usutu, or Ross River virus disease
Bibliographic record
Abstract
Arboviral diseases are a growing global health concern. Pre-existing autoantibodies (auto-Abs) neutralizing type I interferons (IFNs) can underlie encephalitis due to West Nile virus (WNV) (∼40% of patients) and tick-borne encephalitis (TBE, due to TBE virus [TBEV]) (∼10%). We report here that these auto-Abs can also underlie severe forms of rarer arboviral infections. Auto-Abs neutralizing high concentrations of IFN-α2, IFN-β, and/or IFN-ω are present in the single case of severe Powassan virus (POWV) encephalitis studied, two of three cases of severe Usutu virus (USUV) infection studied, and the most severe of 24 cases of Ross River virus (RRV) disease studied. These auto-Abs are not found in any of the 137 individuals with silent or mild infections with these three viruses. Thus, auto-Abs neutralizing type I IFNs underlie an increasing list of severe arboviral diseases due to Flaviviridae (WNV, TBEV, POWV, USUV) or Togaviridae (RRV) viruses transmitted to humans by mosquitos (WNV, USUV, RRV) or ticks (TBEV, POWV).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".