Tregs control the development of symptomatic West Nile virus infection in humans and mice
Bibliographic record
Abstract
West Nile virus (WNV) causes asymptomatic infection in most humans, but for undefined reasons, approximately 20% of immunocompetent individuals develop West Nile fever, a potentially debilitating febrile illness, and approximately 1% develop neuroinvasive disease syndromes. Notably, since its emergence in 1999, WNV has become the leading cause of epidemic viral encephalitis in North America. We hypothesized that CD4+ Tregs might be differentially regulated in subjects with symptomatic compared with those with asymptomatic WNV infection. Here, we show that in 32 blood donors with acute WNV infection, Tregs expanded significantly in the 3 months after index (RNA+) donations in all subjects. Symptomatic donors exhibited lower Treg frequencies from 2 weeks through 1 year after index donation yet did not show differences in systemic T cell or generalized inflammatory responses. In parallel prospective experimental studies, symptomatic WNV-infected mice also developed lower Treg frequencies compared with asymptomatic mice at 2 weeks after infection. Moreover, Treg-deficient mice developed lethal WNV infection at a higher rate than controls. Together, these results suggest that higher levels of peripheral Tregs after infection protect against severe WNV disease in immunocompetent animals and humans.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".