SARS-CoV-2 human challenge reveals biomarkers that discriminate early and late phases of respiratory viral infections
Bibliographic record
Abstract
Blood transcriptional biomarkers of acute viral infections typically reflect type 1 interferon (IFN) signalling, but it is not known whether there are biological differences in their regulation that can be leveraged for distinct translational applications. We use high frequency sampling in the SARS-CoV-2 human challenge model to show induction of IFN-stimulated gene (ISG) expression with different temporal and cellular profiles. MX1 gene expression correlates with a rapid and transient wave of ISG expression across all cell types, which may precede PCR detection of replicative infection. Another ISG, IFI27, shows a delayed but sustained response restricted to myeloid cells, attributable to gene and cell-specific epigenetic regulation. These findings are reproducible in experimental and naturally acquired infections with influenza, respiratory syncytial virus and rhinovirus. Blood MX1 expression is superior to IFI27 expression for diagnosis of early infection, as a correlate of viral load and for discrimination of virus culture positivity. Therefore, MX1 expression offers potential to stratify patients for antiviral therapy or infection control interventions. Blood IFI27 expression is superior to MX1 expression for diagnostic accuracy across the time course of symptomatic infection and thereby, offers higher diagnostic yield for respiratory virus infections that incur a delay between transmission and testing.
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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 | 0.000 |
| 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 | 0.001 |
| 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".