Pegylated-interferon-λ treatment-induced peripheral interferon stimulated genes are associated with SARS-CoV-2 viral load decline despite delayed T cell response in older individuals
Bibliographic record
Abstract
Abstract Interferons (IFNs) are antiviral cytokines induced very early after SARS-CoV-2 infection and are crucial for viral clearance, shaping immunity, and preventing the development of severe COVID-19. We previously demonstrated that a single injection of peginterferon-lambda1 (PEG-IFN-λ) accelerated viral clearance in COVID-19 patients. To determine if the rapid viral decline was mediated by enhanced immunity, we assessed in vivo responses to PEG-IFN-λ by single cell RNA sequencing and measured SARS-CoV-2-specific T cell and antibody responses between placebo and PEG-IFN-λ-treated patients. PEG-IFN-λ treatment induced interferon stimulated genes in peripheral immune cells expressing IFNLR1 , with plasmacytoid dendritic cells having the greatest response, followed by B cells. PEG-IFN-λ did not significantly affect SARS-CoV-2-specific antibody levels in plasma or the magnitude or functionality of virus-specific T cells. However, we identified a delayed T cell response in older adults, suggesting that PEG-IFN-λ can overcome the delay in adaptive immunity to accelerate viral clearance in patients most at risk for severe disease. Taken together, PEG-IFN-λ offers an early COVID-19 treatment option for outpatients to boost innate antiviral defenses without dampening peripheral SARS-CoV-2 adaptive immunity
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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 | 0.000 |
| 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".