Interferon-λ treatment accelerates SARS-CoV-2 clearance despite age-related delays in the induction of T cell immunity
Bibliographic record
Abstract
Interferons induced early after SARS-CoV-2 infection are crucial for shaping immunity and preventing severe COVID-19. We previously demonstrated that injection of pegylated interferon-lambda accelerated viral clearance in COVID-19 patients (NCT04354259). To determine if the viral decline is mediated by enhanced immunity, we assess in vivo responses to interferon-lambda by single cell RNA sequencing and measure SARS-CoV-2-specific T cell and antibody responses between placebo and interferon-lambda-treated patients. Here we show that interferon-lambda treatment induces interferon stimulated genes in peripheral immune cells expressing IFNLR1, including plasmacytoid dendritic cells and B cells. Interferon-lambda does not affect SARS-CoV-2-specific antibody levels or the magnitude of virus-specific T cells. However, we identify delayed T cell responses in older adults, suggesting that interferon-lambda can overcome delays in adaptive immunity to accelerate viral clearance in high-risk patients. Altogether, interferon-lambda offers an early COVID-19 treatment option for outpatients to boost innate antiviral defenses without dampening peripheral 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".