Reduced IFNL1 and/or IFNL2, but not IFNL3 is associated with worse outcome in patients with COVID-19
Bibliographic record
Abstract
The recent pandemic was caused by the emergence of a new human pathogen, SARS-CoV-2. While the rapid development of many vaccines provided an end to the immediate crisis, there remains an urgent need to understand more about this new virus and what constitutes a beneficial immune response in terms of successful resolution of infection. Indeed, this is key for development of vaccines that provide long lasting protective immunity. The interferon lambda (IFNL) family of cytokines are produced early in response to infection and are generally considered anti-viral and beneficial. However, data regarding production of IFNL cytokines in coronavirus disease 2019 (COVID-19) patients is highly variable, and generally from underpowered studies. In this study, we measured all three IFNL1, IFNL2, and IFNL3 cytokines in plasma from a well characterized, large COVID-19 cohort (n = 399) that included good representation from patients with a more indolent disease progression, and hence a beneficial immune response. While all three cytokines were produced, they differed in both the frequency of expression in patients, and the levels produced. IFNL3 was produced in almost all patients but neither protein level nor IFNL3/IFNL4 single nucleotide polymorphisms were associated with clinical outcome. In contrast, both IFNL1 and IFNL2 levels were significantly lower, or absent, in plasma of patients that had a more severe disease outcome. These data are consistent with the concept that early IFNL1 and IFNL2 cytokine production is protective against SARS-CoV-2 infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".