IFN‐λ3 polymorphism indirectly influences NK cell phenotype and function during acute HCV infection
Bibliographic record
Abstract
INTRODUCTION: Polymorphisms in the type III interferon IFN-λ3 and the killer cell immunoglobulin-like receptor (KIR) genes controlling the activity of natural killer (NK) cells can predict spontaneous resolution of acute hepatitis C virus (HCV) infection. We hypothesized that IFN-λ3 polymorphism may modulate NK cell function during acute HCV. METHODS: We monitored the plasma levels of type III IFNs in relation to the phenotype and the function of NK cells in a cohort of people who inject drugs (PWID) during acute HCV infection with different outcomes. RESULTS: Early acute HCV was associated with high variability in type III IFNs plasma levels and the favorable IFN-λ3 CC genotype was associated with higher viral loads. Reduced expression of Natural Killer Group Protein 2A (NKG2A) was associated with lower IFN-λ3 plasma levels and the CC genotype. IFN-γ production by NK cells was higher in individuals with the CC genotype during acute infection but this did not prevent viral persistence. IFN-λ3 plasma levels did not correlate with function of NK cells and IFN-λ3 prestimulation did not affect NK cell activation and function. CONCLUSIONS: These results suggest that IFN-λ3 polymorphism indirectly influences NK cell phenotype and function during acute HCV but other factors may act in concert to determine the outcome of the 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) | 0.000 | 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".