Increased degranulation of natural killer cells during acute HCV correlates with the magnitude of virus-specific T cell responses
Bibliographic record
Abstract
BACKGROUND & AIMS: Natural killer (NK) cells provide early defense against viral infections by killing infected cells and producing cytokines that inhibit viral replication. NK cells also interact with dendritic cells (DCs) and this reciprocal interaction regulates both innate and adaptive immunity. Genetic studies have suggested that NK cell activity is a determinant of HCV infectious outcome but a functional correlation has not been established. We hypothesized that increased NK cell activity during acute HCV infection correlates with spontaneous viral clearance. METHODS: We used multiparametric flow cytometry to monitor longitudinally the phenotype and the activity of NK cells in a cohort of intravenous drug users following HCV exposure. Three groups were studied: acute HCV with chronic evolution (n = 13), acute resolving HCV (n = 11), and exposed un-infected individuals (n = 10). We examined the expression of several NK cell-activating and -inhibiting receptors, IFN-γ production and CD107a degranulation upon stimulation, and the kinetics of NK cell responses relative to T cell responses. RESULTS: We observed decreased expression of the inhibitory NKG2A receptor in NK cells following spontaneous HCV clearance. In addition, we observed increased NK cell degranulation during acute HCV irrespective of infectious outcome. NK cell peak responses preceded or coincided with peak T cell responses. Furthermore, NK cell degranulation correlated with the magnitude of HCV-specific T cells. CONCLUSIONS: Our results demonstrate that NK cells are activated during acute HCV regardless of infection outcome and may play an indirect role through induction and priming of T cell responses.
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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".