NK cells lacking FcεRIγ are associated with reduced liver damage in chronic hepatitis C virus infection
Bibliographic record
Abstract
A novel subset of human natural killer (NK) cells, which displays potent and broad antiviral responsiveness in concert with virus-specific antibodies, was recently uncovered in cytomegalovirus (CMV)+ individuals. This NK-cell subset (g-NK) was characterized by a deficiency in the expression of FcεRIγ adaptor protein and the long-lasting memory-like NK-cell phenotype, suggesting a role in chronic infections. This study investigates whether the g-NK-cell subset is associated with the magnitude of liver disease during chronic hepatitis C virus (HCV) infection. Analysis of g-NK-cell proportions and function in the PBMCs of healthy controls and chronic HCV subjects showed that chronic HCV subjects had slightly lower proportions of the g-NK-cell subset having similarly enhanced antibody-dependent cellular cytotoxicity responses compared to conventional NK cells. Notably, among CMV+ chronic HCV patients, lower levels of liver enzymes and fibrosis were found in those possessing g-NK cells. g-NK cells were predominant among the CD56(neg) NK cell population often found in chronic HCV patients, suggesting their involvement in immune response during HCV infection. For the first time, our findings indicate that the presence of the g-NK cells in CMV+ individuals is associated with amelioration of liver disease in chronic HCV infection, suggesting the beneficial roles of g-NK cells during a chronic 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".