The Influence of Hepatitis C Viral Loads on Natural Killer Cell Function
Bibliographic record
Abstract
Background: Hepatitis C virus (HCV) infection has a high rate of chronicity, attributable to its capacity to alter host immunity, including natural killer (NK) cell function. In this study, the interaction between NK cell activity and HCV viral load was investigated. Methods: Peripheral blood NK cells were examined for cytotoxicity and interferon (IFN)-g amma expression in HCV infected low (LVL, < 800,000 IU/mL, n = 10) and high (HVL, > 800,000 IU/mL, n = 13) viral load patient cohorts. Results: Spontaneous NK cell cytotoxicity was more robust in the LVL cohort resulting in a negative correlation with viral loads (spontaneous, r = -0.437, P = 0.037; IFN-α activated, r = -0.372, P = 0.081). Although the percent of IFN-alpha+ NK cells did not associate with viral load, within the LVL cohort there was a marked increase in IFN-g amma + NK cells upon IFN-alpha± activation relative to medium alone (P < 0.01). To examine the inability of NK cells derived from HVL patients to be further activated, the expression of the exhaustion marker programmed cell death protein (PD)-1 was evaluated. PD-1 expression upon NK cell activation correlated with viral load (r = 0.649, P = 0.009). In addition, HCV proteins upregulated PD-1 expression in vitro (P < 0.05), suggesting that HCV can directly promote NK cell exhaustion. Cells from HVL patients were also more likely to produce IFN-g amma in response to HCV core protein. The finding that NK cell PD-1 and IFN-g amma expression are linked (r = 0.542, P < 0.05) suggests that increased IFN-g amma levels may induce PD-1 as a negative feedback mechanism. Conclusions: High HCV loads appear to promote NK exhaustion in chronic HCV infection. Gastroenterol Res. 2019;12(1):8-15 doi: https://doi.org/10.14740/gr1081w
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".