Natural killer inhibitory receptor expression associated with treatment failure and interleukin-28B genotype in patients with chronic hepatitis C
Bibliographic record
Abstract
UNLABELLED: Natural killer (NK) cells constitute a first line of defense against viral infections; their function is governed by the integration of signals from multiple activating and inhibitory surface receptors. We hypothesized that because NKs become rapidly activated by cytokines, response to anti-hepatitis C virus (HCV) therapy would be predicted by the phenotype and function of NKs. We used a cohort of 101 patients (55 African, 46 Caucasian-American) who received pegylated-interferon (IFN) and ribavirin for 48 weeks. Multiparameter FACS analysis was used to examine relative expression of 14 different inhibitory/activating receptors. Interleukin (IL)-28B genotyping (rs12979860) was also performed. Pretreatment levels of inhibitory receptors CD158a, CD158b, and CD158e were higher in patients who demonstrated poor viral decline within the first 28 days of therapy. Higher expression levels of inhibitory receptors NKG2A, CD158b, and CD158e were demonstrable in patients who failed to achieve sustained virologic response (SVR). Patients carrying the IL-28B T allele had higher NKG2A expression on effector NKs. We created a mathematical regression model incorporating race, viral level, and two inhibitory receptors. The area-under-the curve was 0.88, which is highly predictive of SVR. Moreover, the model performed complementarily with IL-28B across the CC, CT, and TT genotypes. Purified NKG2A(neg) NKs treated with pegylated-IFN-α for 4 hours demonstrated higher levels of IFN-γ-inducible protein-10 (IP-10) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) compared with their NKG2A(pos) counterparts. CONCLUSIONS: These results provide novel insights into the associations of NK phenotype with IL-28B genotype and gene expression patterns, as well as the role of NKs in mediating IFN-induced viral clearance of chronic HCV infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".