NK cell Activation during Acute Hepatitis C Virus Infection (45.18)
Bibliographic record
Abstract
Abstract Genetic studies have implicated the inhibitory natural killer (NK) cell receptor KIR2DL3 and its human leukocyte antigen C group 1 (HLA-C1) ligand in resolution of hepatitis C virus (HCV) infection but a functional correlation was not established. To examine the role of NK cells during acute HCV, we have analyzed longitudinally the phenotype and function of NK cells from a cohort of patients following HCV exposure. Three groups with different infectious outcome were identified: spontaneous resolvers (SR), chronic evolution (C) and exposed un-infected (EU) (n=10 in each group). We have observed increased NK cell cytotoxicity (%CD107a+) in all groups suggesting NK cell activation following exposure but no correlation was established with infectious outcome. Nevertheless, we observed decreased expression of KIR2DL1, associated with highest NK cell inhibition, in SR. Furthermore, the highest IFN-γ production was observed in KIR2DL3+ NK cells, the least inhibited population, in all groups. Our results suggest that NK cell direct cytotoxicity might not be implicated in HCV clearance but that less NK cell inhibition is more frequent in SR patients. These results confirm the important role of host genetic background in NK cell function and provides the first demonstration of such a functional correlation in an in vivo infection setting. Research support: The Dana Foundation, CIHR, FRSQ, NCRTP-HepC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".