Role of NK and NKT cells in the immunopathogenesis of HCV-induced hepatitis
Bibliographic record
Abstract
Natural killer (NK) cells constitute the first line of host defense against invading pathogens. They usually become activated in an early phase of a viral infection. Liver is particularly enriched in NK cells, which are activated by hepatotropic viruses such as hepatitis C virus (HCV). The activated NK cells play an essential role in recruiting virus-specific T cells and in inducing antiviral immunity in liver. They also eliminate virus-infected hepatocytes directly by cytolytic mechanisms and indirectly by secreting cytokines, which induce an antiviral state in host cells. Therefore, optimally activated NK cells are important in limiting viral replication in this organ. This notion is supported by the observations that interferon treatment is effective in HCV-infected persons in whom it increases NK cell activity. Not surprisingly, HCV has evolved multiple strategies to counter host's NK cell response. Compromised NK cell functions have been reported in chronic HCV-infected individuals. It is ironic that activated NK cells may also contribute toward liver injury. Further studies are needed to understand the role of these cells in host defense and in liver pathology in HCV infections. Recent advances in understanding NK cell biology have opened new avenues for boosting innate and adaptive antiviral immune responses in HCV-infected individuals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".