Cytokines induced during chronic hepatitis B virus infection promote a pathway for NK cell–mediated liver damage
Bibliographic record
Abstract
Hepatitis B virus (HBV) causes chronic infection in more than 350 million people worldwide. It replicates in hepatocytes but is non-cytopathic; liver damage is thought to be immune mediated. Here, we investigated the role of innate immune responses in mediating liver damage in patients with chronic HBV infection. Longitudinal analysis revealed a temporal correlation between flares of liver inflammation and fluctuations in interleukin (IL)-8, interferon (IFN)-alpha, and natural killer (NK) cell expression of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) directly ex vivo. A cross-sectional study confirmed these findings in patients with HBV-related liver inflammation compared with healthy carriers. Activated, TRAIL-expressing NK cells were further enriched in the liver of patients with chronic HBV infection, while their hepatocytes expressed increased levels of a TRAIL death-inducing receptor. IFN-alpha concentrations found in patients were capable of activating NK cells to induce TRAIL-mediated hepatocyte apoptosis in vitro. The pathogenic potential of this pathway could be further enhanced by the ability of the IFN-alpha/IL-8 combination to dysregulate the balance of death-inducing and regulatory TRAIL receptors expressed on hepatocytes. We conclude that NK cells may contribute to liver inflammation by TRAIL-mediated death of hepatocytes and demonstrate that this non-antigen-specific mechanism can be switched on by cytokines produced during active HBV infection.
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 | 0.001 |
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".