Intrahepatic natural killer cell activation, but not function, is associated with <scp>HB</scp>sAg levels in patients with <scp>HB</scp>eAg‐negative chronic hepatitis B
Bibliographic record
Abstract
BACKGROUND & AIMS: Natural killer (NK) cells play an important role in the immune response to viruses. As the hepatitis B virus (HBV) replicates in hepatocytes, examination of the liver of chronic hepatitis B (CHB) patients is crucial to better understand the role of NK cells in HBV. HBeAg-negative CHB differs in many aspects from HBeAg-positive CHB, and until now little is known about the intrahepatic NK cell response in HBeAg-negative patients. Intrahepatic immune control might be different in HBeAg-negative as compared with HBeAg-positive patients. METHODS: Liver NK cells were investigated in 21 HBeAg-positive and 35 HBeAg-negative CHB patients. Biopsy specimens were processed for routine histopathology and staging according to Ishak scores. Intrahepatic and blood NK cell frequencies, activation status and function of NK cells were analysed by flow cytometry. RESULTS: In HBeAg-negative CHB patients, compared to blood, liver NK cells displayed a more activated phenotype and stimulation further increased the activation status, but production of IFN-γ was markedly less. There was no difference with HBeAg-positive CHB. Only in HBeAg-negative CHB, but not in HBeAg-positive CHB, NK cell activation was inversely correlated with HBsAg levels. CONCLUSIONS: The present study indicates that liver NK cells of CHB have a higher activation status compared to blood. However, they are not capable to increase cytokine production above levels reached by activated blood NK cells. In HBeAg-negative CHB, the levels of HBsAg may contribute to the incapacity of activated liver NK cells to increase cytokine production.
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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 | 0.000 | 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".