Inhibitory receptor molecules in chronic hepatitis B and C infections: novel targets for immunotherapy?
Bibliographic record
Abstract
Chronic HBV and HCV infections are the leading cause of liver-related morbidity and mortality. For effective antiviral immunity, virus-specific T cells are required, but these cells have been shown to be weak or absent in chronic HBV and HCV patients. One of the mechanisms that underlies the impaired T-cell response is the result of the continuously high viral load that causes HBV-specific and HCV-specific T cells to become exhausted, which is characterized by impaired proliferation, cytokine production and cytotoxic activity of T cells as well as high susceptibility to apoptosis. In vitro studies from chronic HBV and HCV patients as well as in vivo studies in animal models demonstrated a reversible state of T-cell exhaustion, which can be manipulated to reinvigorate the specific antiviral immune responses. In chronic HCV infection, this concept has been explored in clinical trials by administration of specific antibody to block the inhibitory pathways. The manipulation of inhibitory receptors is a promising and potential strategy for immunotherapeutic interventions in chronic HBV and HCV patients to facilitate complete elimination of the viruses or sustained viral control.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".