Preactivation of the interferon signalling in liver is correlated with nonresponse to interferon alpha therapy in patients chronically infected with hepatitis B virus
Bibliographic record
Abstract
Interferon alpha (IFN-α) therapy is widely used to treat patients with chronic hepatitis B (CHB) but the sustained response rate is low, and the molecular mechanisms for the ineffectiveness of IFN-α treatments are not known. We screened differentially expressed genes between responders (Rs) and nonresponders (NRs) in patients with CHB treated with IFN-α to explore the molecular basis for treatment failure. Expression profiling was performed on percutaneous needle liver biopsy specimens taken before therapy. Gene expression levels were compared between seven patients who did not respond to therapy (NR) and six who did respond (R). Gene ontology category and KEGG pathway were analysed for differentially expressed genes, and the selected differentially expressed genes were confirmed using real-time polymerase chain reaction. We identified 3592 genes whose expression levels differed significantly between all Rs and NRs (P < 0.05); many of these genes are IFN-stimulated genes (ISGs) and immune-related genes. The ISGs were more highly expressed, while immune-related genes were inhibited in NRs before IFN-α treatment. Two ISGs (CEB1 and USP18) that are linked in an IFN inhibitory pathway are highly expressed in NRs, and a potential antiviral gene ISG20 was inhibited in NRs, suggesting a possible rationale for treatment nonresponse. Patients who do or do not respond to IFN have different liver gene expression profiles before IFN-α treatment. Preactivation of the IFN signalling pathway leading to the increased expression of inhibitory ISGs and inhibition of immune response in the pretreatment livers was associated with treatment failure.
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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) | 0.001 | 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".