Polymorphism in tumor necrosis factor-related apoptosis-inducing ligand receptor 1 is associated with poor viral response to interferon-based hepatitis C virus therapy in HIV/hepatitis C virus-coinfected individuals
Bibliographic record
Abstract
OBJECTIVE(S): HIV/hepatitis C virus (HCV) coinfection causes accelerated liver disease compared to HCV monoinfection, and only 30-60% of HIV/HCV-coinfected individuals respond to HCV therapy with pegylated interferon and ribavirin. There are currently no biomarkers that predict treatment response in these coinfected patients. DESIGN: We investigated whether there is an association between HCV treatment response and SNPs of apoptosis-related genes during HIV/HCV coinfection. METHOD: Genomic DNA from 53 HIV/HCV-coinfected individuals was analyzed for 82 SNPs of 10 apoptosis-related genes. RESULTS: We found that the presence of the rs4242392 SNP in tumor necrosis factor receptor superfamily, member 10a (TNFRSF10A), which encodes for tumor necrosis factor-related apoptosis-inducing ligand receptor 1, predicts poor outcome to HCV therapy, in HIV/HCV-co-infected patients [odds ratio 5.91 (95% confidence interval 1.63-21.38, P = 0.007)]. CONCLUSION: The rs4242392 SNP of the tumor necrosis factor-related apoptosis-inducing ligand receptor 1 gene predicted poor interferon-based HCV treatment response in HIV/HCV-coinfected patients.
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 | 0.002 |
| 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".