The associations between Toll-like receptor 4 gene polymorphisms and hepatitis C virus infection: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background and objective: The hepatitis C virus (HCV) is able to cause a life-threatening disease relating to lethal hepatocellular carcinoma. Previous, Toll-like receptor polymorphisms were proposed as promising biomarker for HCV-related hepatocellular carcinoma and disease progression. This study aimed to summarize the association of TLR4 polymorphisms and HCV infection through meta-analysis. Methods: We applied a systematic review and meta-analysis performed by using PubMed, EMBASE and Web of Science searches. The Modified Newcastle-Ottawa scale was used for quality assessment. The odd-ratio (OR) and 95% confidence interval (CI) were calculated to assess the association. In silico analysis was applied for proposing the function as microRNA (miRNA) of non-coding polymorphism. Finally, the miRNA target was predicted and annotated to suggest the possible relationship between polymorphism and HCV infection. Results: Our meta-analysis incorporated seven studies involving rs4986791, rs4986790 and rs2149356. No association exists between rs4986791 and HCV infection. However, the heterozygous model (AG vs GG) of rs4986790 significantly associates with HCV infection (OR = 0.33, 95% CI = 0.21–0.49, P<0.0001). Moreover, the rs2149356 TG genotype also associates with HCV infection in the over-dominant model (TG vs TT+TG: OR = 0.54, 95% CI = 0.40–0.75). In silico analysis of rs2149356G allele showed that this mutation is siRNA, which targets the set of genes, especially in the autophagy pathway. Conclusion: We demonstrated that rs4986790 and rs2149356 are associated with HCV 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".