Inflammasome Genes’ Polymorphisms in Egyptian Chronic Hepatitis C Patients: Influence on Vulnerability to Infection and Response to Treatment
Bibliographic record
Abstract
Chronic inflammation is a pivotal contributor to the liver damage mediated by hepatitis C virus (HCV). The NOD-like receptor, pyrin domain-containing 3 (NLRP3) inflammasome is activated by HCV in both hepatocytes and Kupffer cells. The aim of our study was to investigate the association of nine single-nucleotide polymorphisms in four inflammasome genes (NLRP3, CARD8, IL-1β, and IL-18) with the susceptibility to HCV infection and outcome of interferon treatment in 201 Egyptian chronic hepatitis C patients and 95 healthy controls. The genotyping was conducted using TaqMan predesigned SNP assay. In the comparative analysis, the CC genotype of the NLRP3 rs1539019 was found to be associated with the lower risk to chronic HCV infection (OR: 0.33, 95% CI: 0.17-0.62). This association was also found for the CA genotype and the A allele of the NLRP3 rs35829419 (OR: 0.18 and 0.22, respectively), in addition to the GG genotype and G allele of IL-18 rs1946518 (OR: 0.55 and 0.61, respectively). In contrast, the AA genotype of the IL-1βrs1143629 was significantly more frequent in HCV patients (OR: 1.7, 95% CI: 1-2.86). Notably, the frequency of the AA genotype of NLRP3 rs1539019 was significantly higher in patients with lack of response (NR) to the interferon treatment (OR: 1.95, 95% CI: 1-3.7). A similar association was found for both the CC genotype and C allele of the NLRP3 rs35829419 (OR: 2.78 and 2.73, respectively) and for the TT genotype and T allele of CARD8 rs2043211 (OR: 2.64 and 1.54, respectively). Yet, the IL-1β(rs1143629, rs1143634) and IL-18 (rs187238, rs1946518) polymorphisms did not show any significant association with response to interferon treatment. In conclusion, this study reports, for the first time, the association of genetic variations in NLRP3 with hepatitis C susceptibility and response to treatment in Egyptian patients. However, further large-scale studies are recommended to confirm our findings.
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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 | 0.001 |
| 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".