Analysis of IL-6, STAT3 and HSPA1L Gene Polymorphisms in Anti-Tuberculosis Drug-Induced Hepatitis in a Nested Case-Control Study
Bibliographic record
Abstract
OBJECTIVES: To investigate the association of IL-6, STAT3 and HSPA1L polymorphisms with the risk of anti-tuberculosis drug-induced hepatitis (ATDH) in Chinese Han population. METHODS: The study was designed as a nested case-control study within a prospective cohort. Each case was matched with four controls by sex, age at baseline (±5 years), treatment history, disease severity, drug dosage and place of sample collection. Genetic polymorphisms of IL-6, STAT3 and HSPA1L were determined blindly by TaqMan single-nucleotide polymorphism (SNP) genotyping assay. Odds ratio (OR) with 95% confidence intervals (CIs) was estimated by conditional logistic regression model to measure the association between selected SNPs and the risk of ATDH. RESULTS: A total of 89 incident ATDH cases and 356 ATDH-free controls were genotyped for IL-6 (rs2066992, rs2069837, rs1524107), STAT3 (rs1053004, rs1053023, rs1053005) and HSPA1L (rs2227956). In genotype analysis, no significant difference was observed in genotypes frequencies of the seven selected SNPs between case and control group after Bonferroni correction. In haplotype analysis, carriers with STAT3 GAT and AGC (rs1053023-rs1053005-rs1053004) haplotypes had a significantly higher risk of ATDH compared with wild-type haplotype (P<0.0001). CONCLUSION: This study suggested that genetic variants of STAT3 might contribute to ATDH susceptibility in Chinese Han population. Studies in larger, varied populations are required to confirm these 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".