<I>CYP7A1</I>, <I>BAAT</I> and <I>UGT1A1</I> polymorphisms and susceptibility to anti-tuberculosis drug-induced hepatotoxicity
Bibliographic record
Abstract
SETTING: Evidence indicates that the polymorphisms in genes involved in bile acid metabolism may play an important role in the development of anti-tuberculosis drug-induced hepatotoxicity (ATDH) in tuberculosis (TB) patients treated with anti-tuberculosis drugs. OBJECTIVE: To investigate the association between genetic variants of CYP7A1, BAAT and UGT1A1 and the risk of ATDH in a Chinese cohort. DESIGN: In this nested case-control study, 89 TB patients with ATDH and 356 matched ATDH-free TB patients constituted cases and controls, respectively. Genetic polymorphisms of CYP7A1, BAAT and UGT1A1 were determined using the TaqMan single-nucleotide polymorphism genotyping assay. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated using a conditional logistic regression model. RESULTS: Significant differences were found in genotype distributions of rs1457043 in CYP7A1 between patients with and those without ATDH (P = 0.014). Genotype and haplotype analysis showed that patients carrying an AG genotype of rs1457043 and G-C or G-A haplotypes of rs1457043-rs8192870 in CYP7A1 were at a higher risk of ATDH than those with GG genotype and A-C haplotype, with ORs of respectively 2.05 (95%CI 1.18-3.15) and 2.40 (95%CI 1.62-3.57). CONCLUSION: Genetic polymorphisms of CYP7A1 may be associated with susceptibility to ATDH in the Chinese population.
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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".