Impact of passive smoking, cooking with solid fuel exposure, and MBL/MASP-2 gene polymorphism upon susceptibility to tuberculosis
Bibliographic record
Abstract
BACKGROUND: To explore the impact of passive smoking, cooking with solid fuel, mannose-binding lectin (MBL) gene, MBL-associated serine proteases 2 (MASP-2) gene, and gene-environment interactions on the susceptibility to tuberculosis (TB) in non-smokers. METHODS: A total of 205 TB patients and 216 healthy controls were recruited to participate in this case-control study. PCR with sequence-specific primers (PCR-SSP) technology was leveraged to genotype rs7096206 of MBL genes and rs2273346 and rs6695096 of MASP-2 genes. Demographic data and information on exposures of participants were collected. Unconditioned logistic regression analysis was conducted to identify associations between the various factors and TB, and marginal structural linear odds models were used to estimate the interactions. RESULTS: Passive smoking and cooking with solid fuel were associated with the risk of TB, with odds ratios (OR) of 1.58 and 2.93, respectively (p<0.05). Genotype CG at rs7096206 of MBL genes (OR 2.02) and genotype TC at rs6695096 of MASP-2 genes (OR 1.67) were more prevalent in the TB patients than in healthy controls (p<0.05). The relative excess risk of interaction (RERI) between rs7096206 of MBL genes and passive smoking or cooking with solid fuel exposure was 1.86 (95% confidence interval (CI) 0.59-3.16) and 2.66 (95% CI 1.85-3.47), respectively. The RERI between rs6695096 of MASP-2 genes and cooking with solid fuel exposure was 3.70 (95% CI 2.63-4.78), which was also a positive interaction. However, the RERI between rs6695096 of MASP-2 genes and passive smoking was not statistically significant. CONCLUSIONS: Passive smoking, cooking with solid fuel, and polymorphisms of MBL (rs7096206) and MASP-2 (rs6695096) genes were associated with susceptibility to TB in non-smokers, and there were gene-environment interactions among them. Further studies are needed to explore details of the mechanisms of association.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".