Does your job really matter? Job-specific cancer incidence among a cohort coal mine workers in Queensland, Australia - Deborah Catherine Glass; Stella May Gwini; Anthony Del Monaco; Lin Fritschi; Michael J. Abramson; Malcolm Ross Sim; Karen Walker-Bone (International Archives of Occupational and Environmental Health, 2026)
Purpose Globally, coal production exposes millions of workers to coal mine dust. This study aimed to measure overall and site specific cancer incidence among coal mine workers (CMW) who performed different types of work. Methods A retrospective cohort of CMW in Queensland, Australia, was assembled using health assessment records from 1992. CMW were grouped by job type and then into Work Categories and linked to the Australian Cancer Database up to 2016. Standardized cancer incidence ratios (SIR) and relative cancer incidence ratios (aRIR) within the cohort, adjusted for age, era, and smoking status, were calculated for men and women with Poisson regression. Results There were 5,568 cancers diagnosed among 146,553 men and 396 in 19,927 women. The overall cancer risk was comparable to that of the general population for most Work Categories. The risk of lung cancer was higher for male Production (SIR 123, 95% Confidence Interval [CI] 107–142; adjusted Relative incidence Ratio (aRIR) (adjusted for era, age and smoking status) 1.23, 95%CI 0.99-1.52) and Construction workers (SIR 189, 95%CI 31–272; aRIR 1.78, 95%CI 1.20–2.62) when compared with the Australian population and within the cohort after adjusting for smoking. Laryngeal cancer was increased in Production workers (SIR 145, 95%CI 100–212). Conclusions Increased rates of lung and laryngeal cancers were identified for male Production workers and lung cancer for Construction workers. These could be related to mine workplace exposures such as silica and diesel engine exhaust.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.