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ObjectivesThe aim of this study was to investigate factors associated with heart rate variability in firefighters working in a metropolitan city in South Korea.MethodsSelf-administered questionnaires including Korean Occupational Stress Scale (KOSS) as well as surveys collecting socio-demographic characteristics and work-related factors were given to 962 firefighters. After exclusion for missing data, 645 firefighters were included, and analysis of covaiance adjusted for the general risk factors and job characteristics were used to assess the relationship between heart rate variability and associated factors.ResultsSDNN and RMSSD and were decreased in the area of occupational climate of the group with high job stress (p = 0.027, p = 0.036). HF(ln) was decreased in the area of organizational system and occupational climate of the group with high stress that statistically significant level (p = 0.034, p = 0.043).ConclusionsOccupational climate and organizational system are associated with reduction of heart rate variability. Preventive medical care plans for cardiovascular disease of firefighters through the analysis and evaluation of job stress factors are needed.

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Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer’s disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLCγ1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLCγ1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLCγ1 gene, and one of these completely matched with an SNV in exon 27 of PLCγ1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLCγ1 gene is associated with abnormal splicing during messenger RNA maturation. Taken together, our findings suggest that this approach, which combines in silico and deep learning-based analyses, has potential for identifying the clinical utility of critical SNVs in AD prediction.