1.Association between HBV infection and HLA-DPB1 gene in population of Guangzhou Chinese
Zehuan LIU ; Xinlan FAN ; Jianghai LIN ; Zhiyan FU ; Dejing PAN ; Yonggui FU ; Zongjian JIA ; Anlong XU
Chinese Journal of Pathophysiology 2000;0(08):-
AIM: To investigate the association between HBV infection and HLA-DPB1 gene in population of Guangzhou Chinese. METHODS: 58 unrelated patients (test positive of HbsAg,HBeAg,HbcAb) and 75 unrelated healthy control individuals were typed by sequencing based typing (SBT) method in their HLA-DPB1 gene. RESULTS: The phenotype frequencies of HLA-DPB1 alleles of patients and control have no significant difference. CONCLUSION: These results indicate that there is no association between HLA-DPB1 gene and HBV infection.
2.Research progress on correlation between circadian rhythm disturbance and work-related musculoskeletal disorders
Lichong LAI ; Pinyue TAO ; Dejing FAN ; Shuyu LU ; Jie PENG ; Huiqiao HUANG
Journal of Environmental and Occupational Medicine 2025;42(3):319-324
Circadian rhythm refers to the 24-hour periodic changes in behavior, physiology, and molecular processes in the human body. Disruptions to the circadian rhythm not only affect mental health but are also associated with various metabolic disorders, including the regulation of bone and muscle metabolism. Research has shown that work-related musculoskeletal disorders (WMSDs) are influenced not only by workload but also by circadian rhythm factors, such as shift work. This review examined the relationships between circadian rhythm-related antecedents, outcomes, and WMSDs, exploring their shared metabolic markers and mechanisms. It provided a systematic overview of the intrinsic connection between circadian rhythm disruptions and WMSDs. While current studies highlight the impact of circadian rhythm disturbances on musculoskeletal disorders, further investigation is required to address the confounding factors involved. Future research should integrate chronobiology with both subjective and objective data to explore the pathway from environmental factors to intermediate phenotypes to diseases, ultimately providing a more comprehensive understanding of the network mechanisms underlying WMSDs.