Objective:To analyze the depressive symptoms of physical and mental workers and the impact of their behavior and lifestyle on their depressive symptoms.Methods:In August 2022, a cross-sectional study design was adopted to select 553 workers as research subjects using cluster sampling. General demographic information, health-related behaviors and lifestyles were collected, and their depressive symptoms were evaluated using the Chinese version of the Patient Health Questionnaire 9 (PHQ-9). The differences in general demographic information, health-related behaviors and lifestyles between physical and mental workers were analyzed, and logistic regression was used to analyze the influencing factors of depressive symptoms in occupational populations.Results:Among the 553 subjects, 317 were physical workers (57.32%) and 236 were mental workers (42.68%). Statistically significant differences were observed between the two groups in terms of education level, monthly income, smoking rate, regular physical exercise rate and appropriate sleep time rate ( P<0.05). The score and the detection rate of depression symptoms among physical workers were (9.67±2.75) points and 20.82% (66/317), respectively, which were higher than those of mental workers [(8.34±2.18) points, 12.71% (30/236) ] ( t=6.13, χ 2=6.20, P<0.05). The results of logistic regression analysis showed that smoking, regular physical exercise, appropriate sleep time and mental work were influencing factors of depressive symptoms among the occupational population ( OR=1.592, 95% CI: 1.316-1.825; OR=0.659, 95% CI: 0.416-0.830; OR=0.502, 95% CI: 0.257-0.717; OR=0.839, 95% CI: 0.522-0.967; P<0.05) . Conclusion:The depressive symptoms of physical workers are more serious than those of mental workers. Low education level, not frequently participating in physical exercise, smoking and sleep disorder are potential risk factors that affect the depressive symptoms of the occupational population.

Objective:To analyze the depressive symptoms of physical and mental workers and the impact of their behavior and lifestyle on their depressive symptoms.Methods:In August 2022, a cross-sectional study design was adopted to select 553 workers as research subjects using cluster sampling. General demographic information, health-related behaviors and lifestyles were collected, and their depressive symptoms were evaluated using the Chinese version of the Patient Health Questionnaire 9 (PHQ-9). The differences in general demographic information, health-related behaviors and lifestyles between physical and mental workers were analyzed, and logistic regression was used to analyze the influencing factors of depressive symptoms in occupational populations.Results:Among the 553 subjects, 317 were physical workers (57.32%) and 236 were mental workers (42.68%). Statistically significant differences were observed between the two groups in terms of education level, monthly income, smoking rate, regular physical exercise rate and appropriate sleep time rate ( P<0.05). The score and the detection rate of depression symptoms among physical workers were (9.67±2.75) points and 20.82% (66/317), respectively, which were higher than those of mental workers [(8.34±2.18) points, 12.71% (30/236) ] ( t=6.13, χ 2=6.20, P<0.05). The results of logistic regression analysis showed that smoking, regular physical exercise, appropriate sleep time and mental work were influencing factors of depressive symptoms among the occupational population ( OR=1.592, 95% CI: 1.316-1.825; OR=0.659, 95% CI: 0.416-0.830; OR=0.502, 95% CI: 0.257-0.717; OR=0.839, 95% CI: 0.522-0.967; P<0.05) . Conclusion:The depressive symptoms of physical workers are more serious than those of mental workers. Low education level, not frequently participating in physical exercise, smoking and sleep disorder are potential risk factors that affect the depressive symptoms of the occupational population.

cFos is one of the most widely-studied genes in the field of neuroscience. Currently, there is no systematic database focusing on cFos in neuroscience. We developed a curated database-cFos-ANAB-a cFos-based web tool for exploring activated neurons and associated behaviors in rats and mice, comprising 398 brain nuclei and sub-nuclei, and five associated behaviors: pain, fear, feeding, aggression, and sexual behavior. Direct relationships among behaviors and nuclei (even cell types) under specific stimulating conditions were constructed based on cFos expression profiles extracted from original publications. Moreover, overlapping nuclei and sub-nuclei with potentially complex functions among different associated behaviors were emphasized, leading to results serving as important clues to the development of valid hypotheses for exploring as yet unknown circuits. Using the analysis function of cFos-ANAB, multi-layered pictures of networks and their relationships can quickly be explored depending on users' purposes. These features provide a useful tool and good reference for early exploration in neuroscience. The cFos-ANAB database is available at www.cfos-db.net .
Animals ; Fear ; Mice ; Neurons ; Proto-Oncogene Proteins c-fos ; Rats

cFos is one of the most widely-studied genes in the field of neuroscience. Currently, there is no systematic database focusing on cFos in neuroscience. We developed a curated database—cFos-ANAB—a cFos-based web tool for exploring activated neurons and associated behaviors in rats and mice, comprising 398 brain nuclei and sub-nuclei, and five associated behaviors: pain, fear, feeding, aggression, and sexual behavior. Direct relationships among behaviors and nuclei (even cell types) under specific stimulating conditions were constructed based on cFos expression profiles extracted from original publications. Moreover, overlapping nuclei and sub-nuclei with potentially complex functions among different associated behaviors were emphasized, leading to results serving as important clues to the development of valid hypotheses for exploring as yet unknown circuits. Using the analysis function of cFos-ANAB, multi-layered pictures of networks and their relationships can quickly be explored depending on users’ purposes. These features provide a useful tool and good reference for early exploration in neuroscience. The cFos-ANAB database is available at www.cfos-db.net.