The restriction of numerous sectors of society and the uncertainty surrounding the development of the COVID-19 pandemic have resulted in adverse psychological states to college students isolated at home. In this study, we explored the mediating role of fatigue in the effects of epidemic rumination and resilience on depressive symptoms as well as how epidemic rumination and resilience may interact with one another. A large sample of Chinese college students (N = 1,293) completed measures on epidemic rumination, resilience, fatigue, and depressive symptoms. Results indicated depressive symptomology was positively predicted by epidemic rumination while negatively predicted by resilience. In both cases, fatigue partially mediated these effects and positively predicted depressive symptoms. Unexpectedly, epidemic rumination and resilience interacted in a manner where the effect of rumination on fatigue became stronger as resiliency increased. Theoretical and practical implications are provided to further interpret the results.

Quick screening patients with COVID-19 is the most important way of controlling transmission by isolation and medical treatment. Chest computed tomography (CT) has been widely used during the initial screening process, including pneumonia diagnosis, severity assessment, and differential diagnosis of COVID-19. The course of COVID-19 changes rapidly. Serial CT imaging could observe the distribution, density, and range of lesions dynamically, monitor the changes, and then guide towards appropriate treatment. The aim of the review was to explore the chest CT findings and dynamic CT changes of COVID-19 using systematic evaluation methods, instructing the clinical imaging diagnosis. A systematic literature search was performed. The quality of included literature was evaluated with a quality assessment tool, followed by data extraction and meta-analysis. Homogeneity and publishing bias were analyzed. A total of 109 articles were included, involving 2908 adults with COVID-19. The lesions often occurred in bilateral lungs (74%) and were multifocal (77%) with subpleural distribution (81%). Lesions often showed ground-glass opacity (GGO) (68%), followed by GGO with consolidation (48%). The thickening of small vessels (70%) and thickening of intralobular septum (53%) were also common. The dynamic changes of chest CT manifestations showed that lesions were absorbed and improved gradually after reaching the peak (80%), had progressive deterioration (55%), were absorbed and improved gradually (46%), fluctuated (22%), or remained stable (26%). The review showed the common and key CT features and the dynamic imaging change patterns of COVID-19, helping with timely management during COVID-19 pandemic.

Rationale and Objectives: To describe the rational and design of a population-based comparative study. The objective of the study is to assess the screening performance of volume-based management of CT-detected lung nodule in comparison to diameter-based management, and to improve the effectiveness of CT screening for COPD and CVD, in addition to lung cancer, based on quantitative measurement of CT imaging biomarkers in a Chinese screening setting. Materials and Methods: A population-based comparative study is being performed, including 10,000 asymptomatic participants between 40 and 74 years old from Shanghai urban population. Participants in the intervention group undergo a low-dose chest and cardiac CT scan at baseline and one year later, and are managed according to NELCIN-B3 protocol. Participants in the control group undergo a low-dose chest CT scan according to the routine CT protocol and are managed according to the clinical practice. Epidemiological data are collected through questionnaires. In the fourth year from baseline, the diagnosis of the three diseases will be collected. Results: The unnecessary referral rate will be compared between NELCIN-B3 and standard protocol for managing early-detected lung nodules. The effectiveness of quantitative measurement of CT imaging biomarkers for early detection of lung cancer, COPD and CVD will be evaluated. Conclusion: We expect that the quantitative assessment of the CT imaging biomarkers will reduce the number of unnecessary referrals for early detected lung nodules, and will improve the early detection of COPD and CVD in a Chinese urban population.

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Background: Despite the known benefits of physical activity (PA) on cognitive function, the specific dimensions of PA that are associated with cognitive function require further research in China. We aimed to explore the patterns of PA and elucidate the association between cognitive function and different levels of PA in middle aged and elderly Chinese individuals. Methods: A total of 8,023 participants aged ≥45 years were selected from the China Health and Retirement Longitudinal Study. The PA intensity was categorized as: vigorous (VPA), moderate (MPA), and light (LPA). The associations of frequency and duration of PA at different intensities with cognitive function were examined using the multivariable linear model, including all respondents and urban-rural subgroups. Results: Compared with those who had no VPA, those who spent 6-7 days/week (β = −0.59, 95% CI: −1.10, −0.09) or more than 240 min/each time on VPA had poorer cognitive function among rural respondents, whereas cognitive function was only associated with the duration in urban respondents. Compared with those who had no MPA, the rural respondents who spent 1-5 days/week (β = 0.66, 95% CI: 0.12, 1.20) or 6-7days/week, or spent < 2 hours each time had better cognitive function. For LPA, frequency and duration were both positively associated with cognitive function, and were observed in both rural and urban sub-groups. Conclusions: The association between cognitive function and PA depended largely on the intensity and area. Cultural context and geographical differences should be considered when designing intervention policies. Highlights. The prevalence of PA increased as the intensity decreased and was higher in rural respondents than in urban respondents. . Cognitive function was related to the intensity, frequency, and duration of PA. However, the magnitude and direction of the association depended mainly on the intensity and geographical area. . VPA was negatively correlated with cognitive function, and the association was significant only in rural respondents. However, MPA and LPA were positively correlated with cognitive function, while the association between LPA and cognitive function was significant in both rural and urban respondents.

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Purpose To explore optimal threshold of FEV1% predicted value (FEV1%pre) for high-risk chronic obstructive pulmonary disease (COPD) using the parameter response mapping (PRM) based on machine learning classification model. Patients and Methods A total of 561 consecutive non-COPD subjects who were screened for chest diseases in our hospital between August and October 2018 and who had complete questionnaire surveys, pulmonary function tests (PFT), and paired respiratory chest CT scans were enrolled retrospectively. The CT quantitative parameter for small airway remodeling was PRM, and 72 parameters were obtained at the levels of whole lung, left and right lung, and five lobes. To identify a more reasonable thresholds of FEV1% predicted value for distinguishing high-risk COPD patients from the normal, 80 thresholds from 50% to 129% were taken with a partition of 1% to establish a random forest classification model under each threshold, such that novel PFT-parameter-based high-risk criteria would be more consistent with the PRM-based machine learning classification model. Results Machine learning-based PRM showed that consistency between PRM parameters and PFT was better able to distinguish high-risk COPD from the normal, with an AUC of 0.84 when the threshold was 72%. When the threshold was 80%, the AUC was 0.72 and when the threshold was 95%, the AUC was 0.64. Conclusion Machine learning-based PRM is feasible for redefining high-risk COPD, and setting the optimal FEV1% predicted value lays the foundation for redefining high-risk COPD diagnosis.

Background Sensory impairments and eye diseases increase the risk of cognitive decline, but little is known regarding their influence on cognitive function in elderly Chinese and the underlying mechanisms. We aimed to explore these influence mechanism from the social participation perspective. Methods We selected 2876 respondents aged ≥60 from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2013, 2015, and 2018. We assessed sensory impairments and eye diseases based on self-reported responses, and evaluated its relation to social participation and cognitive function by fixed-effects regression and mediation effect analysis over a five-year period. Results Respondents with visual impairment and cataracts had poor memory and mental status. Compared with near visual impairment, distance visual impairment was associated with a 1.7 times higher likelihood of cognitive decline (correlation coefficient (β) = -0.051; 95% confidence interval (CI) = -0.065, -0.036)). Respondents with hearing impairment had bad memory (β = -0.046; 95% CI = -0.065, -0.036), but not mental status. Social participation partially mediated the relationships of sensory impairments and cataracts with cognitive function in elderly Chinese. Individuals with sensory impairments affected by limited social participation reported a faster cognitive decline compared to those with eye disease. Conclusions We found that sensory impairments and eye diseases were negatively associated with cognitive function. Furthermore, sensory impairments and cataracts influence cognitive function partly via social participation. Our results have important theoretical and practical implications and suggests that early interventions for sensory impairments and eye diseases may improve the cognitive function of elderly people.