Background About 10–20% of patients with Coronavirus disease 2019 (COVID-19) infection progressed to severe illness within a week or so after initially diagnosed as mild infection. Identification of this subgroup of patients was crucial for early aggressive intervention to improve survival. The purpose of this study was to evaluate whether computer tomography (CT) - derived measurements of body composition such as myosteatosis indicating fat deposition inside the muscles could be used to predict the risk of transition to severe illness in patients with initial diagnosis of mild COVID-19 infection. Methods Patients with laboratory-confirmed COVID-19 infection presenting initially as having the mild common-subtype illness were retrospectively recruited between January 21, 2020 and February 19, 2020. CT-derived body composition measurements were obtained from the initial chest CT images at the level of the twelfth thoracic vertebra (T12) and were used to build models to predict the risk of transition. A myosteatosis nomogram was constructed using multivariate logistic regression incorporating both clinical variables and myosteatosis measurements. The performance of the prediction models was assessed by receiver operating characteristic (ROC) curve including the area under the curve (AUC). The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Results A total of 234 patients were included in this study. Thirty-one of the enrolled patients transitioned to severe illness. Myosteatosis measurements including SM-RA (skeletal muscle radiation attenuation) and SMFI (skeletal muscle fat index) score fitted with SMFI, age and gender, were significantly associated with risk of transition for both the training and validation cohorts (P < 0.01). The nomogram combining the SM-RA, SMFI score and clinical model improved prediction for the transition risk with an AUC of 0.85 [95% CI, 0.75 to 0.95] for the training cohort and 0.84 [95% CI, 0.71 to 0.97] for the validation cohort, as compared to the nomogram of the clinical model with AUC of 0.75 and 0.74 for the training and validation cohorts respectively. Favorable clinical utility was observed using decision curve analysis. Conclusion We found CT-derived measurements of thoracic myosteatosis to be associated with higher risk of transition to severe illness in patients affected by COVID-19 who presented initially as having the mild common-subtype infection. Our study showed the relevance of skeletal muscle examination in the overall assessment of disease progression and prognosis of patients with COVID-19 infection.

Background The COVID-19 pandemic has led to a spike in deleterious mental health. This dual-center retrospective cross-sectional study assessed the prevalence of depression in young adults during this pandemic and explored its association with various physical fitness measures. Methods This study enrolled 12,889 (80% female) young adults (mean age 20 ± 1) who performed a National Student Physical Fitness battery from December 1st, 2019, to January 20th, 2020, and completed a questionnaire including Beck’s Depression Inventory in May 2020. Independent associations between prior physical fitness and depression during the pandemic were assessed using multivariable linear and binary logistic regressions accordingly, covariates including age, dwelling location, economic level, smoking, alcohol, living status, weight change, and exercise volume during the pandemic. Sex- and baseline stress-stratified analyses were performed. Results Of the study population 13.9% of men and 15.0% of women sampled qualified for a diagnosis of depression. After multivariable adjustment, anaerobic (mean change 95% CI −3.3 [−4.8 to 1.8]) aerobic (−1.5 [−2.64 to −0.5]), explosive (−1.64 [−2.7 to −0.6]) and muscular (−1.7 [−3.0 to −0.5]) fitness were independently and inversely associated with depression for the overall population. These remained consistent after sex- and baseline stress-stratification. In binary logistic regression, the combined participants with moderate, high or excellent fitness also showed a much lower risk compared to those least fit in anaerobic (odd ratio (OR) 95% CI 0.68 [0.55–0.82]), aerobic (0.80 [0.68–0.91]), explosive (0.72 [0.61–0.82]), and muscular (0.66 [0.57–0.75]) fitness. Conclusions These findings suggest that prior physical fitness may be inversely associated with depression in young adults during a pandemic.

During April-August 2020, a preemptive testing strategy combined with accessible isolation and symptom screening among people experiencing homelessness in congregant living settings in San Diego contributed to a low incidence proportion of COVID-19: 0.9%. Proactively addressing challenges specific to a vulnerable population may significantly prevent spread and community outbreaks.

Coal–rock dynamic disasters seriously threaten safe production in coal mines, and an effective early warning is especially important to reduce the losses caused by these disasters. The occurrence of coal–rock dynamic disasters is determined by mining-induced stress loading and unloading. Therefore, it is of great significance to analyze the precursory information of coal deformation and failure during true triaxial stress loading and unloading. In this study, the deformation and failure of coal samples subjected to true triaxial loading and unloading, including fixed axial stress and unloading confining stress (FASUCS), are experimentally investigated. Meanwhile, acoustic emission (AE) during the deformation of coal samples is monitored, and the multi-fractal characteristics of AE are analyzed. Furthermore, combined with the deformation and failure of coal samples, the precursory information of coal deformation and rupture during true triaxial stress loading and unloading is obtained. Finally, the relationship between multi-fractal characteristics and damage evolution of coal samples under FASUCS is discussed. The results show that the multi-fractal spectral widths of AE time series under the conditions of FASUCS with different initial confining stresses or unloading rates are quite different, but the dynamic changes of multi-fractal parameters [Formula: see text] and [Formula: see text] are similar. This indicates that the microscopic complexity of AE events of coal samples under different conditions of FASUCS differs, but the macroscopic generation mechanism of AE events has inherent uniformity. The dynamic changes of [Formula: see text] and [Formula: see text] can reflect the stress and damage degree of coal samples. The dynamic change process of [Formula: see text] well accords with the damage evolution process of coal samples. A gradual decrease of [Formula: see text] corresponds to a slow increase of damage, while a sharp increase of it corresponds to a rapid growth of damage. At the same time, the mutation point of damage curve at distinct stress difference levels shares the same variation trend with the [Formula: see text] mutation point. The change of [Formula: see text] can reflect the damage process of coal samples, which can be used as precursor information for predicting coal–rock rupture. The finding is of great significance for the early warning of coal–rock dynamic disasters.