Summary Over 85 590 000 individuals have been infected with severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Although there have been an increasing number of reports on coronavirus disease 2019 (COVID-19), it is unclear why infected children show milder symptoms than adults. A retrospective case study was performed at two designated hospitals for COVID-19. Patients (56 children and 63 adults) with confirmed SARS-CoV-2 infection and mild pneumonia were randomly enrolled in this study. The median age of the children was 7.0 years, and 51.79% of them were boys. The median age of the adults was 57 years, and 47.62% were men. The most common symptoms were fever, cough, sputum and diarrhoea. There were no significant differences in symptoms between children and adult patients. In terms of immunological indices on admission, adult patients displayed typical leukopenia and markedly higher levels of IL-2, IL-4, and IL-6 than child patients. The elevation of IL-2, IL-4 and IL-6 in adults induced more extensive lung injury. The effective and non-aggressive immune response successfully resisted SARS-CoV-2 invasion and maintained mild symptoms in child patients. The correlation of higher IL-2, IL-4, and IL-6 with the lung injury might be evidence that preventing excessive cytokine production can avoid further lung damage in these patients.

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.