Background: A cluster of patients with coronavirus disease 2019 (COVID-19) pneumonia were discharged from hospitals in Wuhan, China. We aimed to determine the cumulative percentage of the complete radiological resolution at each time point, to explore the relevant affecting factors, and describe the chest CT findings through different timepoints after hospital discharge.Methods: Patients with COVID-19 pneumonia confirmed by RT-PCR who were discharged consecutively from hospital between 5 February 2020 to 10 March 2020 and underwent serial chest CT scans on schedule were enrolled. Radiological demonstrations of all patients were collected and analyzed. The total CT score was the sum of non-GGO invovlement determined at discharge. Afterwards, all patients underwent chest CT scans at 1st, 2nd, and 3rd week after discharge. Imaging features and distribution were analyzed across different time points.Results: 149 patients who completed all CT scans were evaluated, 67 (45.0%) men and 82 (55.0%) women with median age of 43 years old (IQR 36-56). The cumulative percentage of the complete radiological resolution was 8.1% (12 patients), 41.6% (62), 50.3% (75), 53% (79) at discharge and the 1st, 2nd, and 3rd week after discharge, respectively. Patients ≤44 years old showed a significantly higher CP than patients >44 years old after 3-week follow-up. The predominant pattern of abnormality observed at discharge were ground-glass opacification(GGO) (65 [43.6%]), fibrous stripe (45 [30.2%]), and thickening of the adjacent pleura (16 [10.7%]). Lung lesion showed obvious resolution from 2 to 3 weeks after discharge, especially in GGO and fibrous stripe. “Tinted” sign and branchovascular bundle distortion as two special features were discovered in the evolution.Conclusion: Lung lesion of COVID-19 pneumonia patient can be absorbed completely in short-term follow-up with no sequelae. 3 weeks after discharge might be the optimal time point for early radiological estimation.

DBE is a useful diagnostic tool with high clinical practice value and should be considered the gold standard for the investigation of small bowel tumors.

In many engineering applications, unknown states, inputs, and parameters exist in the structures. However, most methods require one or two of these variables to be known in order to identify the other(s). Recently, the authors have proposed a method called EGDF for coupled state/input/parameter identification for nonlinear system in state space. However, the EGDF method based solely on acceleration measurements is found to be unstable, which can cause the drift of the identified inputs and displacements. Although some regularization methods can be adopted for solving the problem, they are not suitable for joint input-state identification in real time. In this paper, a strategy of data fusion of displacement and acceleration measurements is used to avoid the low-frequency drift in the identified inputs and structural displacements for linear structural systems. Two numerical examples about a plane truss and a single-stage isolation system are conducted to verify the effectiveness of the proposed modified EGDF algorithm.

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