Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection.

Since the first report of SARS-CoV-2 virus in Wuhan, China in December 2019, a global outbreak of Corona Virus Disease 2019 (COVID-19) pandemic has been aroused. In the prevention of this disease, accurate diagnosis of COVID-19 is the center of the problem. However, due to the limitation of detection technology, the test results are impossible to be totally free from pseudo-positive or -negative.

The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19.

Background: This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19.

Methods: The clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions.

Effectiveness of telemedicine for cardiovascular disease management: systematic review and meta-analysis.

Background: With the development of information communication technology (ICT), telemedicine has become a promising option for patients with chronic diseases who need continuous monitoring at home or in remote health care facilities. As cardiovascular disease (CVD) is responsible for an estimated 17.9 million deaths globally each year, it is appropriate to evaluate the effectiveness of telemedicine for the health care management of CVD patients.

Development and validation of a clinical prediction model to estimate the risk of critical patients with COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. This study was aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID-19 at admission progressing to critical patients. Patients admitted to the hospital between January 16, 2020, and March 10, 2020, were retrospectively enrolled, and they were observed for at least 14 days after admission to determine whether they developed into severe pneumonia.

Value of imaging findings in predicting post-operative recurrence of desmoid-type fibromatosis.

Desmoid-type fibromatosis is a rare type of soft-tissue tumor originating from connective tissue of the fascia or aponeurosis, which exhibits aggressive growth, high likelihood of relapse and less frequent distant metastasis. The present study aimed to predict the recurrence rate and time by retrospectively analyzing the clinical data (sex, age and recurrence time), imaging findings [tumor location, maximum diameter, border, computed tomography (CT) enhancement ratio, magnetic resonance enhancement ratio and T2 signal ratio] and pathological features (Ki-67 and microscopic margin) in a total of 102 cases of pathologically confirmed desmoid-type fibromatosis. The risk ratio of each factor was calculated using the Cox proportional hazards regression model and the cumulative recurrence-free survival rate was determined using the Kaplan-Meier method and the log-rank test.