Objective: To provide clinical management guidelines for novel coronavirus in pregnancy. Methods:On February 5, 2020, a multidisciplinary teleconference comprising Chinese physicians and researchers was held and medical management strategies of COVID-19 infection in pregnancy were discussed. Results:Ten key recommendations were provided for the management of COVID-19 infections in pregnancy. Conclusion:Currently, there is no clear evidence regarding optimal delivery timing, the safety of vaginal delivery, or whether cesarean delivery prevents vertical transmission at the time of delivery; therefore, route of delivery and delivery timing should be individualized based on obstetrical indications and maternal-fetal status.

Soft tubular actuators can be widely found both in nature and in engineering applications. The benefits of tubular actuators include (i) multiple actuation modes such as contraction, bending, and expansion; (ii) facile fabrication from a planar sheet; and (iii) a large interior space for accommodating additional components or for transporting fluids. Most recently developed soft tubular actuators are driven by pneumatics, hydraulics, or tendons. Each of these actuation modes has limitations including complex fabrication, integration, and nonuniform strain. Here, we design and construct soft tubular actuators using an emerging artificial muscle material that can be easily patterned with programmable strain: liquid crystal elastomer. Controlled by an externally applied electrical potential, the tubular actuator can exhibit multidirectional bending as well as large (~40%) homogenous contraction. Using multiple tubular actuators, we build a multifunctional soft gripper and an untethered soft robot.

Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.