Robots Under COVID-19 Pandemic: A Comprehensive Survey.

As a result of the difficulties brought by COVID-19 and its associated lockdowns, many individuals and companies have turned to robots in order to overcome the challenges of the pandemic. Compared with traditional human labor, robotic and autonomous systems have advantages such as an intrinsic immunity to the virus and an inability for human-robot-human spread of any disease-causing pathogens, though there are still many technical hurdles for the robotics industry to overcome. This survey comprehensively reviews over 200 reports covering robotic systems which have emerged or have been repurposed during the past several months, to provide insights to both academia and industry. In each chapter, we cover both the advantages and the challenges for each robot, finding that robotics systems are overall apt solutions for dealing with many of the problems brought on by COVID-19, including: diagnosis, screening, disinfection, surgery, telehealth, care, logistics, manufacturing and broader interpersonal problems unique to the lockdowns of the pandemic. By discussing the potential new robot capabilities and fields they applied to, we expect the robotics industry to take a leap forward due to this unexpected pandemic.

3D Bioprinted GelMA Based Models for the Study of Trophoblast Cell Invasion.

Bioprinting is an emerging and promising technique for fabricating 3D cell-laden constructs for various biomedical applications. In this paper, we employed 3D bioprinted GelMA-based models to investigate the trophoblast cell invasion phenomenon, enabling studies of key placental functions. Initially, a set of optimized material and process parameters including GelMA concentration, UV crosslinking time and printing configuration were identified by systematic, parametric study. Following this, a multiple-ring model (2D multi-ring model) was tested with the HTR-8/SVneo trophoblast cell line to measure cell movement under the influence of EGF (chemoattractant) gradients. In the multi-ring model, the cell front used as a cell invasion indicator moves at a rate of 85 ± 33 µm/day with an EGF gradient of 16 µM. However, the rate was dramatically reduced to 13 ± 5 µm/day, when the multi-ring model was covered with a GelMA layer to constrain cells within the 3D environment (3D multi-ring model). Due to the geometric and the functional limitations of multi-ring model, a multi-strip model (2D multi-strip model) was developed to investigate cell movement in the presence and absence of the EGF chemoattractant. The results show that in the absence of an overlying cell-free layer of GelMA, movement of the cell front shows no significant differences between control and EGF-stimulated rates, due to the combination of migration and proliferation at high cell density (6 × 106 cells/ml) near the GelMA surface. When the model was covered by a layer of GelMA (3D multi-strip model) and migration was excluded, EGF-stimulated cells showed an invasion rate of 21 ± 3 µm/day compared to the rate for unstimulated cells, of 5 ± 4 µm/day. The novel features described in this report advance the use of the 3D bioprinted placental model as a practical tool for not only measurement of trophoblast invasion but also the interaction of invading cells with other tissue elements.

Designing a wearable navigation system for image-guided cancer resection surgery.

A wearable surgical navigation system is developed for intraoperative imaging of surgical margin in cancer resection surgery. The system consists of an excitation light source, a monochromatic CCD camera, a host computer, and a wearable headset unit in either of the following two modes: head-mounted display (HMD) and Google glass. In the HMD mode, a CMOS camera is installed on a personal cinema system to capture the surgical scene in real-time and transmit the image to the host computer through a USB port. In the Google glass mode, a wireless connection is established between the glass and the host computer for image acquisition and data transport tasks. A software program is written in Python to call OpenCV functions for image calibration, co-registration, fusion, and display with augmented reality. The imaging performance of the surgical navigation system is characterized in a tumor simulating phantom. Image-guided surgical resection is demonstrated in an ex vivo tissue model. Surgical margins identified by the wearable navigation system are co-incident with those acquired by a standard small animal imaging system, indicating the technical feasibility for intraoperative surgical margin detection. The proposed surgical navigation system combines the sensitivity and specificity of a fluorescence imaging system and the mobility of a wearable goggle. It can be potentially used by a surgeon to identify the residual tumor foci and reduce the risk of recurrent diseases without interfering with the regular resection procedure.