Kan, H. | Search bioRxiv

bioRxiv2020

Aerodynamic Characteristics and RNA Concentration of SARS-CoV-2 Aerosol in Wuhan Hospitals during COVID-19 Outbreak

BackgroundThe ongoing outbreak of COVID-19 has spread rapidly and sparked global concern. While the transmission of SARS-CoV-2 through human respiratory droplets and contact with infected persons is clear, the aerosol transmission of SARS-CoV-2 has been little studied. MethodsThirty-five aerosol samples of three different types (total suspended particle, size segregated and deposition aerosol) were collected in Patient Areas (PAA) and Medical Staff Areas (MSA) of Renmin Hospital of Wuhan University (Renmin) and Wuchang Fangcang Field Hospital (Fangcang), and Public Areas (PUA) in Wuhan, China during COVID-19 outbreak. A robust droplet digital polymerase chain reaction (ddPCR) method was employed to quantitate the viral SARS-CoV-2 RNA genome and determine aerosol RNA concentration. ResultsThe ICU, CCU and general patient rooms inside Renmin, patient hall inside Fangcang had undetectable or low airborne SARS-CoV-2 concentration but deposition samples inside ICU and air sample in Fangcang patient toilet tested positive. The airborne SARS-CoV-2 in Fangcang MSA had bimodal distribution with higher concentration than those in Renmin during the outbreak but turned negative after patients number reduced and rigorous sanitization implemented. PUA had undetectable airborne SARS-CoV-2 concentration but obviously increased with accumulating crowd flow. ConclusionsRoom ventilation, open space, proper use and disinfection of toilet can effectively limit aerosol transmission of SARS-CoV-2. Gathering of crowds with asymptomatic carriers is a potential source of airborne SARS-CoV-2. The virus aerosol deposition on protective apparel or floor surface and their subsequent resuspension is a potential transmission pathway and effective sanitization is critical in minimizing aerosol transmission of SARS-CoV-2.

microbiology↗

bioRxiv2018

CNN-based radiographic acute tibial fracture detection in the setting of open growth plates

Pediatric tibial fractures are commonly diagnosed by radiographs and constitute one of the common tasks performed by pediatric radiologists. Here, we assess the performance of a convolutional neural network for the detection of acute tibial fractures trained with a limited number of cases in skeletally immature patients. This retrospective study was performed on radiology reports manually classified as normal or tibial fracture. Classified images of orthopaedic implants, casting, and images including other pathology were excluded. The remaining cases constituted 516 studies containing 2118 radiographs. These radiographs were truncated to include a limited investigated field of view which included the distal third of the leg, inclusive of the distal physis. After exclusions, the culled dataset was randomly divided into a training set containing 784 radiographs, a validation set containing 98 radiographs, and a test set 98 radiographs. We used a modified transfer learning approach based on the Xception architecture with additional fully convoluted reasoning and drop-out layers. Of 49 fractures, two were misdiagnosed as normal. Of 49 normal exams, none were misdiagnosed. This led to model accuracy of 97.9%, sensitivity 95.9%, and specificity 100%, comparable to or better than human radiologists. In no instances were normal physes or normal developmental epiphyseal fragmentation of the tibial tuberosity or medial malleolus misclassified as a fracture. We report an efficient method to use a pre-trained network and adapt it to a medical classification task using only a small number of radiographs dedicated to precise anatomical location.

bioinformatics↗