Evaluation of 3D Human Intestinal Organoids as a Platform for EV-A71 Antiviral Drug Discovery.

Enteroviruses are a leading cause of upper respiratory tract, gastrointestinal, and neurological infections. Management of enterovirus-related diseases has been hindered by the lack of specific antiviral treatment. The pre-clinical and clinical development of such antivirals has been challenging, calling for novel model systems and strategies to identify suitable pre-clinical candidates. Organoids represent a new and outstanding opportunity to test antiviral agents in a more physiologically relevant system. However, dedicated studies addressing the validation and direct comparison of organoids versus commonly used cell lines are lacking. Here, we described the use of human small intestinal organoids (HIOs) as a model to study antiviral treatment against human enterovirus 71 (EV-A71) infection and compared this model to EV-A71-infected RD cells. We used reference antiviral compounds such as enviroxime, rupintrivir, and 2'-C-methylcytidine (2'CMC) to assess their effects on cell viability, virus-induced cytopathic effect, and viral RNA yield in EV-A71-infected HIOs and cell line. The results indicated a difference in the activity of the tested compounds between the two models, with HIOs being more sensitive to infection and drug treatment. In conclusion, the outcome reveals the value added by using the organoid model in virus and antiviral studies.

Topology optimization search of deep convolution neural networks for CT and X-ray image classification.

Covid-19 is a disease that can lead to pneumonia, respiratory syndrome, septic shock, multiple organ failure, and death. This pandemic is viewed as a critical component of the fight against an enormous threat to the human population. Deep convolutional neural networks have recently proved their ability to perform well in classification and dimension reduction tasks. Selecting hyper-parameters is critical for these networks. This is because the search space expands exponentially in size as the number of layers increases. All existing approaches utilize a pre-trained or designed architecture as an input. None of them takes design and pruning into account throughout the process. In fact, there exists a convolutional topology for any architecture, and each block of a CNN corresponds to an optimization problem with a large search space. However, there are no guidelines for designing a specific architecture for a specific purpose; thus, such design is highly subjective and heavily reliant on data scientists' knowledge and expertise. Motivated by this observation, we propose a topology optimization method for designing a convolutional neural network capable of classifying radiography images and detecting probable chest anomalies and infections, including COVID-19. Our method has been validated in a number of comparative studies against relevant state-of-the-art architectures.