Antiviral Efficacy of Pralatrexate against SARS-CoV-2

Novel coronavirus (SARS-CoV-2) has caused more than 100 million confirmed cases of human infectious disease (COVID-19) since December 2019 to paralyze our global community. However, only limited access has been allowed to COVID-19 vaccines and antiviral treatment options. Here, we report the efficacy of the anticancer drug pralatrexate against SARS-CoV-2. In Vero and human lung epithelial Calu-3 cells, pralatrexate reduced viral RNA copies of SARS-CoV-2 without detectable cytotoxicity, and viral replication was successfully inhibited in a dose-dependent manner. In a time-to-addition assay, pralatrexate treatment at almost half a day after infection also exhibited inhibitory effects on the replication of SARS-CoV-2 in Calu-3 cells. Taken together, these results suggest the potential of pralatrexate as a drug repurposing COVID-19 remedy.

Antiviral Efficacy of Pralatrexate against SARS-CoV-2

Novel coronavirus (SARS-CoV-2) has caused more than 100 million confirmed cases of human infectious disease (COVID-19) since December 2019 to paralyze our global community. However, only limited access has been allowed to COVID-19 vaccines and antiviral treatment options. Here, we report the efficacy of the anticancer drug pralatrexate against SARS-CoV-2. In Vero and human lung epithelial Calu-3 cells, pralatrexate reduced viral RNA copies of SARS-CoV-2 without detectable cytotoxicity, and viral replication was successfully inhibited in a dose-dependent manner. In a time-to-addition assay, pralatrexate treatment at almost half a day after infection also exhibited inhibitory effects on the replication of SARS-CoV-2 in Calu-3 cells. Taken together, these results suggest the potential of pralatrexate as a drug repurposing COVID-19 remedy.

Flexible sliding frame for gait enhancing mechatronic system (GEMS).

This paper presents a novel flexible sliding thigh frame for a gait enhancing mechatronic system. With its two-layered unique structure, the frame is flexible in certain locations and directions, and stiff at certain other locations, so that it can fît well to the wearer's thigh and transmit the assisting torque without joint loading. The paper describes the basic mechanics of this 3D flexible frame and its stiffness characteristics. We implemented the 3D flexible frame on a gait enhancing mechatronic system and conducted experiments. The performance of the proposed mechanism is verified by simulation and experiments.

A self-aligning knee joint for walking assistance devices.

This paper presents a novel self-aligning knee mechanism for walking assistance devices for the elderly to provide physical gait assistance. Self-aligning knee joints can assist in flexion/extension motions of the knee joint and compensate the knee's transitional movements in the sagittal plane. In order to compensate the center of rotation, which moves with the flexion/extension motion of the human knee joint, a self-aligning knee joint is proposed that adds redundant degrees of freedom (i.e., 2-DoF) to the 1-DoF revolute joint. The key idea of the proposed mechanism is to decouple joint rotations and translations for use in lower-extremity wearable devices. This paper describes the mechanical design of this self-aligning knee mechanism and its implementation on a wearable robot and in preliminary experiments. The performance of the proposed mechanism is verified by simulations and experiments.

Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis / 대한의료정보학회지

OBJECTIVES: Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. METHODS: We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. RESULTS: With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). CONCLUSIONS: In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.