ABSTRACT
In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300-400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum.
Subject(s)
COVID-19 Drug Treatment , Computational Biology/methods , Drug Delivery Systems/methods , A549 Cells , Anti-Inflammatory Agents/chemistry , Anti-Inflammatory Agents/therapeutic use , Benzamidines/chemistry , Benzamidines/therapeutic use , Cell Survival/drug effects , Cluster Analysis , Computer Simulation , Databases, Pharmaceutical , Drug Carriers/chemistry , Drug Repositioning , Drug Stability , Glycyrrhizic Acid/chemistry , Glycyrrhizic Acid/therapeutic use , Guanidines/chemistry , Guanidines/therapeutic use , Humans , Hydrophobic and Hydrophilic Interactions , Micelles , Microscopy, Electron, Transmission , Molecular Structure , Nanoparticles/chemistry , Particle SizeABSTRACT
Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.