Secondary metabolites from natural sources are promising starting points for discovering and developing drug prototypes and new drugs, as many current treatments for numerous diseases are directly or indirectly related to such compounds. Recent advances in bioinformatics tools and molecular networking methods have made it possible to identify novel bioactive compounds. In this study, a workflow combining network-based methods for identifying bioactive compounds found in natural products was streamlined by innovating an automated bioinformatics software. The workflow relies on Global Natural Product Social Molecular Networking (GNPS), a web-based mass spectrometry ecosystem that aims to be an open-access knowledge base for community-wide organization and sharing of raw, processed, or annotated fragmentation mass spectrometry data. By combining computational tools including MZmine2, GNPS, and Cytoscape, the integrated dashboard quickly creates bioactive molecular networks with minimal user intervention and reduces the processing time of the original workflow by over 80%. This newly automated workflow quickens the process of discovering bioactive compounds from natural products. This study uses extracts from Psidium guajava leaves to demonstrate the application of our automated software.
Electromyography (EMG) is an electrical voltage potential linked to muscle contraction, resulting in human joint motion, such as knee flexion. Knee injuries, such as knee osteoarthritis (KOA), disrupt functional mobility of the knee joint and subsequently atrophy the muscles controlling knee movement during activities of daily living (ADL). Consequently, weakened muscles exhibiting deteriorated EMG signal fidelity are hypothesized to have discernible signal patterns from a healthy individual's EMG signals. Pattern recognition algorithms are useful for mapping a set of complex inputs (EMG signals and knee angles) to classify knee health status (injured vs. healthy). A secondary outcome is to predict future knee angles from previous input signals to inform a robotic knee exoskeleton to apply real-time torque assistance to a patient during ADL. A Decision Tree Classifier, Random Forest, Naive Bayes, and a Feed-Forward Neural Network (Fully Connected) were used for binary classification (healthy vs. injured). Partial Least Squares Regression, Decision Tree Regressor, and XGBoost were used to predict future joint angles for the regression task (knee angle prediction). Overall, the Random Forest Classifier had the best overall classification performance. XGBoost and Decision Tree Regression performed the best among regression algorithms for predicting real-time angles during walking while Partial Least Squares Regression performed the best during the standing tasks. In summary, our Machine Learning methods are useful for assisting clinicians and patients during physical rehabilitation by providing quantitative insight into the patient's neuromuscular control of the knee.
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