Mcdougalldugan3113

We propose a novel ABCDE (assessment, behavior modification, cardiorespiratory fitness, dosage, and education) practical framework. This clinical paradigm is grounded in ongoing physical therapist assessment throughout the episode of care, along with behavior modification, assessment of cardiorespiratory fitness, appropriate selection and dosing of interventions, and patient education. Examples highlighting the use of this model in patients with heart failure across the continuum of care are provided for application in clinical care.

Heterogeneity is a hallmark of many complex human diseases, and unsupervised heterogeneity analysis has been extensively conducted using high-throughput molecular measurements and histopathological imaging features. "Classic" heterogeneity analysis has been based on simple statistics such as mean, variance, and correlation. Network-based analysis takes interconnections as well as individual variable properties into consideration and can be more informative. Several Gaussian graphical model (GGM)-based heterogeneity analysis techniques have been developed, but friendly and portable software is still lacking. To facilitate more extensive usage, we develop the R package HeteroGGM, which conducts GGM-based heterogeneity analysis using the advanced penaliztaion techniques, can provide informative summary and graphical presentation, and is efficient and friendly.

The package is available at https//CRAN.R-project.org/package=HeteroGGM.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

A global effort is underway to identify compounds for the treatment of COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases.

We propose a machine learning representation framework that uses deep learning induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model in-turn uses a consensus framework to rank approved compounds against viral proteins of interest.

Our consensus framework achieves a highmean Pearson correlation of 0.916, mean R2 of 0.840 and a low mean squared error of 0.313 for the task of compound-viral protein activity prediction on an independent test set. As a use case, we identify a ranked list of 47 compounds common to three main proteins of SARS-COV-2 virus (PL-PRO, 3CL-PRO and Spike protein) as potential targets including 21 antivirals, 15 anticancer, 5 antibiotics and 6 other investigationalhuman compounds.We performadditional molecular docking simulations to demonstrate thatmajority of these compounds have low binding energies and thus high binding affinity with the potential to be effective against the SARS-COV-2 virus.

All the source code and data is available at https//github.com/raghvendra5688/Drug-Repurposing and https//dx.doi.org/10.17632/8rrwnbcgmx.3. We also implemented a web-server at https//machinelearning-protein.qcri.org/index.html.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.The homeostasis of the vertebrate body depends on anabolic and catabolic activities that are closely linked the inside and outside of the cell. Lipid metabolism plays an essential role in these metabolic activities. Although a large amount of evidence shows that normal lipid metabolism guarantees the conventional physiological activities of organs in the vertebrate body and that abnormal lipid metabolism plays an important role in the occurrence and deterioration of cardiovascular-related diseases, such as obesity, atherosclerosis, and type II diabetes, little is known about the role of lipid metabolism in cartilage and its diseases. This review aims to summarize the latest advances about the function of lipid metabolism in cartilage and its diseases including osteoarthritis, rheumatoid arthritis, and cartilage tumors. With the gradual in-depth understanding of lipid metabolism in cartilage, treatment methods could be explored to focus on this metabolic process in various cartilage diseases.

We aimed to develop a model for accurate prediction of general care inpatient deterioration.

Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. click here Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call.

Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score.

Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering.

MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.