ObjectiveTo predict changes in the quality of life scores of hemodialysis patients for the coming month and the development of an early warning system using machine learningMethodsIt was a prospective cohort study (one-month duration) at the dialysis center of a tertiary care hospital in Pakistan. The study started on 1st October 2016. About 78 patients have been enrolled till now. Bachelor of Medicine and Bachelor of Surgery (MBBS) qualified doctors administered a proforma with demographics and the validated Urdu version of World Health Organization Quality Of Life-BREF (WHOQOL-BREF). It was to be repeated after one month to the same patient by the same investigator. Simple statistics were computed using SPSS version 24 (IBM Corp., Armonk, NY) while machine learning was performed using R (version 3.0) and Orange (version 3.1).ResultsUsing machine learning algorithms, two models (classification tree and Naïve Bayes) were generated to predict an increase or decrease of 5% in a patient’s WHOQOL-BREF score over one month. The classification tree was selected as the most accurate model with an area under curve (AUC) of 83.3% (accuracy: 81.9%) for the prediction of 5% increase in QOL and an AUC of 76.2% (accuracy: 81.8%) for the prediction of 5% decrease in QOL over the coming month. The factors associated with an increase of QOL by 5% or more over the next month included younger age (<19 years) and higher iron sucrose doses (>278mg/month). Drops in psychological, physical, and social domain scores lead to a decrease of 5% or more in QOL scores over the following month.ConclusionAn early warning system, dialysis data interpretation for algorithmic-prediction on quality of life (DIAL) was built for the early detection of deteriorating QOL scores in the hemodialysis population using machine learning algorithms. The model pointed out that working on psychological and environmental domains, in particular, may prevent the drop in QOL scores from occurring. DIAL, if implemented on a larger scale, is expected to help patients in terms of ensuring a better QOL and in reducing the financial burden in the long term.

BackgroundThe current Novel Coronavirus (SARS-CoV-2) pandemic is the third major outbreak of the 21st century which emerged in December 2019 from Wuhan, China. At present there are no known treatments or vaccines to cure or prevent the illness.ObjectiveThe objective of this study was to explore a list of potential drugs (herbal and antivirals) for their role in inhibiting activity and or replication of SARS-CoV-2 by using molecular docking onto the crystal structures of key viral proteins.MethodologyIn this study, we used molecular docking to estimate the binding affinities of 3699 drugs on the potential active sites of the 6 main SARS-CoV-2 proteins (Papain like protease, Main protease, ADP Ribose phosphatase, Spike protein, NSP-9 and NSP-10 to 16 complex). While other studies have mostly been performed on the homology models, we obtained the most recently submitted crystal structures of all 6 proteins from the protein data bank for this analysis.ResultsOur results showed the top ligands as Theasinensin A, Epigallocatechin, Theaflavin, Theasinensin A, Epigallocatechin and Favipiravir showing the highest binding affinities against papain-like protease, ADP ribose phosphatase, main protease, spike protein, RNA replicase (NSP-9) and methyl-transferase (NSP-16) respectively.ConclusionWe show that the compounds from our list with the greatest inhibitory potential against SARS-CoV-2 activity or replication include Theasinensin A, Epigallocatechin-3-gallate, Theaflavin, Favipiravir, Curucumin, Quercetin, Mitoxantrone, Amentoflavone, Colistin, Cimicifugic acid, Theaflavin, Silymarin and Chebulagic. We recommend further wet-lab and clinical testing of these compounds to further explore their role against SARS-CoV-2.