Last decade has witnessed three major pandemics caused by SARS-CoV, SARS-CoV-2 and MERS-CoV that belong to Coronavirus family. Currently, there are no effective therapies available for corona virus infections. Since the three viruses belong to the same family and share many common features, we can theoretically design a drug that can be effective on all the three of them. In this study, using computational approach, we designed a peptide (Peptide 7) that can bind to the Receptor Binding Domain (RBD) of SARS-CoV, SARS-CoV-2 and MERS-CoV thereby preventing the entry of the viruses into the host cell. The peptide inhibitor was designed as a consensus peptide from three different peptides that might individually bind to the RBD of the three viruses. Docking studies and molecular dynamic simulations using Peptide 7 has shown that it binds with higher affinity than the native receptors of the RBD and forms a stable complex thereby preventing further viral-receptor interaction and inhibiting their cellular entry. This effective binding is observed for the three RBDs, despite the Peptide 7 interactions being slightly different. Hence; this peptide inhibitor can be used as a potential candidate for the development of peptide based anti-viral therapy against Corona viruses.
Infertility is a global crisis affecting 15% of global population. Rapidly declining sperm counts below critical levels demands immediate attention to make fertility treatment widely available; accessible and affordable; the triple aim in healthcare. Though fertility treatments have advanced in recent years manifold, unfortunately many are still away from accessing the available treatment due to various behavioural influences and biases. Infertility not only affects physical health, but also impacts mental, social and emotional health of individuals and society. Unawareness, guilt, shame and coping issues are some of the strong biases/influences that effect healthcare seeking action. Beneficial effects of behavioural economics (should vs would) has been well studied and applied in health policy and treatment interventions, especially in chronic diseases. A systematic understanding of behavioural stages patients go through during fertility treatment journey; from seeking treatment, adjusting to the multiple cycles of anticipation to welcoming a baby can greatly help individuals access available treatment sooner, in the appropriate way and accept the journey for better outcome with less burden. Providers too will be better equipped to help patients in an informed empathetic counselling once they understand the psycho-behavioural transitions of the patients throughout the journey. Fertility policies, patient education can be designed based on behavioural models that can make fertility treatment accessible at community level.
In this paper, we propose a method for detecting arrhythmia in single-lead electro-cardiogram (ECG) signal. By applying a sequence of pre-processing steps (filtering, baseline correction), beat classification and rhythm identification, six different beat-types and four abnormal rhythms are detected. Beat classification uses fast Fourier transform (FFT) as the feature and a support vector machine (SVM) classifier. Subsequently rhythm identification uses a deterministic finite state machine to detect abnormal rhythms. We evaluate the performance of our technique on the MIT-BIH database, to obtain 97% beat classification accuracy and perfect rhythm identification result.
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