Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine.

The recent elevation of cases infected from novel COVID-19 has placed the human life in trepidation mode, especially for those suffering from comorbidities. Most of the studies in the last few months have undeniably raised concerns for hypertensive patients that face greater risk of fatality from COVID-19. Furthermore, one of the recent WHO reports has estimated a total of 1.13 billion people are at a risk of hypertension of which two-thirds live in low and middle income countries. The gradual escalation of the hypertension problem andthe sudden rise of COVID-19 cases have placed an increasingly higher number of human lives at risk in low and middle income countries. To lower the risk of hypertension, most physicians recommend drugs that have angiotensin-converting enzyme (ACE) inhibitors. However, prolonged use of such drugs is not recommended due to metabolic risks and the increase in the expression of ACE-II which could facilitate COVID-19 infection. In contrast, the intake of optimal macronutrients is one of the possible alternatives to naturally control hypertension. In the present study, a nontrivial feature selection and machine learning algorithm is adopted to intelligently predict the food-derived antihypertensive peptide. The proposed idea of the paper lies in reducing the computational power while retaining the performance of the support vector machine (SVM) by estimating the dominant pattern in the features space through feature filtering. The proposed feature filtering algorithm has reported a trade-off performance by reducing the chances of Type I error, which is desirable when recommending a dietary food to patients suffering from hypertension. The maximum achievable accuracy of the best performing SVM models through feature selection are 86.17% and 85.61%, respectively.

Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity.

Angiotensin converting enzyme-I (ACE-I) is a key therapeutic target of the renin-angiotensin-aldosterone system (RAAS), the central pathway of blood pressure regulation. Food-derived peptides with ACE-I inhibitory activities are receiving significant research attention. However, identification of ACE-I inhibitory peptides from different food proteins is a labor-intensive, lengthy, and expensive process. For successful identification of potential ACE-I inhibitory peptides from food sources, a machine learning and structural bioinformatics-based web server has been developed and reported in this study. The web server can take input in the FASTA format or through UniProt ID to perform the in silico gastrointestinal digestion and then screen the resulting peptides for ACE-I inhibitory activity. This unique platform provides elaborated structural and functional features of the active peptides and their interaction with ACE-I. Thus, it can potentially enhance the efficacy and reduce the time and cost in identifying and characterizing novel ACE-I inhibitory peptides from food proteins. URL: http://hazralab.iitr.ac.in/ahpp/index.php.

Improved robustness and performance of discrete time sliding mode control systems.

This paper presents a theoretical analysis along with simulations to show that increased robustness can be achieved for discrete time sliding mode control systems by choosing the sliding variable, or the output, to be of relative degree two instead of relative degree one. In other words it successfully reduces the ultimate bound of the sliding variable compared to the ultimate bound for standard discrete time sliding mode control systems. It is also found out that for such a selection of relative degree two output of the discrete time system, the reduced order system during sliding becomes finite time stable in absence of disturbance. With disturbance, it becomes finite time ultimately bounded.