Prediction and early detection of cardiovascular disease in South Asians with diabetes mellitus

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.

Novel roles of PIWI proteins and PIWI-interacting RNAs in human health and diseases

Non-coding RNA has aroused great research interest recently, they play a wide range of biological functions, such as regulating cell cycle, cell proliferation, and intracellular substance metabolism. Piwi-interacting RNAs (piRNAs) are emerging small non-coding RNAs that are 24-31 nucleotides in length. Previous studies on piRNAs were mainly limited to evaluating the binding to the PIWI protein family to play the biological role. However, recent studies have shed more lights on piRNA functions; aberrant piRNAs play unique roles in many human diseases, including diverse lethal cancers. Therefore, understanding the mechanism of piRNAs expression and the specific functional roles of piRNAs in human diseases is crucial for developing its clinical applications. Presently, research on piRNAs mainly focuses on their cancer-specific functions but lacks investigation of their expressions and epigenetic modifications. This review discusses piRNA's biogenesis and functional roles and the recent progress of functions of piRNA/PIWI protein complexes in human diseases. Video Abstract.