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Cardiovascular diseases result in millions of deaths around the globe annually, most of which are avoidable if identified early. Preventive healthcare has a major role in the fight against cardiovascular diseases. Primary, secondary, and tertiary prevention have their own applications along with benefits and drawbacks. This paper aims to elevate the sensitivity of "secondary prevention of cardiovascular diseases." Firstly, it discusses common types of cardiovascular diseases around the globe and their causes. Secondly, it analyzes different risk factors associated with cardiovascular diseases and then discusses incoming technological trends in cardiovascular disease prediction and finally provides an insight into the importance of secondary prevention of cardiovascular diseases and commonly prescribed interventions for high risk patients.

Diseases resulting from alterations in gene expressions through mutations in the genes or through changes in the gene expression regulation could be identified through the analysis of RNA expressions. ncRNAs play a significant role in regulation of the gene expression by controlling the expression levels of the coding RNAs and other cellular processes. Discoveries have shown that the human genome is encoded with sequences responsible for the transcription of thousands of ncRNAs. Even though the studies conducted on ncRNAs are still at initial stages, facts established so far display biomarkers that confirm their relationship with certain diseases such as cancers, cardiovascular diseases, and insulin resistance. These studies have been facilitated with high throughput modern sequencing techniques such as microarrays and RNA sequencing. The data obtained through the above analysis are processed with the aid of existing databases, to deduce conclusions on different diagnostic biomarkers and therapeutic targets for specific diseases. This review focuses on the association of ncRNAs in disease prediction, focusing mainly on cardiovascular diseases and disorders caused by insulin resistance. The report also analyzes regulatory functions of ncRNAs and novel approaches used in disease therapeutics.

With the exponential growth in the capacity of information generated and the emerging need for data to be stored for prolonged period of time, there emerges a need for a storage medium with high capacity, high storage density, and possibility to withstand extreme environmental conditions. DNA emerges as the prospective medium for data storage with its striking features. Diverse encoding models for reading and writing data onto DNA, codes for encrypting data which addresses issues of error generation, and approaches for developing codons and storage styles have been developed over the recent past. DNA has been identified as a potential medium for secret writing, which achieves the way towards DNA cryptography and stenography. DNA utilized as an organic memory device along with big data storage and analytics in DNA has paved the way towards DNA computing for solving computational problems. This paper critically analyzes the various methods used for encoding and encrypting data onto DNA while identifying the advantages and capability of every scheme to overcome the drawbacks identified priorly. Cryptography and stenography techniques have been analyzed in a critical approach while identifying the limitations of each method. This paper also identifies the advantages and limitations of DNA as a memory device and memory applications.

Predicting disease genes for a particular genetic disease is very challenging in bioinformatics. Based on current research studies, this challenge can be tackled via network-based approaches. Furthermore, it has been highlighted that it is necessary to consider disease similarity along with the protein's proximity to disease genes in a protein-protein interaction (PPI) network in order to improve the accuracy of disease gene prioritization. In this study we propose a new algorithm called proximity disease similarity algorithm (ProSim), which takes both of the aforementioned properties into consideration, to prioritize disease genes. To illustrate the proposed algorithm, we have conducted six case studies, namely, prostate cancer, Alzheimer's disease, diabetes mellitus type 2, breast cancer, colorectal cancer, and lung cancer. We employed leave-one-out cross validation, mean enrichment, tenfold cross validation, and ROC curves to evaluate our proposed method and other existing methods. The results show that our proposed method outperforms existing methods such as PRINCE, RWR, and DADA.