Preterm birth (PTB), birth at <37 weeks of gestation, is a significant global public health problem. World-wide, about 15 million babies are born preterm each year resulting in more than a million deaths of children. Preterm neonates are more prone to problems and need intensive care hospitalization. Health issues may persist through early adulthood and even be carried on to the next generation. Majority (70 %) of PTBs are spontaneous with about a half without any apparent cause and the other half associated with a number of risk factors. Genetic factors are one of the significant risks for PTB. The focus of this review is on single nucleotide gene polymorphisms (SNPs) that are reported to be associated with PTB. A comprehensive evaluation of studies on SNPs known to confer potential risk of PTB was done by performing a targeted PubMed search for the years 2007–2015 and systematically reviewing all relevant studies. Evaluation of 92 studies identified 119 candidate genes with SNPs that had potential association with PTB. The genes were associated with functions of a wide spectrum of tissue and cell types such as endocrine, tissue remodeling, vascular, metabolic, and immune and inflammatory systems. A number of potential functional candidate gene variants have been reported that predispose women for PTB. Understanding the complex genomic landscape of PTB needs high-throughput genome sequencing methods such as whole-exome sequencing and whole-genome sequencing approaches that will significantly enhance the understanding of PTB. Identification of high risk women, avoidance of possible risk factors, and provision of personalized health care are important to manage PTB.

Conventional treatment methods make even the most basic healthcare issues more complicated, which in turn increases the number of parties involved. Classical computing lacks the speed and accuracy needed for effective stakeholder collaboration in COVID-19 healthcare solutions, such as patients, insurance agents, healthcare practitioners, pharmaceutical suppliers, etc. The research uses organizational information processing theory (OIPT) to examine how quantum computing which is applications of artificial intelligence (AI) could transform the healthcare business, creating a more sustainable and less burdened system. The study of quantum computing (QC) has the potential to bring about “quantum leaps,” which might have unforeseen consequences for healthcare. The discovery of new medications, the personalization of medicinal treatments, and the acceleration of DNA sequencing are just a few of the many possible applications of this method. The potential of QC to transform compute-intensive healthcare tasks like drug-discovery, personalized-medicine, DNA-sequencing, medical-imaging, and operational-optimization is the primary focus of this survey paper, which offers the first comprehensive analysis of QCs diverse capabilities in improving healthcare systems. After a thorough literature study, we created taxonomies on the healthcare QC paradigm’s history and supporting technologies, applications, needs, architectures, security, outstanding questions, and future research prospects. We hope that by conducting this survey, researchers with varying levels of experience in quantum computing and healthcare will better understand the state of the art, assess opportunities and threats, and make informed decisions as they develop novel architectures and applications for this emerging field.

Recent genetic studies have revealed the impact of mutations in associated genes for cardiac sarcomere components leading to dilated cardiomyopathy (DCM). The cardiac sarcomere is composed of thick and thin filaments and a giant muscle protein known as titin or connectin. Titin interacts with T-cap/telethonin in the Z-line region and plays a vital role in regulating sarcomere assembly. Initially, we screened all the variants associated with giant protein titin and analyzed their impact with the aid of pathogenicity and stability prediction methods. V54M mutation found in the hydrophobic core region of the protein associated with abnormal clinical phenotype leads to DCM was selected for further analysis. To address this issue, we mapped the deleterious mutant V54M, modeled the mutant protein complex, and deciphered the impact of mutation on binding with its partner telethonin in the titin crystal structure of PDB ID: 1YA5 with the aid of docking analysis. Furthermore, two run molecular dynamics simulation was initiated to understand the mechanistic action of V54M mutation in altering the protein structure, dynamics, and stability. According to the results obtained from the repeated 50 ns trajectory files, the overall effect of V54M mutation was destabilizing and transition of bend to coil in the secondary structure was observed. Furthermore, MMPBSA elucidated that V54M found in the Z-line region of titin decreases the binding affinity of titin to Z-line proteins T-cap/telethonin thereby hindering the protein–protein interaction.