The Human Genome Project (HGP), as the primary sequencing of the human genome, lasted more than one decade to be completed using the traditional Sanger's method. At present, next-generation sequencing (NGS) technology could provide the genome sequence data in hours. NGS has also decreased the expense of sequencing; therefore, nowadays it is possible to carry out both whole-genome (WGS) and whole-exome sequencing (WES) for the variations detection in patients with rare genetic diseases as well as complex disorders such as common cardiovascular diseases (CVDs). Finding new variants may contribute to establishing a risk profile for the pathology process of diseases. Here, recent applications of NGS in cardiovascular medicine are discussed; both Mendelian disorders of the cardiovascular system and complex genetic CVDs including inherited cardiomyopathy, channelopathies, stroke, coronary artery disease (CAD) and are considered. We also state some future use of NGS in clinical practice for increasing our information about the CVDs genetics and the limitations of this new technology. Key messages Traditional Sanger's method was the mainstay for Human Genome Project (HGP); Sanger sequencing has high fidelity but is slow and costly as compared to next generation methods. Within cardiovascular medicine, NGS has been shown to be successful in identifying novel causative mutations and in the diagnosis of Mendelian diseases which are caused by a single variant in a single gene. NGS has provided the opportunity to perform parallel analysis of a great number of genes in an unbiased approach (i.e. without knowing the underlying biological mechanism) which probably contribute to advance our knowledge regarding the pathology of complex diseases such as CVD.

The giant protein titin (TTN) is a sarcomeric protein that forms the myofibrillar backbone for the components of the contractile machinery which plays a crucial role in muscle disorders and cardiomyopathies. Diagnosing TTN pathogenic variants has important implications for patient management and genetic counseling. Genetic testing for TTN variants can help identify individuals at risk for developing cardiomyopathies, allowing for early intervention and personalized treatment strategies. Furthermore, identifying TTN variants can inform prognosis and guide therapeutic decisions. Deciphering the intricate genotype–phenotype correlations between TTN variants and their pathologic traits in cardiomyopathies is imperative for gene-based diagnosis, risk assessment, and personalized clinical management. With the increasing use of next-generation sequencing (NGS), a high number of variants in the TTN gene have been detected in patients with cardiomyopathies. However, not all TTN variants detected in cardiomyopathy cohorts can be assumed to be disease-causing. The interpretation of TTN variants remains challenging due to high background population variation. This narrative review aimed to comprehensively summarize current evidence on TTN variants identified in published cardiomyopathy studies and determine which specific variants are likely pathogenic contributors to cardiomyopathy development.

Cardiovascular diseases (CVDs) constitute one of the significant causes of death worldwide. Different pathological states are linked to CVDs, which despite interventions and treatments, still have poor prognoses. The genetic component, as a beneficial tool in the risk stratification of CVD development, plays a role in the pathogenesis of this group of diseases. The emergence of genome‐wide association studies (GWAS) have led to the identification of non‐coding parts associated with cardiovascular traits and disorders. Variants located in functional non‐coding regions, including promoters/enhancers, introns, miRNAs and 5′/3′ UTRs, account for 90% of all identified single‐nucleotide polymorphisms associated with CVDs. Here, for the first time, we conducted a comprehensive review on the reported non‐coding variants for different CVDs, including hypercholesterolemia, cardiomyopathies, congenital heart diseases, thoracic aortic aneurysms/dissections and coronary artery diseases. Additionally, we present the most commonly reported genes involved in each CVD. In total, 1469 non‐coding variants constitute most reports on familial hypercholesterolemia, hypertrophic cardiomyopathy and dilated cardiomyopathy. The application and identification of non‐coding variants are beneficial for the genetic diagnosis and better therapeutic management of CVDs.

Background: Patients’ rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients’ messages by a combination of lexicon-based and machine learning methods to identify positive or negative comments and to determine the different ward and staff names mentioned in patients’ messages. Methods: The level of satisfaction and observance of the rights of 250 service recipients of the hospital was evaluated through the related checklists by the evaluator. In total, 822 Persian messages, composed of 540 negative and 282 positive comments, were collected and labeled by the evaluator. Pre-processing was performed on the messages and followed by 2 feature vectors which were extracted from the messages, including the term frequency–inverse document frequency (TFIDF) vector and a combination of the multifeature (MF) (a lexicon-based method) and TFIDF (MF + TFIDF) vectors. Six feature selectors and 5 classifiers were used in this study. For the evaluations, 5-fold cross-validation with different metrics including area under the receiver operating characteristic curve (AUC), accuracy (ACC), F1 score, sensitivity (SEN), specificity (SPE) and Precision-Recall Curves (PRC) were reported. Message tag detection, which featured different hospital wards and identified staff names mentioned in the study patients’ messages, was implemented by the lexicon-based method. Results: The best classifier was Multinomial Naïve Bayes in combination with MF + TFIDF feature vector and SelectFromModel (SFM) feature selection (ACC = 0.89 ± 0.03, AUC = 0.87 ± 0.03, F1 = 0.92 ± 0.03, SEN = 0.93 ± 0.04, and SPE = 0.82 ± 0.02, PRC-AUC = 0.97). Two methods of assessment by the evaluator and artificial intelligence as well as survey systems were compared. Conclusion: Our results demonstrated that the lexicon-based method, in combination with machine learning classifiers, could extract sentiments in patients’ comments and classify them into positive and negative categories. We also developed an online survey system to analyze patients’ satisfaction in different wards and to remove conventional assessments by the evaluator.