Here, we present a small Russian family, where the index patient received a diagnosis of left-ventricular non-compaction cardiomyopathy (LVNC) in combination with a skeletal myopathy. Clinical follow-up analysis revealed a LVNC phenotype also in her son. Therefore, we applied a broad next-generation sequencing gene panel approach for the identification of the underlying mutation. Interestingly, DES-p.A337P was identified in the genomes of both patients, whereas only the index patient carried DSP-p.L1348X. DES encodes the muscle-specific intermediate filament protein desmin and DSP encodes desmoplakin, which is a cytolinker protein connecting desmosomes with the intermediate filaments. Because the majority of DES mutations cause severe filament assembly defects and because this mutation was found in both affected patients, we analyzed this DES mutation in vitro by cell transfection experiments in combination with confocal microscopy. Of note, desmin-p.A337P forms cytoplasmic aggregates in transfected SW-13 cells and in cardiomyocytes derived from induced pluripotent stem cells underlining its pathogenicity. In conclusion, we suggest including the DES gene in the genetic analysis for LVNC patients in the future, especially if clinical involvement of the skeletal muscle is present.

About 50% of patients with arrhythmogenic cardiomyopathy (ACM) carry a pathogenic or likely pathogenic mutation in the desmosomal genes. However, there is a significant number of patients without positive familial anamnesis. Therefore, the molecular reasons for ACM in these patients are frequently unknown and a genetic contribution might be underestimated. Here, we used a next-generation sequencing (NGS) approach and in addition single nucleotide polymor-phism (SNP) arrays for the genetic analysis of two independent index patients without familial medical history. Of note, this genetic strategy revealed a homozygous splice site mutation (DSG2–c.378+1G>T) in the first patient and a nonsense mutation (DSG2–p.L772X) in combination with a large deletion in DSG2 in the second one. In conclusion, a recessive inheritance pattern is likely for both cases, which might contribute to the hidden medical history in both families. This is the first report about these novel loss-of-function mutations in DSG2 that have not been previously identi-fied. Therefore, we suggest performing deep genetic analyses using NGS in combination with SNP arrays also for ACM index patients without obvious familial medical history. In the future, this finding might has relevance for the genetic counseling of similar cases.

Aim. To analyze and demonstrate various phenotypes in patients with familial left ventricular noncompaction (LVNC). Materials and methods. In 2013 was created a multicenter registry of LVNC patients. On its basis 30 families with a familial LVNC were selected. Results. 30 LVNC families were selected from the register. From a total of 115 people (probands and relatives) in 71 (61.7%) LVNC was diagnosed (30 probands and 41 relatives with non-compact myocardial criteria). The most common type of remodeling in patients was the dilated type (DT) (n=30), the isolated LVNC with preserved ejection fraction (EF) was slightly less common (n=23), and the hypertrophic type (GT) was detected in 8 patients. 4 patients were diagnosed with the isolated LVNC with a reduced EF. 3 patients were with a combination of non-compact myocardium with congenital heart disease and with a combination of DT and GT (DT+GT). During the analysis of cases a combination of different phenotypes in the same family was observed. The largest number of families was diagnosed with a combination of DT and the isolated LVNC with preserved EF. The development of cardiovascular complications was associated with DT. Conclusion. Family cases of LVNC had different types of myocardial remodeling and variants of clinical course. In one family a combination of different types of left ventricular remodeling is possible. DT is associated with the most severe clinical manifestations. The clinical picture of the isolated LVNC with preserved EF, is the most favorable, but in rare cases, serious clinical manifestations were observed.

The purpose of the study. To analyze the possibility of using artificial intelligence as a decision support system for radiologists for pulmonary nodules detection on Chest CT before and during the COVID-19 pandemic on the example of the system Botkin.AI.Materials and methods. Two groups of Chest CT studies were identified: those performed before (group 1) and during the COVID-19 pandemic (group 2). Each group contains anonymized CT data of 150 patients. Chest CT scans for group 2 were selected based on the percentage of coronavirus lung damage from 0 to 25%. The research was analyzed by the artificial intelligence system Botkin. AI for the presence of peripheral pulmonary nodes up to 6 mm, followed by a “blind” check of the analysis results by three radiologists.Results. In group 1, the sensitivity of the method was 1.0; specificity – 0.88 and AUC – 0.94. In the 2nd group 0.93; 0.81 and 0.86, respectively.In group 2, a slight decrease in specificity is mainly associated with an increase in false positive results in the pulmonary opcities, as manifestations of coronavirus lung damage, taken by the AI model for pulmonary nodes.Conclusion. The platform has a high accuracy of detecting pulmonary nodules on computed tomography of the chest both in studies conducted before and during the COVID-19 pandemic. It can be useful to prevent possible omissions of important findings in conditions of increased workload for radiologists.