Participation of transcriptional factor ZBTB16 in the processes of physiological bone tissue formation and in pathological calcification of the aortic valve

Degenerative calcific aortic valve stenosis is the most common type of heart valve disease in the Western world. Patients with severe stenosis are associated with 50 percent chance of mortality within two years in the absence of intervention. Surgical interventions are the only treatment method for severe calcific aortic valve stenosis to date. Pharmacological approaches have so far failed to affect the course of the disease. Thus, there is an urgent need to develop novel treatment strategies that could slow down the progression of the stenosis. ZBTB16 is a zinc finger protein with N-term BTB/POZ domain (protein-protein interaction motif) and 9 zinc finger domains (DNA binding motif) in C-term. There is growing evidence proving the participation of ZBTB16 in skeletal development. ZBTB16 has been shown to play a role in the specification of limb and axial skeleton patterning. Moreover, the expression of ZBTB16 is increased in patients with ectopic bone formation. Nowadays, the evidence supports that the mechanisms that play key roles in the formation of bone tissue are similar to the processes occurring during the development of ectopic ossification of the aortic valve. Thus, it can be assumed that ZBTB16 is heavily involved in osteogenic transformation in the aortic valve. Understanding similarities and differences in the mechanisms that mediate osteogenic differentiation of stem cells during bone formation and pathological ossification of tissues can help to find the ways to control the osteogenic differentiation in the human body. The aim of this review is to summarize data on the role of ZBTB16 and its products in the regulation of differentiation and proliferation of cells involved in osteogenesis and in the development of ectopic calcification of the aortic valve. The study of the dynamic changes of ZBTB16 expression in aortic valve calcification is a new and relevant study field.

On-device artificial intelligence: mobile solution for detecting severe aortic valve stenosis based on heart sounds

Background Aortic stenosis is still one of the major causes of sudden cardiac death in the elderly. Noninvasive screening for severe aortic valve stenosis (AS) may result in early cardiac diagnostic leading to an appropriate and timely medical intervention. Purpose The aims of this study were 1) to develop an artificial intelligence to detect severe AS based on heart sounds and 2) to build an application to screen patients using electronic stethoscope and smartphones, which will provide an efficient diagnostic workflow for screening as a complementary tool in daily clinical practice. Methods We enrolled 100 patients diagnosed with severe AS and 200 patients without severe AS (no echocardiographic sign of AS [n=100], mild AS [n=50], moderate AS [n=50]). The heart sounds were recorded in 4000 Hz waveform audio format at the following 3 sites of each patient; the 2nd intercostal right sternal border, the Erb's area and the apex. Each record was divided into multiple data of 4 seconds duration, which built 10800 sound records in total. We developed multiple convolutional neural networks (CNN) designed to recognize severe AS in heart sounds according to the recorded 3 sites. We adopted a stratified 4-fold cross-validation method by which the CNN was trained with 60% of the whole data, validated with 20% data and tested with the remaining 20% data not used during training and validation. As performance metrics we adopted the accuracy, F1 value and the area under the curve (AUC) calculated as the average of all cross-validation folds. For the smartphone application, we combined the best CNN-models from each recorded site for the best performance. Further 40 patients were newly enrolled for its clinical validation (no AS [n=10], mild AS [n=10], moderate AS [n=10], severe AS [n=10]). Results The accuracy, F1 value and AUC of each model were 88.9±5.7%, 0.888±0.006 and 0.953±0.008, respectively. The sensitivity and specificity were 87.9±2.2% and 89.9±2.4%. The recognition accuracy of moderate AS was significantly lower as compared to the other AS grades (moderate AS 74.1±6.1% vs no AS 98.0±1.4%, mild AS 97.6±1.2%, severe AS 87.9±2.2%, respectively, P<0.05). Our smartphone application showed a sensitivity of 100% (10/10), a specificity of 73.3% (22/30), and an accuracy of 80.0% (32/40), which implicated a good utility for screening. In the detailed analysis of 8 mistaken decisions, these were highly affected by the presence of severe mitral or tricuspid valve regurgitation despite of non-severe AS (7/8 [87.5%]). Conclusions This study demonstrated the promising possibility of an end-to-end screening for severe aortic valve stenosis using an electronic stethoscope and a smartphone application. This technology may improve the efficacy of daily medicine particularly where the human resource is limited or support a remote medical consultation. Further investigations are necessary to increase accuracy. Funding Acknowledgement Type of funding sources: None.

411 Reproducibility and applicability of automated speckle tracking echocardiography in patients with aortic valve stenosis

Aims New speckle tracking echocardiography (STE) tools allow an automated detection of strain measurements, but in case of suboptimal image quality or electrocardiogram signal, both regions-of-interest (ROIs) tracking and cardiac cycle’s landmarks need to be edited. In this study, we aimed to assess the variability of STE measurement performed with Tomtec’s Autostrain LV/RV/LA® application across different operators’ expertise in patients with significant aortic valve stenosis, known to have often suboptimal acoustic windows. Methods and results Automated strain analysis was performed by two observers with different levels of expertise in STE (a student sonographer and a trained cardiologist) on scans from 30 consecutive patients with moderate to severe aortic stenosis. Interobserver variability was tested. Intra-observer variability was also assessed repeating measurements about one month after the first set. Manual editing of the automated ROIs or cardiac cycle landmarks tracing results was manually made from both users on the majority (>80%) of examinations; notably RV strain analysis required the least editing. At repeated measurement test (Figure), the average strain values were not found to be significantly different for measurements of apical-four-chamber-global-longitudinal-strain (A4C-GLS) (mean difference 0.96%, P = 0.08), left-atrium-longitudinal strain (LALS) (peak-atrial-longitudinal-strain, PALS, mean difference −0.89% P = 0.37; peak-atrial-contraction-strain, PACS, mean difference −0.76%, P = 0.13) and right-ventricle-free-wall-strain (RV-FWS) (mean difference 0.44%, P = 0.59). A stronger agreement between testers was observed for patients with markedly reduced left-ventricle-global-longitudinal-strain (LV-GLS) and was not affected by the quality of acoustic windows. Similar trend was evident for the left atrial and right ventricular parameters. Conclusions New tool AutoStrain LV/RV/LA® for automated strain analysis showed a good agreement between operators with different levels of expertise in the challenging setting of patients with aortic valve stenosis. The reproducibility was particularly accurate for A4C view and in patients with worse LV-GLS values, independently from image quality.

Proteomic mapping of atrial and ventricular heart tissue in patients with aortic valve stenosis

Aortic valve stenosis (AVS) is one of the most common valve diseases in the world. However, detailed biological understanding of the myocardial changes in AVS hearts on the proteome level is still lacking. Proteomic studies using high-resolution mass spectrometry of formalin-fixed and paraffin-embedded (FFPE) human myocardial tissue of AVS-patients are very rare due to methodical issues. To overcome these issues this study used high resolution mass spectrometry in combination with a stem cell-derived cardiac specific protein quantification-standard to profile the proteomes of 17 atrial and 29 left ventricular myocardial FFPE human myocardial tissue samples from AVS-patients. In our proteomic analysis we quantified a median of 1980 (range 1495–2281) proteins in every single sample and identified significant upregulation of 239 proteins in atrial and 54 proteins in ventricular myocardium. We compared the proteins with published data. Well studied proteins reflect disease-related changes in AVS, such as cardiac hypertrophy, development of fibrosis, impairment of mitochondria and downregulated blood supply. In summary, we provide both a workflow for quantitative proteomics of human FFPE heart tissue and a comprehensive proteomic resource for AVS induced changes in the human myocardium.

Moderate or Severe Functional MR and Severe AS: Is Mitral Valve Surgery Justified?

Objective Mitral regurgitation (MR) and severe aortic valve stenosis often coexist. Concomitant replacement of both valves is associated with a significantly higher morbidity and mortality. This study sought to investigate the progression of MR after isolated aortic valve replacement. Methods We analyzed the severity and progression of MR, survival and echocardiographic parameters in 506 patients with severe aortic valve stenosis and moderate to severe functional MR who received isolated aortic valve replacement during a 9-year period. Results Transcatheter aortic valve implantation (TAVI) was performed in 381 patients and 125 patients received surgical aortic valve replacement (SAVR). The median age of the cohort was 82 years. Median ejection fraction before and after TAVI or SAVR was 35 and 36% respectively (p = 0.64). There was a statistically significant reduction in the MR (p < 0.001) within both groups. Survival in both groups at 5 years was at 25%. Conclusion Isolated aortic valve replacement in patients with accompanying moderate to severe functional MR may present an adequate treatment option for this high-risk patient collective.