Life-long tailoring of management for patients with hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease, characterised by complex pathophysiology and extensive genetic and clinical heterogeneity. In most patients, HCM is caused by mutations in cardiac sarcomere protein genes and inherited as an autosomal dominant trait. The clinical phenotype ranges from severe presentations at a young age to lack of left ventricular hypertrophy in genotype-positive individuals. No preventative treatment is available as the sequence and causality of the pathomechanisms that initiate and exacerbate HCM are unknown. Sudden cardiac death and end-stage heart failure are devastating expressions of this disease. Contemporary management including surgical myectomy and implantable cardiac defibrillators has shown significant impact on long-term prognosis. However, timely recognition of specific scenarios – including transition to the end-stage phase – may be challenging due to limited awareness of the progression patterns of HCM. This in turn may lead to missed therapeutic opportunities. To illustrate these difficulties, we describe two HCM patients who progressed from the typical hyperdynamic stage of asymmetric septal thickening to end-stage heart failure with severely reduced ejection fraction. We highlight the different stages of this complex inherited cardiomyopathy based on the clinical staging pro-posed by Olivotto and colleagues. In this way, we aim to provide a practical guide for clinicians and hope to increase awareness for this common form of cardiac disease

Фінанси санаторно-курортних підприємств України

Метою дослідження є аналіз стану санаторно-курортної сфери України як на рівні галузі, так і на рівні окремого санаторно-курортного підприємства

Unraveling the relationships between alpha- and beta-adrenergic modulation and the risk of heart failure

Background: The effects of α and ß adrenergic receptor modulation on the risk of developing heart failure (HF) remains uncertain due to a lack of randomized controlled trials. This study aimed to estimate the effects of α and ß adrenergic receptors modulation on the risk of HF and to provide proof of principle for genetic target validation studies in HF. Methods: Genetic variants within the cis regions encoding the adrenergic receptors α1A, α2B, ß1, and ß2 associated with blood pressure in a 757,601-participant genome-wide association study (GWAS) were selected as instruments to perform a drug target Mendelian randomization study. Effects of these variants on HF risk were derived from the HERMES GWAS (542,362 controls; 40,805 HF cases). Results: Lower α1A or ß1 activity was associated with reduced HF risk: odds ratio (OR) 0.83 (95% CI 0.74–0.93, P = 0.001) and 0.95 (95% CI 0.93–0.97, P = 8 × 10−6). Conversely, lower α2B activity was associated with increased HF risk: OR 1.09 (95% CI 1.05–1.12, P = 3 × 10−7). No evidence of an effect of lower ß2 activity on HF risk was found: OR 0.99 (95% CI 0.92–1.07, P = 0.95). Complementary analyses showed that these effects were consistent with those on left ventricular dimensions and acted independently of any potential effect on coronary artery disease. Conclusions: This study provides genetic evidence that α1A or ß1 receptor inhibition will likely decrease HF risk, while lower α2B activity may increase this risk. Genetic variant analysis can assist with drug development for HF prevention

Automatic Identification of Patients With Unexplained Left Ventricular Hypertrophy in Electronic Health Record Data to Improve Targeted Treatment and Family Screening

Background: Unexplained Left Ventricular Hypertrophy (ULVH) may be caused by genetic and non-genetic etiologies (e.g., sarcomere variants, cardiac amyloid, or Anderson-Fabry's disease). Identification of ULVH patients allows for early targeted treatment and family screening. Aim: To automatically identify patients with ULVH in electronic health record (EHR) data using two computer methods: text-mining and machine learning (ML). Methods: Adults with echocardiographic measurement of interventricular septum thickness (IVSt) were included. A text-mining algorithm was developed to identify patients with ULVH. An ML algorithm including a variety of clinical, ECG and echocardiographic data was trained and tested in an 80/20% split. Clinical diagnosis of ULVH was considered the gold standard. Misclassifications were reviewed by an experienced cardiologist. Sensitivity, specificity, positive, and negative likelihood ratios (LHR+ and LHR-) of both text-mining and ML were reported. Results: In total, 26,954 subjects (median age 61 years, 55% male) were included. ULVH was diagnosed in 204/26,954 (0.8%) patients, of which 56 had amyloidosis and two Anderson-Fabry Disease. Text-mining flagged 8,192 patients with possible ULVH, of whom 159 were true positives (sensitivity, specificity, LHR+, and LHR- of 0.78, 0.67, 2.36, and 0.33). Machine learning resulted in a sensitivity, specificity, LHR+, and LHR- of 0.32, 0.99, 32, and 0.68, respectively. Pivotal variables included IVSt, systolic blood pressure, and age. Conclusions: Automatic identification of patients with ULVH is possible with both Text-mining and ML. Text-mining may be a comprehensive scaffold but can be less specific than machine learning. Deployment of either method depends on existing infrastructures and clinical applications

До мінералогії сезонних сульфатів мису Фіолент (Південно-Західний Крим)

Комплексом методів вивчено колекцію зразків вторинних мінералів одного з узбережних відслонень зони окиснення сульфідної мінералізації мису Фіолент (Південно-Західний Крим). Установлено, що всі досліджені зразки є полімінеральними утвореннями, в яких одночасно співіснують у різних пропорціях сульфати Mg, Al, Fe²⁺, Fe³⁺, Ca тощо: пікерингіт (найпоширеніший), пікерингіт залізистий, гексагідрит, старкіїт, епсоміт, алуноген, ботріоген, копіапіт, ярозит, гіпс та ін. Старкіїт і ботріоген у Криму виявлено вперше.The collection of secondary minerals from one of littoral occurrences of sulphide zone of oxidation of the Fiolent Cape (South-Western Crimea) is studied by different methods. It was established that all studied samples were polymineral formations which consisted of sulphates of Mg, Al, Fe²⁺, Fe³⁺, Ca, etc. in different proportions: pickeringite (the most wide-spread), ferropickeringite, hexahydrite, starkeyite, epsomite, alunogen, botryogen, copiapite, jarosite, gypsum etc. Starkeyite and botryogen are detected in the Crimea for the first time

Modeling the His-Purkinje Effect in Non-invasive Estimation of Endocardial and Epicardial Ventricular Activation

Inverse electrocardiography (iECG) estimates epi- and endocardial electrical activity from body surface potentials maps (BSPM). In individuals at risk for cardiomyopathy, non-invasive estimation of normal ventricular activation may provide valuable information to aid risk stratification to prevent sudden cardiac death. However, multiple simultaneous activation wavefronts initiated by the His-Purkinje system, severely complicate iECG. To improve the estimation of normal ventricular activation, the iECG method should accurately mimic the effect of the His-Purkinje system, which is not taken into account in the previously published multi-focal iECG. Therefore, we introduce the novel multi-wave iECG method and report on its performance. Multi-wave iECG and multi-focal iECG were tested in four patients undergoing invasive electro-anatomical mapping during normal ventricular activation. In each subject, 67-electrode BSPM were recorded and used as input for both iECG methods. The iECG and invasive local activation timing (LAT) maps were compared. Median epicardial inter-map correlation coefficient (CC) between invasive LAT maps and estimated multi-wave iECG versus multi-focal iECG was 0.61 versus 0.31. Endocardial inter-map CC was 0.54 respectively 0.22. Modeling the His-Purkinje system resulted in a physiologically realistic and robust non-invasive estimation of normal ventricular activation, which might enable the early detection of cardiac disease during normal sinus rhythm

Cardiovascular Risk Associated with Interactions among Polymorphisms in Genes from the Renin-Angiotensin, Bradykinin, and Fibrinolytic Systems

Vascular fibrinolytic balance is maintained primarily by interplay of tissue plasminogen activator (t-PA) and plasminogen activator inhibitor type 1 (PAI-1). Previous research has shown that polymorphisms in genes from the renin-angiotensin (RA), bradykinin, and fibrinolytic systems affect plasma concentrations of both t-PA and PAI-1 through a set of gene-gene interactions. In the present study, we extend this finding by exploring the effects of polymorphisms in genes from these systems on incident cardiovascular disease, explicitly examining two-way interactions in a large population-based study