Abstract Background BrS is an inherited sudden cardiac death syndrome. Less than 35% of BrS probands have genetically identified pathogenic variants. Recent evidence has implicated SCN10A , a ...neuronal sodium channel gene encoding Nav 1.8, in the electrical function of the heart. Objectives The purpose of this study was to test the hypothesis that SCN10A variants contribute to the development of Brugada syndrome (BrS). Methods Clinical analysis and direct sequencing of BrS susceptibility genes were performed for 150 probands and family members as well as >200 healthy controls. Expression and coimmunoprecipitation studies were performed to functionally characterize the putative pathogenic mutations. Results We identified 17 SCN10A mutations in 25 probands (20 male and 5 female); 23 of the 25 probands (92.0%) displayed overlapping phenotypes. SCN10A mutations were found in 16.7% of BrS probands, approaching our yield for SCN5A mutations (20.1%). Patients with BrS who had SCN10A mutations were more symptomatic and displayed significantly longer PR and QRS intervals compared with SCN10A -negative BrS probands. The majority of mutations localized to the transmembrane-spanning regions. Heterologous coexpression of wild-type (WT) SCN10A with WT- SCN5A in HEK cells caused a near doubling of sodium channel current compared with WT -SCN5A alone. In contrast, coexpression of SCN10A mutants (R14L and R1268Q) with WT- SCN5A caused a 79.4% and 84.4% reduction in sodium channel current, respectively. The coimmunoprecipitation studies provided evidence for the coassociation of Nav 1.8 and Nav 1.5 in the plasma membrane. Conclusions Our study identified SCN10A as a major susceptibility gene for BrS, thus greatly enhancing our ability to genotype and risk stratify probands and family members.
Direct His-bundle pacing (DHBP) produces rapid sequential multisite synchronous ventricular activation and, therefore, would be an ideal alternative to right ventricular apical (RVA) pacing. In 54 ...patients with cardiomyopathy, ejection fraction (EF) 0.23 +/- 0.11, persistent atrial fibrillation, and normal QRS < 120 ms. DHBP was attempted. This was successful in 39 patients. In seven patients, the effect of increasing heart rate on contractility (Treppe effect) was investigated. Twelve patients who also received a RVA lead underwent cardiopulmonary testing. After a mean follow-up of 42 months, 29 patients are still alive with EF improving from 0.23 +/- 0.11 to 0.33 +/- 0.15. Functional class improved from 3.5 to 2.2. DP/dt increased at each pacing site (P < 0.05) as the heart rate increased to 60, 100, and 120 beats/min. Rise in dP/dt by DHBP pacing at 120 beats/min was at least 170 +/- mmHg/s, greater than any other site in the ventricle (P < 0.05). Cardiopulmonary testing revealed longer exercise time (RVA 255 +/- 110 s) (His 280 +/- 104 s) (P < 0.05), higher O2 uptake (RVA 15 +/- 4 mL/kg per minute) (His 16 +/- 4 mL/kg minute) (P < 0.05), and later anaerobic threshold (RVA 126 +/- 71 s) (His 145 +/- 74 s) (P < 0.05) with DHBP compared to RVA pacing. Long-term DHBP is safe and effective in humans. DHBP is associated with a superior Treppe effect and increased cardiopulmonary reserve when compared to RVA pacing.
Abstract Direct His bundle pacing provides the most physiologic means of artificial pacing of the ventricles with a preserved His-Purkinje system and may play a role in patients with a diseased ...intrinsic conduction system. We describe our initial motivations and experience with permanent direct His bundle pacing and important lessons learned since that time.
In addition to the His bundle, numerous other sites have been evaluated as more physiologic alternatives to pacing at the right ventricular apex. Several hemodynamic studies have shown the benefit of ...His bundle pacing and septal pacing in comparison with right ventricular apical pacing. This article summarizes this literature and presents acute hemodynamic data in an intrapatient study examining His bundle pacing, right ventricular septal pacing, and right ventricular apical pacing.
This article summarizes the initial experience with permanent His bundle pacing, the lessons learned, and the concepts that have been developed in the subsequent decade of experience with His bundle ...pacing. This article also addresses the advancements in technology, which have allowed His bundle pacing to be more widely adopted and used in various clinical situations.
Accurate classification of breast cancer from the histopathology images poses a difficult task because of various benign breast tissue proliferative lesions and heterogeneity of abnormal cell growth. ...Various breast cancer classification methods are adopted in recent decades to categorize the breast cancer from histopathology images, but generating accurate classification result poses a complex task in medical image analysis system. Therefore, an accurate breast cancer classification method named Shuffled Shepherd Deer Hunting Optimization-based Deep Neural Network (SSDHO-based DNN) is designed for classifying the breast tumor images into six different classes, like non-tubule, non-tumor nuclei, tubule, apoptosis, tumor nuclei, and mitosis. The proposed algorithm named SSDHO is modelled by merging the Shuffled Shepherd Optimization Algorithm (SSOA) and Deer Hunting Optimization Algorithm (DHOA). Here, the feature, like statistical features, shape features and Convolutional Neural Network (CNN) features are effectively mined from the segmented blood cells such that these extracted features make the classification process more effective using DNN. By employing DNN, the breast cancer classification process is achieved more efficiently with their associated hidden neurons and generates the classification result more accurately. The devised approach obtained maximum results in terms of accuracy, precision, sensitivity, and specificity by acquiring the values of 0.9561, 0.8232, 0.7903, and 0.9426, respectively.
Breast cancer is one of the substantial diseases that affect millions of females each year also the velocity of affected individuals is rising every year. Timely recognition of the illness is the ...only feasible solution to reduce its influence of the disease. Numerous techniques are invented by researchers in support of the determination of breast cancer and the usage of histopathology descriptions provided the auspicious solution. As an enhancement, in this research, a Deer-Canid based deep CNN is implemented by means of the histopathology images used for the detection of breast cancer through the taxonomy of benign, malignant, and normal regions. The segmentation of the histopathology images is performed using the V-net architecture that segments the image without losing its originality. The primary involvement of the research relies on the Deer-Canid optimization that helps in attaining the global best solution and effectively minimizes the time taken for the classification. The superiority of the research is proved by measuring the values of accuracy, precision, recall, and f1 measure, and the proposed Deer Canid optimization-based deep CNN attained the values of 92.967%, 94.342%, 93.454%, 92.896%, which is more efficient.
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