Modelling and performance of heat pipes with long evaporator sections

This paper presents a planar cooling strategy for advanced electronic applications using heat pipe technology. The principle idea is to use an array of relatively long heat pipes, whereby heat is disposed to a long section of the pipes. The proposed design uses 1 m long heat pipes and top cooling through a fan-based heat sink. Successful heat pipe operation and experimental performances are determined for seven heating configurations, considering active bottom, middle and top sections, and four orientation angles (0°, 30°, 60° and 90°). For all heating sections active, the heat pipe oriented vertically in an evaporator-down mode and a power input of 150 W, the overall thermal resistance was 0.014 K/W at a thermal gradient of 2.1 K and an average operating temperature of 50.7 °C. Vertical operation showed best results, as can be expected; horizontally the heat pipe could not be tested up to the power limit and dry-out occurred between 20 and 80 W depending on the heating configuration. Heating configurations without the bottom section active demonstrated a dynamic start-up effect, caused by heat conduction towards the liquid pool and thereafter batch-wise introducing the working fluid into the two-phase cycle. By analysing the heat pipe limitations for the intended operating conditions, a suitable heat pipe geometry was chosen. To predict the thermal performance a thermal model using a resistance network was created. The model compares well with the measurement data, especially for higher input powers. Finally, the thermal model is used for the design of a 1 kW planar system-level electronics cooling infrastructure featuring six 1 m heat pipes in parallel having a long (~75%) evaporator section

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

Comparing Non-invasive Inverse Electrocardiography With Invasive Endocardial and Epicardial Electroanatomical Mapping During Sinus Rhythm

This study presents a novel non-invasive equivalent dipole layer (EDL) based inverse electrocardiography (iECG) technique which estimates both endocardial and epicardial ventricular activation sequences. We aimed to quantitatively compare our iECG approach with invasive electro-anatomical mapping (EAM) during sinus rhythm with the objective of enabling functional substrate imaging and sudden cardiac death risk stratification in patients with cardiomyopathy. Thirteen patients (77% males, 48 ± 20 years old) referred for endocardial and epicardial EAM underwent 67-electrode body surface potential mapping and CT imaging. The EDL-based iECG approach was improved by mimicking the effects of the His-Purkinje system on ventricular activation. EAM local activation timing (LAT) maps were compared with iECG-LAT maps using absolute differences and Pearson’s correlation coefficient, reported as mean ± standard deviation [95% confidence interval]. The correlation coefficient between iECG-LAT maps and EAM was 0.54 ± 0.19 [0.49–0.59] for epicardial activation, 0.50 ± 0.27 [0.41–0.58] for right ventricular endocardial activation and 0.44 ± 0.29 [0.32–0.56] for left ventricular endocardial activation. The absolute difference in timing between iECG maps and EAM was 17.4 ± 7.2 ms for epicardial maps, 19.5 ± 7.7 ms for right ventricular endocardial maps, 27.9 ± 8.7 ms for left ventricular endocardial maps. The absolute distance between right ventricular endocardial breakthrough sites was 30 ± 16 mm and 31 ± 17 mm for the left ventricle. The absolute distance for latest epicardial activation was median 12.8 [IQR: 2.9–29.3] mm. This first in-human quantitative comparison of iECG and invasive LAT-maps on both the endocardial and epicardial surface during sinus rhythm showed improved agreement, although with considerable absolute difference and moderate correlation coefficient. Non-invasive iECG requires further refinements to facilitate clinical implementation and risk stratification

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

Integrating Exercise Into Personalized Ventricular Arrhythmia Risk Prediction in Arrhythmogenic Right Ventricular Cardiomyopathy

BACKGROUND: Exercise is associated with sustained ventricular arrhythmias (VA) in Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) but is not included in the ARVC risk calculator (arvcrisk.com). The objective of this study is to quantify the influence of exercise at diagnosis on incident VA risk and evaluate whether the risk calculator needs adjustment for exercise. METHODS: We interviewed ARVC patients without sustained VA at diagnosis about their exercise history. The relationship between exercise dose 3 years preceding diagnosis (average METh/wk) and incident VA during follow-up was analyzed with time-to-event analysis. The incremental prognostic value of exercise to the risk calculator was evaluated by Cox models. RESULTS: We included 176 patients (male, 43.2%; age, 37.6±16.1 years) from 3 ARVC centers, of whom 53 (30.1%) developed sustained VA during 5.4 (2.7-9.7) years of follow-up. Exercise at diagnosis showed a dose-dependent nonlinear relationship with VA, with no significant risk increase 18, >24, and >36 METh/wk), was significantly associated with VA (hazard ratios, 2.53-2.91) but was also correlated with risk factors currently in the risk calculator model. Thus, adding athlete status to the model did not change the C index of 0.77 (0.71-0.84) and showed no significant improvement (Akaike information criterion change, <2). CONCLUSIONS: Exercise at diagnosis was dose dependently associated with risk of sustained VA in ARVC patients but only above 15 to 30 METh/wk. Exercise does not appear to have incremental prognostic value over the risk calculator. The ARVC risk calculator can be used accurately in athletic patients without modification

Ordered arrays of multiferroic epitaxial nanostructures

Epitaxial heterostructures combining ferroelectric (FE) and ferromagnetic (FiM) oxides are a possible route to explore coupling mechanisms between the two independent order parameters, polarization and magnetization of the component phases. We report on the fabrication and properties of arrays of hybrid epitaxial nanostructures of FiM NiFe2O4 (NFO) and FE PbZr0.52Ti0.48O3 or PbZr0.2Ti0.8O3, with large range order and lateral dimensions from 200 nm to 1 micron

Risk stratification and subclinical phenotyping of dilated and/or arrhythmogenic cardiomyopathy mutation-positive relatives:CVON eDETECT consortium

In relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy, early detection of disease onset is essential to prevent sudden cardiac death and facilitate early treatment of heart failure. However, the optimal screening interval and combination of diagnostic techniques are unknown. The clinical course of disease in index patients and their relatives is variable due to incomplete and age-dependent penetrance. Several biomarkers, electrocardiographic and imaging (echocardiographic deformation imaging and cardiac magnetic resonance imaging) techniques are promising non-invasive methods for detection of subclinical cardiomyopathy. However, these techniques need optimisation and integration into clinical practice. Furthermore, determining the optimal interval and intensity of cascade screening may require a personalised approach. To address this, the CVON-eDETECT (early detection of disease in cardiomyopathy mutation carriers) consortium aims to integrate electronic health record data from long-term follow-up, diagnostic data sets, tissue and plasma samples in a multidisciplinary biobank environment to provide personalised risk stratification for heart failure and sudden cardiac death. Adequate risk stratification may lead to personalised screening, treatment and optimal timing of implantable cardioverter defibrillator implantation. In this article, we describe non-invasive diagnostic techniques used for detection of subclinical disease in relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy