Accurately Determining the Phase Transition Temperature of CsPbI3 via Random-Phase Approximation Calculations and Phase-Transferable Machine Learning Potentials

While metal halide perovskites (MHPs) have shown great potential for various optoelectronic applications, their widespread adoption in commercial photovoltaic cells or photo-sensors is currently restricted, given that MHPs such as CsPbI3 and FAPbI(3) spontaneously transition to an optically inactive non-perovskite phase at ambient conditions. Herein, we put forward an accurate first-principles procedure to obtain fundamental insight into this phase stability conundrum. To this end, we computationally predict the Helmholtz free energy, composed of the electronic ground state energy and thermal corrections, as this is the fundamental quantity describing the phase stability in polymorphic materials. By adopting the random phase approximation method as a wave function-based method that intrinsically accounts for many-body electron correlation effects as a benchmark for the ground state energy, we validate the performance of different exchange-correlation functionals and dispersion methods. The thermal corrections, accessed through the vibrational density of states, are accessed through molecular dynamics simulations, using a phase-transferable machine learning potential to accurately account for the MHPs' anharmonicity and mitigate size effects. The here proposed procedure is critically validated on CsPbI3, which is a challenging material as its phase stability changes slowly with varying temperature. We demonstrate that our procedure is essential to reproduce the experimental transition temperature, as choosing an inadequate functional can easily miss the transition temperature by more than 100 K. These results demonstrate that the here validated methodology is ideally suited to understand how factors such as strain engineering, surface functionalization, or compositional engineering could help to phase-stabilize MHPs for targeted applications

Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers

Echocardiographic deformation curves provide detailed information on myocardial function. Deep neural networks (DNNs) may enable automated detection of disease features in deformation curves, and improve the clinical assessment of these curves. We aimed to investigate whether an explainable DNN-based pipeline can be used to detect and visualize disease features in echocardiographic deformation curves of phospholamban (PLN) p.Arg14del variant carriers. A DNN was trained to discriminate PLN variant carriers (n = 278) from control subjects (n = 621) using raw deformation curves obtained by 2D-speckle tracking in the longitudinal axis. A visualization technique was used to identify the parts of these curves that were used by the DNN for classification. The PLN variant carriers were clustered according to the output of the visualization technique. The DNN showed excellent discriminatory performance (C-statistic 0.93 [95% CI 0.87–0.97]). We identified four clusters with PLN-associated disease features in the deformation curves. Two clusters showed previously described features: apical post-systolic shortening and reduced systolic strain. The two other clusters revealed novel features, both reflecting delayed relaxation. Additionally, a fifth cluster was identified containing variant carriers without disease features in the deformation curves, who were classified as controls by the DNN. This latter cluster had a very benign disease course regarding development of ventricular arrhythmias. Applying an explainable DNN-based pipeline to myocardial deformation curves enables automated detection and visualization of disease features. In PLN variant carriers, we discovered novel disease features which may improve individual risk stratification. Applying this approach to other diseases will further expand our knowledge on disease-specific deformation patterns. Graphical abstract: [Figure not available: see fulltext.] Overview of the deep neural network-based pipeline for feature detection in myocardial deformation curves. Firstly, phospholamban (PLN) p.Arg14del variant carriers and controls were selected and a deep neural network (DNN) was trained to detect the PLN variant carriers. Subsequently, a clustering-based approach was performed on the attention maps of the DNN, which revealed 4 distinct phenotypes of PLN variant carriers with different prognoses. Moreover, a cluster without features and a benign prognosis was detected

Exercise does not influence development of phenotype in PLN p.(Arg14del) cardiomyopathy

BACKGROUND: Endurance and frequent exercise are associated with earlier onset of arrhythmogenic right ventricular cardiomyopathy (ARVC) and ventricular arrhythmias (VA) in desmosomal gene variant carriers. Individuals with the pathogenic c.40_42del; p.(Arg14del) variant in the PLN gene are frequently diagnosed with ARVC or dilated cardiomyopathy (DCM). The aim of this study was to evaluate the effect of exercise in PLN p.(Arg14del) carriers. METHODS: In total, 207 adult PLN p.(Arg14del) carriers (39.1% male; mean age 53 ± 15 years) were interviewed on their regular physical activity since the age of 10 years. The association of exercise with diagnosis of ARVC, DCM, sustained VA and hospitalisation for heart failure (HF) was studied. RESULTS: Individuals participated in regular physical activities with a median of 1661 metabolic equivalent of task (MET) hours per year (31.9 MET-hours per week) until clinical presentation. The 50% most and least active individuals had a similar frequency of sustained VA (18.3% vs 18.4%; p = 0.974) and hospitalisation for HF (9.6% vs 8.7%; p = 0.827). There was no relationship between exercise and survival free from (incident) sustained VA (p = 0.65), hospitalisation for HF (p = 0.81), diagnosis of ARVC (p = 0.67) or DCM (p = 0.39) during follow-up. In multivariate analyses, exercise was not associated with sustained VA or HF hospitalisation during follow-up in this relatively not-active cohort. CONCLUSION: There was no association between the amount of exercise and the susceptibility to develop ARVC, DCM, VA or HF in PLN p.(Arg14del) carriers. This suggested unaffected PLN p.(Arg14del) carriers can safely perform mild-moderate exercise, in contrast to desmosomal variant carriers and ARVC patients

Hospital utilisation and the costs associated with complications of ICD implantation in a contemporary primary prevention cohort

Introduction: Implantation of an implantable cardioverter defibrillator (ICD) is standard care for primary prevention of sudden cardiac death. However, ICD-related complications are increasing as the population of ICD recipients grows. Methods: ICD-related complications in a national DO-IT Registry cohort of 1442 primary prevention ICD patients were assessed in terms of additional use of hospital care resources and costs. Results: During a median follow-up of 28.7 months (IQR 25.2–33.7) one or more complications occurred in 13.5% of patients. A complication resulted in a surgical intervention in 53% of cases and required on average 3.65 additional hospital days. The additional hospital costs were €6,876 per complication or €8,110 per patient, to which clinical re-interventions and additional hospital days contributed most. Per category of complications, infections required most hospital utilisation and were most expensive at an average of €22,892. The mean costs were €5,800 for lead-related complications, €2,291 for pocket-related complications and €5,619 for complications due to other causes. We estimate that the total yearly incidence-based costs in the Netherlands for hospital management of ICD-related complications following ICD implantation for primary prevention are €2.7 million. Conclusion: Complications following ICD implantation are related to a substantial additional need for hospital resources. When performing cost-effectiveness analyses of ICD implantation, including the costs associated with complications, one should be aware that real-world complication rates may deviate from trial data. Considering the economic implications, strategies to reduce the incidence of complications are encouraged.</p

Distributed Multipoles from a Robust Basis-Space Implementation of the Iterated Stockholder Atoms Procedure

The recently developed iterated stockholder atoms (ISA) approach of Lillestolen and Wheatley (<i>Chem. Commun.</i> <b>2008</b>, 5909) offers a powerful method for defining atoms in a molecule. However, the real-space algorithm is known to converge very slowly, if at all. Here, we present a robust, basis-space algorithm of the ISA method and demonstrate its applicability on a variety of systems. We show that this algorithm exhibits rapid convergence (taking around 10–80 iterations) with the number of iterations needed being unrelated to the system size or basis set used. Further, we show that the multipole moments calculated using this basis-space ISA method are as good as, or better than, those obtained from Stone’s distributed multipole analysis (<i>J. Chem. Theory Comput.</i> <b>2005</b>, <i>1</i>, 1128), exhibiting better convergence properties and resulting in better behaved penetration energies. This can have significant consequences in the development of intermolecular interaction models

Prediction of ventricular arrhythmia in phospholamban p.Arg14del mutation carriers–reaching the frontiers of individual risk prediction

Aims: This study aims to improve risk stratification for primary prevention implantable cardioverter defibrillator (ICD) implantation by developing a new mutation-specific prediction model for malignant ventricular arrhythmia (VA) in phospholamban (PLN) p.Arg14del mutation carriers. The proposed model is compared to an existing PLN risk model. / Methods and results: Data were collected from PLN p.Arg14del mutation carriers with no history of malignant VA at baseline, identified between 2009 and 2020. Malignant VA was defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. A prediction model was developed using Cox regression. The study cohort consisted of 679 PLN p.Arg14del mutation carriers, with a minority of index patients (17%) and male sex (43%), and a median age of 42 years [interquartile range (IQR) 27-55]. During a median follow-up of 4.3 years (IQR 1.7-7.4), 72 (10.6%) carriers experienced malignant VA. Significant predictors were left ventricular ejection fraction, premature ventricular contraction count/24 h, amount of negative T waves, and presence of low-voltage electrocardiogram. The multivariable model had an excellent discriminative ability {C-statistic 0.83 [95% confidence interval (CI) 0.78-0.88]}. Applying the existing PLN risk model to the complete cohort yielded a C-statistic of 0.68 (95% CI 0.61-0.75). / Conclusion: This new mutation-specific prediction model for individual VA risk in PLN p.Arg14del mutation carriers is superior to the existing PLN risk model, suggesting that risk prediction using mutation-specific phenotypic features can improve accuracy compared to a more generic approach

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Dutch outcome in implantable cardioverter-defibrillator therapy (DO-IT)

Background Implantable cardioverter-defibrillators (ICDs) are widely used for the prevention of sudden cardiac death. At present, both clinical benefit and cost-effectiveness of ICD therapy in primary prevention patients are topics of discussion, as only a minority of these patients will eventually receive appropriate ICD therapy. Methods/design The DO-IT Registry is a nationwide prospective cohort with a target enrolment of 1,500 primary prevention ICD patients with reduced left ventricular function in a setting of structural heart disease. The primary outcome measures are death and appropriate ICD therapy for ventricular tachyarrhythmias. Secondary outcome measures are inappropriate ICD therapy, death of any cause, hospitalisation for ICD related complications and for cardiovascular reasons. As of December 2016, data on demographic, clinical, and ICD characteristics of 1,468 patients have been collected. Follow-up will continue up to 24 months after inclusion of the last patient. During follow-up, clinical and ICD data are collected based on the normal follow-up of these patients, assuming ICD interrogations take place every six months and clinical follow-up i