Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks

Correct interpretation of the electrocardiogram (ECG) is critical for the diagnosis of many cardiac diseases, and current computerized algorithms are not accurate enough to provide automated comprehensive interpretation of the ECG. This study aimed to develop and validate the use of a pre-trained exponentially dilated causal convolutional neural network for interpretation of the ECG as part of the 2020 Physionet/Computing in Cardiology Challenge. The network was pre-trained on a physician-annotated dataset of 254,044 12-lead ECGs. The weights of the pre-trained network were partially frozen, and the others were finetuned on the challenge dataset of 42,511 ECGs. 10-fold cross-validation was applied and the best performing model in each fold was selected and used to construct an ensemble. The proposed method yielded a cross-validated area under the receiver operating curve (AU-ROC) of 0.939 ± 0.004 and a challenge score of 0.565 ± 0.005. Evaluation on the hidden test set resulted in a score of 0.417, placing us 7th out of 41 in the official ranking (team name UMCUVA). We demonstrated that an ensemble of exponentially dilated causal convolutional networks and pre-training on a large dataset of ECGs from a different country and device manufacturer performs excellent for interpretation of ECGs

Functional characterization of infiltrating T lymphocytes in human hepatic allografts

We have employed recently developed techniques in T-cell culturing to study the nature and function of infiltrating hepatic allograft T cells. Using the rationale that intragraft T cells are activated during cell mediated damage to the allograft, we were able to show that these cells would propagate and remain functionally active in the presence of the T-cell growth factor, IL-2. In several instances, phenotyiic analysis of cells grown in this manner was very similar to that found within the graft. Both proliferative and cytotoxic responses could be detected from the cultured cell lines. The majority of the proliferative responses were donor-directed and immunogenetic analysis could define donor-directed HLA reactivity, to either class I or class II antigens, or both. Monoclonal anti-HLA antibodies inhibition profiles verified the apparent HLA reactivity. In a smaller percentage of cases, only IL-2 responsiveness could be detected, and no HLA reactivity could be determined. Cytotoxicity could be detected against both class I and class II antigens, however, those cells which demonstrated a greater magnitude of donor-directed cytotoxicity appeared to be directed against class I antigens. A significant correlation between donor-directed proliferation of biopsy cultured lymphocytes and cellular rejection was found. This model appears to be useful in delineating functions of the intragraft T-cell population during rejection. © 1986

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

Purpose of ReviewAs machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease.Recent Findings and SummaryThere is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies.</p

High Resolution Systematic Digital Histological Quantification of Cardiac Fibrosis and Adipose Tissue in Phospholamban p.Arg14del Mutation Associated Cardiomyopathy

Myocardial fibrosis can lead to heart failure and act as a substrate for cardiac arrhythmias. In dilated cardiomyopathy diffuse interstitial reactive fibrosis can be observed, whereas arrhythmogenic cardiomyopathy is characterized by fibrofatty replacement in predominantly the right ventricle. The p.Arg14del mutation in the phospholamban (PLN) gene has been associated with dilated cardiomyopathy and recently also with arrhythmogenic cardiomyopathy. Aim of the present study is to determine the exact pattern of fibrosis and fatty replacement in PLN p.Arg14del mutation positive patients, with a novel method for high resolution systematic digital histological quantification of fibrosis and fatty tissue in cardiac tissue. Transversal mid-ventricular slices (n = 8) from whole hearts were collected from patients with the PLN p.Arg14del mutation (age 48±16 years; 4 (50%) male). An in-house developed open source MATLAB script was used for digital analysis of Masson's trichrome stained slides (http://sourceforge.net/projects/fibroquant/). Slides were divided into trabecular, inner and outer compact myocardium. Per region the percentage of connective tissue, cardiomyocytes and fatty tissue was quantified. In PLN p.Arg14del mutation associated cardiomyopathy, myocardial fibrosis is predominantly present in the left posterolateral wall and to a lesser extent in the right ventricular wall, whereas fatty changes are more pronounced in the right ventricular wall. No difference in distribution pattern of fibrosis and adipocytes was observed between patients with a clinical predominantly dilated and arrhythmogenic cardiomyopathy phenotype. In the future, this novel method for quantifying fibrosis and fatty tissue can be used to assess cardiac fibrosis and fatty tissue in animal models and a broad range of human cardiomyopathies

Modelling the Dynamics of Feral Alfalfa Populations and Its Management Implications

BACKGROUND: Feral populations of cultivated crops can pose challenges to novel trait confinement within agricultural landscapes. Simulation models can be helpful in investigating the underlying dynamics of feral populations and determining suitable management options. METHODOLOGY/PRINCIPAL FINDINGS: We developed a stage-structured matrix population model for roadside feral alfalfa populations occurring in southern Manitoba, Canada. The model accounted for the existence of density-dependence and recruitment subsidy in feral populations. We used the model to investigate the long-term dynamics of feral alfalfa populations, and to evaluate the effectiveness of simulated management strategies such as herbicide application and mowing in controlling feral alfalfa. Results suggest that alfalfa populations occurring in roadside habitats can be persistent and less likely to go extinct under current roadverge management scenarios. Management attempts focused on controlling adult plants alone can be counterproductive due to the presence of density-dependent effects. Targeted herbicide application, which can achieve complete control of seedlings, rosettes and established plants, will be an effective strategy, but the seedbank population may contribute to new recruits. In regions where roadside mowing is regularly practiced, devising a timely mowing strategy (early- to mid-August for southern Manitoba), one that can totally prevent seed production, will be a feasible option for managing feral alfalfa populations. CONCLUSIONS/SIGNIFICANCE: Feral alfalfa populations can be persistent in roadside habitats. Timely mowing or regular targeted herbicide application will be effective in managing feral alfalfa populations and limit feral-population-mediated gene flow in alfalfa. However, in the context of novel trait confinement, the extent to which feral alfalfa populations need to be managed will be dictated by the tolerance levels established by specific production systems for specific traits. The modelling framework outlined in this paper could be applied to other perennial herbaceous plants with similar life-history characteristics

Evaluation of patients with a recent clinical fracture and osteoporosis, a multidisciplinary approach

The aetiology of osteoporotic fractures is multifactorial, but little is known about the way to evaluate patients with a recent clinical fracture for the presence of secondary osteoporosis

Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks

AIMS: While electrocardiogram (ECG) characteristics have been associated with life-threatening ventricular arrhythmias (LTVA) in dilated cardiomyopathy (DCM), they typically rely on human-derived parameters. Deep neural networks (DNNs) can discover complex ECG patterns, but the interpretation is hampered by their 'black-box' characteristics. We aimed to detect DCM patients at risk of LTVA using an inherently explainable DNN. METHODS AND RESULTS: In this two-phase study, we first developed a variational autoencoder DNN on more than 1 million 12-lead median beat ECGs, compressing the ECG into 21 different factors (F): FactorECG. Next, we used two cohorts with a combined total of 695 DCM patients and entered these factors in a Cox regression for the composite LTVA outcome, which was defined as sudden cardiac arrest, spontaneous sustained ventricular tachycardia, or implantable cardioverter-defibrillator treated ventricular arrhythmia. Most patients were male (n = 442, 64%) with a median age of 54 years [interquartile range (IQR) 44-62], and median left ventricular ejection fraction of 30% (IQR 23-39). A total of 115 patients (16.5%) reached the study outcome. Factors F8 (prolonged PR-interval and P-wave duration, P < 0.005), F15 (reduced P-wave height, P = 0.04), F25 (increased right bundle branch delay, P = 0.02), F27 (P-wave axis P < 0.005), and F32 (reduced QRS-T voltages P = 0.03) were significantly associated with LTVA. CONCLUSION: Inherently explainable DNNs can detect patients at risk of LTVA which is mainly driven by P-wave abnormalities

Rare Species Support Vulnerable Functions in High-Diversity Ecosystems

Around the world, the human-induced collapses of populations and species have triggered a sixth mass extinction crisis, with rare species often being the first to disappear. Although the role of species diversity in the maintenance of ecosystem processes has been widely investigated, the role of rare species remains controversial. A critical issue is whether common species insure against the loss of functions supported by rare species. This issue is even more critical in species-rich ecosystems where high functional redundancy among species is likely and where it is thus often assumed that ecosystem functioning is buffered against species loss. Here, using extensive datasets of species occurrences and functional traits from three highly diverse ecosystems (846 coral reef fishes, 2,979 alpine plants, and 662 tropical trees), we demonstrate that the most distinct combinations of traits are supported predominantly by rare species both in terms of local abundance and regional occupancy. Moreover, species that have low functional redundancy and are likely to support the most vulnerable functions, with no other species carrying similar combinations of traits, are rarer than expected by chance in all three ecosystems. For instance, 63% and 98% of fish species that are likely to support highly vulnerable functions in coral reef ecosystems are locally and regionally rare, respectively. For alpine plants, 32% and 89% of such species are locally and regionally rare, respectively. Remarkably, 47% of fish species and 55% of tropical tree species that are likely to support highly vulnerable functions have only one individual per sample on average. Our results emphasize the importance of rare species conservation, even in highly diverse ecosystems, which are thought to exhibit high functional redundancy. Rare species offer more than aesthetic, cultural, or taxonomic diversity value; they disproportionately increase the potential breadth of functions provided by ecosystems across spatial scales. As such, they are likely to insure against future uncertainty arising from climate change and the ever-increasing anthropogenic pressures on ecosystems. Our results call for a more detailed understanding of the role of rarity and functional vulnerability in ecosystem functioning