Imaging data in COVID-19 patients: focused on echocardiographic findings

To assess imaging data in COVID-19 patients and its association with clinical course and survival and 86 consecutive patients (52 males, 34 females, mean age = 58.8 year) with documented COVID-19 infection were included. Seventy-eight patients (91) were in severe stage of the disease. All patients underwent transthoracic echocardiography. Mean LVEF was 48.1 and mean estimated systolic pulmonary artery pressure (sPAP) was 27.9 mmHg. LV diastolic dysfunction was mildly abnormal in 49 patients (57.6) and moderately abnormal in 7 cases (8.2). Pericardial effusion was present in 5/86 (minimal in size in 3 cases and mild- moderate in 2). In 32/86 cases (37.2), the severity of infection progressed from �severe� to �critical�. Eleven patients (12.8) died. sPAP and computed tomography score were associated with disease progression (P value = 0.002, 0.002 respectively). Tricuspid annular plane systolic excursion (TAPSE) was significantly higher in patients with no disease progression compared with those who deteriorated (P value = 0.005). Pericardial effusion (minimal, mild or moderate) was detected more often in progressive disease (P = 0.03). sPAP was significantly lower among survivors (P value = 0.007). Echocardiographic findings (including systolic PAP, TAPSE and pericardial effusion), total CT score may have prognostic and therapeutic implication in COVID-19 patients. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature

Organic Embedded Architecture For Sustainable Fpga Soft-Core Processors

Mission-critical systems require increasing capability for fault handling and self-adaptation as their system complexities and inter-dependencies increase. Organic Computing (OC) architectures utilize biologically-inspired self-x properties which include self-configuration, self-reorganization, and self-healing which comprise the focus of this paper. To provide OC architectures with sufficient capability for exhibiting self-adaptive behavior, reconfigurable logic devices offer a suitable hardware platform. SRAM-based Field Programmable Gate Array (FPGA) logic devices can realize self-adaptation within their reconfigurable logic fabric using Evolvable Hardware techniques based on crossover, mutation, and iterative selection with intrinsic fitness assessment of the underlying hardware resources. In this paper, a dual-layer Organic Computing architecture called the Organic Embedded System (OES) is prototyped on a Xilinx FPGA reconfigurable fabric and assessed for maintainability metrics of completeness of repair, repair time, and degraded throughput during the repair phase. The approach used extends a widely known generic OC platform consisting of two layers: the Functional Layer and the Autonomic Layer. The Autonomic layer contains Autonomic Elements (AEs) that are responsible for correct operation of the corresponding Functional Elements (FEs) present on the Functional Layer. Innovations include autonomously degraded online throughput during regeneration, spare configuration aging and outlier driven repair assessment, and a uniform design for AEs despite the fact that they monitor different types of FEs. Using the OES approach; a malfunctioning or faulty AE among the population can be distinguished by its discrepant performance. The OES approach is implemented using high-level Hardware Description Language (HDL) which directs a Supervisor Element (SE) to function as a fault management unit through the collection of AE information. Experimental results show that the OES Autonomic Layer demonstrates 100% faulty component isolation for both FEs and AEs with randomly injected single faults. Using logic circuits from the MCNC-91 benchmark test set, throughput during repair phases averaged 75.05%, 82.21%, and 65.21% for the z4ml (2-bit adder), cm85a (high fan-in combinational logic), and cm138a (balanced I/O combinational logic) circuits respectively under stated conditions

Entropy-Based Modeling for Estimating Adversarial Bit-flip Attack Impact on Binarized Neural Network

Over past years, the high demand to efficiently process deep learning (DL) models has driven the market of the chip design companies. However, the new Deep Chip architectures, a common term to refer to DL hardware accelerator, have slightly paid attention to the security requirements in quantized neural networks (QNNs), while the black/white -box adversarial attacks can jeopardize the integrity of the inference accelerator. Therefore in this paper, a comprehensive study of the resiliency of QNN topologies to black-box attacks is examined. Herein, different attack scenarios are performed on an FPGA-processor co-design, and the collected results are extensively analyzed to give an estimation of the impact\u27s degree of different types of attacks on the QNN topology. To be specific, we evaluated the sensitivity of the QNN accelerator to a range number of bitflip attacks (BFAs) that might occur in the operational lifetime of the device. The BFAs are injected at uniformly distributed times either across the entire QNN or per individual layer during the image classification. The acquired results are utilized to build the entropy-based model that can be leveraged to construct resilient QNN architectures to bit-flip attacks

Differences between familial and sporadic dilated cardiomyopathy: ESC EORP Cardiomyopathy & Myocarditis registry

Aims: Dilated cardiomyopathy (DCM) is a complex disease where genetics interplay with extrinsic factors. This study aims to compare the phenotype, management, and outcome of familial DCM (FDCM) and non-familial (sporadic) DCM (SDCM) across Europe. Methods and results: Patients with DCM that were enrolled in the prospective ESC EORP Cardiomyopathy & Myocarditis Registry were included. Baseline characteristics, genetic testing, genetic yield, and outcome were analysed comparing FDCM and SDCM; 1260 adult patients were studied (238 FDCM, 707 SDCM, and 315 not disclosed). Patients with FDCM were younger (P\ua0<\ua00.01), had less severe disease phenotype at presentation (P\ua0<\ua00.02), more favourable baseline cardiovascular risk profiles (P\ua0 64\ua00.007), and less medication use (P\ua0 64\ua00.042). Outcome at 1\ua0year was similar and predicted by NYHA class (HR 0.45; 95% CI [0.25\u20130.81]) and LVEF per % decrease (HR 1.05; 95% CI [1.02\u20131.08]. Throughout Europe, patients with FDCM received more genetic testing (47% vs. 8%, P\ua0<\ua00.01) and had higher genetic yield (55% vs. 22%, P\ua0<\ua00.01). Conclusions: We observed that FDCM and SDCM have significant differences at baseline but similar short-term prognosis. Whether modification of associated cardiovascular risk factors provide opportunities for treatment remains to be investigated. Our results also show a prevalent role of genetics in FDCM and a non-marginal yield in SDCM although genetic testing is largely neglected in SDCM. Limited genetic testing and heterogeneity in panels provides a scaffold for improvement of guideline adherence