Fontan Hepatopathy-Managing Unknowns.

BACKGROUND AND AIMS: How to best monitor Fontan-associated liver disease (FALD) remains unclear. We describe results from a prospective liver care pathway in adults (n=84) with a Fontan circulation. METHODS: Routine assessment of the liver, by acoustic radiation force frequency and ultrasound was undertaken. Results, including liver biochemistry, systemic ventricular function (echocardiography), functional class, medication use and clinical endpoints (varices, hepatocellular carcinoma, heart transplantation and death) were collated. RESULTS: Most individuals returned a cirrhotic range acoustic radiation force impulse imaging (ARFI) result. ARFI values were greater in the proportion of individuals with hepatic nodularity (p=0.024). Univariate analysis demonstrated moderate correlation with platelet number (Spearmans rho= -0.376, p=0.049). Patients with clinical endpoints had lower platelets (p=0.012) but only a trend to hepatic nodularity (p=0.057). Clinical endpoints were more common in those with ventricular dysfunction (p=0.011). Multivariate analysis revealed that age at Fontan and being on angiotensin converting enzyme inhibitors (ACEI) predicted ARFI score (ß=0.06 [95% CI 0.01-0.09], p=0.007 and ß=0.53 [95% CI 0.17-0.89], p=0.005, respectively). However, these associations were not significant once adjusted for Fontan type, age at ARFI, systemic ventricle morphology, ventricle function, or Model for End-stage Liver Disease (MELD-XI) excluding international normalised ratio (INR) (p>0.05 for all). CONCLUSIONS: Ideal FALD monitoring remains unclear. ARFI has utility as a binary non-invasive indicator of cirrhosis, highlighting individuals who may need more frequent ongoing monitoring for hepatocellular carcinoma. However, no definite advantage to serial ARFI, once cirrhotic range ARFI results are present, has been identified.

A comprehensive review of federated learning for COVID-19 detection.

The coronavirus of 2019 (COVID-19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID-19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID-19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID-19 detection using chest X-ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID-19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.

Throughput Analysis of Buffer-Aided Decode-and-Forward Wireless Relaying with RF Energy Harvesting.

In this paper, we investigated a buffer-aided decode-and-forward (DF) wireless relaying system over fading channels, where the source and relay harvest radio-frequency (RF) energy from a power station for data transmissions. We derived exact expressions for end-to-end throughput considering half-duplex (HD) and full-duplex (FD) relaying schemes. The numerical results illustrate the throughput and energy efficiencies of the relaying schemes under different self-interference (SI) cancellation levels and relay deployment locations. It was demonstrated that throughput-optimal relaying is not necessarily energy efficiency-optimal. The results provide guidance on optimal relaying network deployment and operation under different performance criteria.

SMARTphone-based, early cardiac REHABilitation in patients with acute coronary syndromes [SMART-REHAB Trial]: a randomized controlled trial protocol.

BACKGROUND: There are well-documented treatment gaps in secondary prevention of coronary heart disease and no clear guidelines to assist early physical activity after acute coronary syndromes (ACS). Smartphone technology may provide an innovative platform to close these gaps. This paper describes the study design of a randomized controlled trial assessing whether a smartphone-based secondary prevention program can facilitate early physical activity and improve cardiovascular health in patients with ACS. METHODS: We have developed a multi-faceted, patient-centred smartphone-based secondary prevention program emphasizing early physical activity with a graduated walking program initiated on discharge from ACS admission. The program incorporates; physical activity tracking through the smartphone's accelerometer with interactive feedback and goal setting; a dynamic dashboard to review and optimize cardiovascular risk factors; educational messages delivered twice weekly; a photographic food diary; pharmacotherapy review; and support through a short message service. The primary endpoint of the trial is change in exercise capacity, as measured by the change in six-minute walk test distance at 8-weeks when compared to baseline. Secondary endpoints include improvements in cardiovascular risk factor status, psychological well-being and quality of life, medication adherence, uptake of cardiac rehabilitation and re-hospitalizations. DISCUSSION: This randomized controlled trial will use a smartphone-phone based secondary prevention program to emphasize early physical activity post-ACS. It will provide evidence regarding the feasibility and utility of this innovative platform in closing the treatment gaps in secondary prevention. TRIAL REGISTRATION: The trial was retrospectively registered in the Australian New Zealand Clinical Trials Registry (ANZCTR) on April 4, 2016. The registration number is ACTRN12616000426482 .

Concurrent left ventricular and left anterior coronary artery thrombus: is COVID-19 an innocent bystander?

We present the angiographic findings of a case of myocardial infarction associated with COVID-19 with a heavy burden of thrombus, despite only minor obstructive coronary disease.

Correlation-Aware Spatial-Temporal Graph Learning for Multivariate Time-Series Anomaly Detection.

Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the nonlinear relations well or conventional deep learning (DL) models e.g., convolutional neural network (CNN) and long short-term memory (LSTM) that do not explicitly learn the pairwise correlations among variables. To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed ), for time-series anomaly detection. explicitly captures the pairwise correlations via a correlation learning (MTCL) module based on which a spatial-temporal graph neural network (STGNN) can be developed. Then, by employing a graph convolution network (GCN) that exploits one-and multihop neighbor information, our STGNN component can encode rich spatial information from complex pairwise dependencies between variables. With a temporal module that consists of dilated convolutional functions, the STGNN can further capture long-range dependence over time. A novel anomaly scoring component is further integrated into to estimate the degree of an anomaly in a purely unsupervised manner. Experimental results demonstrate that can detect and diagnose anomalies effectively in general settings as well as enable early detection across different time delays. Our code is available at https://github.com/huankoh/CST-GL.

SMARTphone-based, early cardiac REHABilitation in patients with acute coronary syndromes: a randomized controlled trial.

BACKGROUND: There are well-documented treatment gaps in secondary prevention of coronary heart disease with a lack of clearly defined strategies to assist early physical activity after acute coronary syndromes (ACS). Smartphone technology may provide an innovative platform to close these gaps. OBJECTIVES: The primary goal of this study was to assess whether a smartphone-based, early cardiac rehabilitation program improved exercise capacity in patients with ACS. METHODS: A total of 206 patients with ACS across six tertiary Australian hospitals were included in this randomized controlled trial. Participants were randomized to usual care (UC; including referral to traditional cardiac rehabilitation), with or without an adjunctive smartphone-based cardiac rehabilitation program (S-CRP) upon hospital discharge. The primary endpoint was change in exercise capacity, measured by the change in 6-minute walk test distance at 8 weeks when compared to baseline, between groups. Secondary endpoints included uptake and adherence to cardiac rehabilitation, changes in cardiac risk factors, psychological well-being and quality of life status. RESULTS: Of the 168 patients with complete follow-up (age 56 ± 10 years; 16% females), 83 were in the S-CRP. At 8-week follow-up, the S-CRP group had a clinically significant improvement in 6-minute walk test distance (Δ117 ± 76 vs. Δ91 ± 110 m; P = 0.02). Patients in the S-CRP were more likely to participate (87% vs. 51%, P < 0.001) and adhere (72% vs. 22%, P < 0.001) to a cardiac rehabilitation program. Compared to UC, patients receiving S-CRP had similar smoking cessation rates, LDL-cholesterol levels, blood pressure reduction, depression, anxiety and quality of life measures (all P = NS). CONCLUSION: In patients with ACS, a S-CRP, as an adjunct to UC improved exercise capacity at 8 weeks in addition to participation and adherence to cardiac rehabilitation (Australian New Zealand Clinical Trials Registry; ACTRN12616000426482).

Reconfiguration of magnetic domain structures of ErFeO3 by intense terahertz free electron laser pulses.

Understanding the interaction between intense terahertz (THz) electromagnetic fields and spin systems has been gaining importance in modern spintronics research as a unique pathway to realize ultrafast macroscopic magnetization control. In this work, we used intense THz pulses with pulse energies in the order of 10 mJ/pulse generated from the terahertz free electron laser (THz-FEL) to irradiate the ferromagnetic domains of ErFeO3 single crystal. It was found that the domain shape can be locally reconfigured by irradiating the THz - FEL pulses near the domain boundary. Observed domain reconfiguration mechanism can be phenomenologically understood by the combination of depinning effect and the entropic force due to local thermal gradient exerted by terahertz irradiation. Our finding opens up a new possibility of realizing thermal-spin effects at THz frequency ranges by using THz-FEL pulses.