A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

Background: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. Methods and results: We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62–0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P  Conclusion: The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art. </p

Adipose tissue-derived WNT5A regulates vascular redox signaling in obesity via USP17//RAC1-mediated activation of NADPH oxidases

Obesity is associated with changes in the secretome of adipose tissue (AT), which affects the vasculature through endocrine and paracrine mechanisms. Wingless-related integration site 5A (WNT5A) and secreted frizzled-related protein 5 (SFRP5), adipokines that regulate noncanonical Wnt signaling, are dysregulated in obesity. We hypothesized that WNT5A released from AT exerts endocrine and paracrine effects on the arterial wall through noncanonical RAC1-mediated Wnt signaling. In a cohort of 1004 humans with atherosclerosis, obesity was associated with increased WNT5A bioavailability in the circulation and the AT, higher expression of WNT5A receptors Frizzled 2 and Frizzled 5 in the human arterial wall, and increased vascular oxidative stress due to activation of NADPH oxidases. Plasma concentration of WNT5A was elevated in patients with coronary artery disease compared to matched controls and was independently associated with calcified coronary plaque progression. We further demonstrated that WNT5A induces arterial oxidative stress and redox-sensitive migration of vascular smooth muscle cells via Frizzled 2–mediated activation of a previously uncharacterized pathway involving the deubiquitinating enzyme ubiquitin-specific protease 17 (USP17) and the GTPase RAC1. Our study identifies WNT5A and its downstream vascular signaling as a link between obesity and vascular disease pathogenesis, with translational implications in humans

A Data Compression Hardware Accelerator Enabling Long-Term Biosignal Monitoring Based on Ultra-Low Power IoT Platforms

For highly demanding scenarios such as continuous bio-signal monitoring, transmitting excessive volumes of data wirelessly comprises one of the most critical challenges. This is due to the resource limitations posed by typical hardware and communication technologies. Driven by such shortcomings, this paper aims at addressing the respective deficiencies. The main axes of this work include (a) data compression, and (b) the presentation of a complete, efficient and practical hardware accelerator design able to be integrated in any Internet of Things (IoT) platform for addressing critical challenges of data compression. On one hand, the developed algorithm is presented and evaluated on software, exhibiting significant benefits compared to respective competition. On the other hand, the algorithm is fully implemented on hardware providing a further proof of concept regarding the implementation feasibility with respect to state-of-the art hardware design approaches. Finally, system-level performance benefits, regarding data transmission delay and energy saving, are highlighted, taking into consideration the characteristics of prominent IoT platforms. Concluding, this paper presents a holistic approach based on data compression that is able to drastically enhance an IoT platform’s performance and tackle efficiently a notorious challenge of highly demanding IoT applications such as real-time bio-signal monitoring

Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring

In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel