With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.

Cardiovascular diseases (CVDs) are the leading causes of death worldwide. Epicardial adipose tissue (EAT) is defined as a fat depot localized between the myocardial surface and the visceral layer of the pericardium and is a type of visceral fat. EAT is one of the most important risk factors for atherosclerosis and cardiovascular events and a promising new therapeutic target in CVDs. In health conditions, EAT has a protective function, including protection against hypothermia or mechanical stress, providing myocardial energy supply from free fatty acid and release of adiponectin. In patients with obesity, metabolic syndrome, or diabetes mellitus, EAT becomes a deleterious tissue promoting the development of CVDs. Previously, we showed an adverse modulation of gene expression in pericoronary adipose tissue in patients with coronary artery disease (CAD). Here, we summarize the currently available evidence regarding the role of EAT in the development of CVDs, including CAD, heart failure, and atrial fibrillation. Due to the rapid development of the COVID-19 pandemic, we also discuss data regarding the association between EAT and the course of COVID-19. Finally, we present the potential therapeutic possibilities aiming at modifying EAT\'s function. The development of novel therapies specifically targeting EAT could revolutionize the prognosis in CVDs.

To critically review and summarize current knowledge regarding the assessment of newborns with neonatal abstinence syndrome (NAS).
We searched the following databases for articles on the assessment of newborns with NAS that were published in English between January 2014 and June 2020: PubMed, CINAHL, and PsycINFO. Keywords and Medical Subject Heading terms used to identify relevant research articles included neonatal abstinence syndrome; Finnegan Scale; eat, sleep, console; epigenetics; genetics; pharmacokinetics; and measurement. We independently reviewed articles for inclusion.
We retrieved 435 articles through database searches and 17 through manual reference searches; 31 articles are included in the final review. Excluded articles were duplicates, not relevant to NAS, qualitative studies, and/or of low quality.
We used the methodology of Whittemore and Knafl to guide this integrative review. We extracted and organized data under the following headings: author, year and country, purpose, study design, participants, measurement, biomarker (if applicable), results, limitations, recommendations, and intervention.
The Finnegan Neonatal Abstinence Scale is the most widely used instrument to measure symptoms of NAS in newborns, although it is very subjective. Recently, there has been a transition from the Finnegan Neonatal Abstinence Scale to the eat, sleep, console method, which consists of structured assessment and intervention and has been shown to decrease length of hospital stay and total opioid treatment dose. Researchers examined biomarkers of NAS, including genetic markers and autonomic nervous system responses, on the variation in incidence and differential severity of NAS. In the included articles, women with opioid use disorder who were treated with naltrexone during pregnancy gave birth to newborns without NAS diagnoses. However, most women who were treated with buprenorphine gave birth to newborns with NAS diagnoses.
NAS negatively affects newborns in a multitude of ways, and the objective assessment and measurement of the newborn\'s response to withdrawal remains understudied and needs further investigation.