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Summary: Artificial intelligence is a rapidly evolving area of technology whose integration into healthcare delivery infrastructure is predicted to have profound implications for medicine delivery in the 21st century. Artificial intelligence as it relates to healthcare is a term used to cover a wide scope of computer‐based algorithms whose application varies from patient selection to enhancements in imaging and postoperative prognostication. This article reviews the literature to contextualise how AI is currently being implemented in interventional radiology. This review considers the literature from a preoperative, intraoperative and postoperative perspective. [ABSTRACT FROM AUTHOR]

Introduction: Association between coronary artery disease (CAD) and internal carotid artery stenosis (ICAS) could prove clinically relevant. However, evidence defining this association is currently inconclusive. Our study investigates the prevalence of ICAS in non‐emergent, ambulatory patients presenting for PCA with suspected CAD in an Australian context. Methods: Between February 2019 and June 2019, 121 consecutive participants were verbally consented and enrolled in our study. The data were analysed retrospectively. PCA and CUS were performed within 24 h of each other. Multinomial logistic regression assessed independent predictors for ICAS, with statistical significance set at P value < 0.05. Linear regression analysis correlated CAD and ICAS severity, with significance of a P‐value < 0.05. Analysis was conducted using IBM SPSS 26 software (Chicago, Illinois). Results: The final study included 121 patients (age 73 ± 9 years, 76.9% male). ICAS on CUS was present in 55.4% of participants for PCA. CAD was an independent risk factor for ICAS on multinomial logistic regression odds ratio 3.87 (P = 0.023). CAD severity (multi vessel disease) showed significant correlation with ICAS r = 0.22 (P = 0.014) using linear regression analysis. Conclusion: CAD is an independent predictor of ICAS, and severity of ICAS is correlated with CAD disease. However, most participants had only minor ICAS (16–49% diameter stenosis). Our findings are consistent with internationally published studies, suggesting their data are generalisable to the Australian population. Larger studies are needed to address the applicability of CUS screening in patients with advanced CAD. [ABSTRACT FROM AUTHOR]

Aspirin, the pro-drug of salicylate, is associated with reduced incidence of death from cancers of the colon, lung and prostate and is commonly prescribed in combination with metformin in individuals with type 2 diabetes. Salicylate activates the AMPactivated protein kinase (AMPK) by binding at the A-769662 drug binding site on theAMPKß1-subunit, amechanism that is distinct from metformin which disrupts the adenylate charge of the cell. A hallmark of many cancers is high rates of fatty acid synthesis and AMPK inhibits this pathway through phosphorylation of acetyl-CoA carboxylase (ACC). It is currently unknown whether targeting the AMPK-ACC-lipogenic pathway using salicylate and/or metformin may be effective for inhibiting cancer cell survival. Salicylate suppresses clonogenic survival of prostate and lung cancer cells at therapeutic concentrations achievable following the ingestion of aspirin (<1.0 mM); effects not observed in prostate (PNT1A) and lung (MRC-5) epithelial cell lines. Salicylate concentrations of 1 mM increased the phosphorylation of ACC and suppressed de novo lipogenesis and these effects were enhanced with the addition of clinical concentrations of metformin (100 µM) and eliminated in mouse embryonic fibroblasts (MEFs) deficient in AMPK ß1. Supplementation of media with fatty acids and/or cholesterol reverses the suppressive effects of salicylate and metformin on cell survival indicating the inhibition of de novo lipogenesis is probably important. Preclinical studies evaluating the use of salicylate based drugs alone and in combination with metformin to inhibit de novo lipogenesis and the survival of prostate and lung cancers are warranted. [ABSTRACT FROM AUTHOR]

Antenna arrays with space-time adaptive processing (STAP) are commonly utilized to allow the reception of signals in harsh interference environments. The primary applications of STAP have been radar and communications systems, where the STAP weights are optimized for output signal-to-interference-plus-noise ratio (SINR); however, there is increasing demand for STAP in time-of-arrival (TOA) estimation applications, such as global navigation satellite system (GNSS) receivers. It is understood that TOA estimation performance is primarily dependent on receiver postcorrelation carrier-to-noise ratio (C/N0). In this study, the distinction between SINR and C/N0 is established, and a performance analysis of the common STAP algorithms is performed in the presence of interference. It is demonstrated that output SINR is an inadequate indication of GNSS receiver C/N0 performance. As a result, caution should be used when measuring the relative performance of different STAP algorithms in GNSS applications. Consequently, the present work develops a novel STAP algorithm which maximizes C/N0. Additionally, the performance of common STAP algorithms is analyzed in the context of this performance bound, and it is demonstrated that, in many cases, their performance is near optimal. [ABSTRACT FROM AUTHOR]