VIRAL COAGULATION: Pushing the Envelope.

Many virus types affect the blood clotting system with correlations to pathology that range widely from thrombosis to haemorrhage linking to inflammation. Here we overview the intricate crosstalk induced by infection between proteins on the virus encoded by either the host or virus genomes, coagulation proteins, platelets, leukocytes, and endothelial cells. For blood-borne viruses with an outer covering acquired from the host cell, the envelope, a key player may be the cell-derived trigger of coagulation on the virus surface, tissue factor (TF). TF is a multifunctional transmembrane cofactor that accelerates factor (F) VIIa-dependent activation of FX to FXa leading to clot formation. However, the nascent TF/FVIIa/FXa complex also facilitates G-protein-coupled modulation of cells via protease activated receptor-2. As a viral envelope constituent, TF can bypass the physiological modes of regulation, thereby initiating the activation of neighbouring platelets leukocytes and endothelial cells. A thromboinflammatory environment is predicted due to feedback amplification in response to cellular release of cytokines, procoagulant proteins and neutrophil extracellular traps, and stimulus-induced accessibility of adhesive receptors resulting in cellular aggregates. The pathobiological effects of thromboinflammation ultimately contribute to innate and adaptive immunity for viral clearance. In contrast, the preceding stages of viral infection may be enhanced via the TF-protease axis.

Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients undergoing routine Coronary Computed Tomography Angiography.

AIMS: Coronary Computed Tomography Angiography (CCTA) is a first line investigation for chest pain in patients with suspected obstructive coronary artery disease (CAD). However, many acute cardiac events occur in the absence of obstructive CAD. We assessed the lifetime cost-effectiveness of integrating a novel artificial intelligence-enhanced image analysis algorithm (AI-Risk) that stratifies the risk of cardiac events by quantifying coronary inflammation, combined with the extent of coronary artery plaque and clinical risk factors, by analysing images from routine CCTA. METHODS AND RESULTS: A hybrid decision-tree with population cohort Markov model was developed from 3,393 consecutive patients who underwent routine CCTA for suspected obstructive CAD and followed up for major adverse cardiac events over a median(IQR) of 7.7(6.4-9.1) years. In a prospective real-world evaluation survey of 744 consecutive patients undergoing CCTA for chest pain investigation, the availability of AI-Risk assessment led to treatment initiation or intensification in 45% of patients. In a further prospective study of 1,214 consecutive patients with extensive guideline recommended cardiovascular risk profiling, AI-Risk stratification led to treatment initiation or intensification in 39% of patients beyond the current clinical guideline recommendations. Treatment guided by AI-Risk modelled over a lifetime horizon could lead to fewer cardiac events (relative reductions of 4%, 4%, 11%, and 12% for myocardial infarction, ischaemic stroke, heart failure and cardiac death, respectively). Implementing AI-Risk classification in routine interpretation of CCTA is highly likely to be cost-effective (Incremental cost-effectiveness ratio £1,371-3,244), both in scenarios of current guideline compliance or when applied only to patients without obstructive CAD. CONCLUSIONS: Compared with standard care, the addition of AI-Risk assessment in routine CCTA interpretation is cost effective, by refining risk guided medical management.

Advances in Clinical Imaging of Vascular Inflammation: A State-of-the-Art Review.

Vascular inflammation is a major contributor to cardiovascular disease, particularly atherosclerotic disease, and early detection of vascular inflammation may be key to the ultimate reduction of residual cardiovascular morbidity and mortality. This review paper discusses the progress toward the clinical utility of noninvasive imaging techniques for assessing vascular inflammation, with a focus on coronary atherosclerosis. A discussion of multiple modalities is included: computed tomography (CT) imaging (the major focus of the review), cardiac magnetic resonance, ultrasound, and positron emission tomography imaging. The review covers recent progress in new technologies such as the novel CT biomarkers of coronary inflammation (eg, the perivascular fat attenuation index), new inflammation-specific tracers for positron emission tomography-CT imaging, and others. The strengths and limitations of each modality are explored, highlighting the potential for multi-modality imaging and the use of artificial intelligence image interpretation to improve both diagnostic and prognostic potential for common conditions such as coronary artery disease.

Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study.

BACKGROUND: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. METHODS: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. FINDINGS: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. INTERPRETATION: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. FUNDING: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

Adipose Tissue in Cardiovascular Disease: From Basic Science to Clinical Translation.

The perception of adipose tissue as a metabolically quiescent tissue, primarily responsible for lipid storage and energy balance (with some endocrine, thermogenic, and insulation functions), has changed. It is now accepted that adipose tissue is a crucial regulator of metabolic health, maintaining bidirectional communication with other organs including the cardiovascular system. Additionally, adipose tissue depots are functionally and morphologically heterogeneous, acting not only as sources of bioactive molecules that regulate the physiological functioning of the vasculature and myocardium but also as biosensors of the paracrine and endocrine signals arising from these tissues. In this way, adipose tissue undergoes phenotypic switching in response to vascular and/or myocardial signals (proinflammatory, profibrotic, prolipolytic), a process that novel imaging technologies are able to visualize and quantify with implications for clinical prognosis. Furthermore, a range of therapeutic modalities have emerged targeting adipose tissue metabolism and altering its secretome, potentially benefiting those at risk of cardiovascular disease.

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.

BACKGROUND: Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented. OBJECTIVES: This study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care. METHODS: The deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value. RESULTS: External validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post-cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01). CONCLUSIONS: Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.

Trace residue identification, characterization, and longitudinal monitoring of the novel synthetic opioid ß-U10, from discarded drug paraphernalia.

Empirical data regarding dynamic alterations in illicit drug supply markets in response to the COVID-19 pandemic, including the potential for introduction of novel drug substances and/or increased poly-drug combination use at the "street" level, that is, directly proximal to the point of consumption, are currently lacking. Here, a high-throughput strategy employing ambient ionization-mass spectrometry is described for the trace residue identification, characterization, and longitudinal monitoring of illicit drug substances found within >6,600 discarded drug paraphernalia (DDP) samples collected during a pilot study of an early warning system for illicit drug use in Melbourne, Australia from August 2020 to February 2021, while significant COVID-19 lockdown conditions were imposed. The utility of this approach is demonstrated for the de novo identification and structural characterization of ß-U10, a previously unreported naphthamide analog within the "U-series" of synthetic opioid drugs, including differentiation from its α-U10 isomer without need for sample preparation or chromatographic separation prior to analysis. Notably, ß-U10 was observed with 23 other drug substances, most commonly in temporally distinct clusters with heroin, etizolam, and diphenhydramine, and in a total of 182 different poly-drug combinations. Longitudinal monitoring of the number and weekly "average signal intensity" (ASI) values of identified substances, developed here as a semi-quantitative proxy indicator of changes in availability, relative purity and compositions of street level drug samples, revealed that increases in the number of identifications and ASI for ß-U10 and etizolam coincided with a 50% decrease in the number of positive detections and an order of magnitude decrease in the ASI for heroin.

Persistently elevated complement alternative pathway biomarkers in COVID-19 correlate with hypoxemia and predict in-hospital mortality.

Mechanisms underlying the SARS-CoV-2-triggered hyperacute thrombo-inflammatory response that causes multi-organ damage in coronavirus disease 2019 (COVID-19) are poorly understood. Several lines of evidence implicate overactivation of complement. To delineate the involvement of complement in COVID-19, we prospectively studied 25 ICU-hospitalized patients for up to 21 days. Complement biomarkers in patient sera and healthy controls were quantified by enzyme-linked immunosorbent assays. Correlations with respiratory function and mortality were analyzed. Activation of complement via the classical/lectin pathways was variably increased. Strikingly, all patients had increased activation of the alternative pathway (AP) with elevated levels of activation fragments, Ba and Bb. This was associated with a reduction of the AP negative regulator, factor (F) H. Correspondingly, terminal pathway biomarkers of complement activation, C5a and sC5b-9, were significantly elevated in all COVID-19 patient sera. C5a and AP constituents Ba and Bb, were significantly associated with hypoxemia. Ba and FD at the time of ICU admission were strong independent predictors of mortality in the following 30 days. Levels of all complement activation markers were sustained throughout the patients' ICU stays, contrasting with the varying serum levels of IL-6, C-reactive protein, and ferritin. Severely ill COVID-19 patients have increased and persistent activation of complement, mediated strongly via the AP. Complement activation biomarkers may be valuable measures of severity of lung disease and the risk of mortality. Large-scale studies will reveal the relevance of these findings to thrombo-inflammation in acute and post-acute COVID-19.

Early Warning System for Illicit Drug Use at Large Public Events: Trace Residue Analysis of Discarded Drug Packaging Samples.

Inspired by Locard's exchange principle, which states "every contact leaves a trace", a trace residue sampling strategy has been developed for the analysis of discarded drug packaging samples (DPS), as part of an early warning system for illicit drug use at large public events including music/dance festivals. Using direct analysis in real time/mass spectrometry and tandem mass spectrometry, rapid and high-throughput identification and characterization of a wide range of illicit drugs and adulterant substances was achieved, including in complex polydrug mixtures and at low relative ion abundances. A total of 1362 DPS were analyzed either off-site using laboratory-based instrumentation or on-site and in close to real time using a transportable mass spectrometer housed within a mobile analytical laboratory, with each analysis requiring less than 1 min per sample. Of the DPS analyzed, 92.2% yielded positive results for at least one of 15 different drugs and/or adulterants, including cocaine, MDMA, and ketamine, as well as numerous novel psychoactive substances (NPS). Also, 52.6% of positive DPS were found to contain polydrug mixtures, and a total of 42 different drug and polydrug combinations were observed throughout the study. For analyses performed on-site, reports to key stakeholders including event organizers, first aid and medical personnel, and peer-based harm reduction workers could be provided in as little as 5 min after sample collection. Following risk assessment of the potential harms associated with their use, drug advisories or alerts were then disseminated to event staff and patrons and subsequently to the general public when substances with particularly toxic properties were identified.