Stratification of acute myocardial and endothelial cell injury, salvage index and final infarct size by systematic microRNA profiling in acute ST-elevation myocardial infarction.

Aim: Acute injury and subsequent remodelling responses to ST-segment elevation myocardial infarction (STEMI) are major determinants of clinical outcome. Current imaging and plasma biomarkers provide delayed readouts of myocardial injury and recovery. Here, we sought to systematically characterize all microRNAs (miRs) released during the acute phase of STEMI and relate miR release to magnetic resonance imaging (MRI) findings to predict acute and late responses to STEMI, from a single early blood sample.

Protective Role for Smooth Muscle Cell Hepcidin in Abdominal Aortic Aneurysm.

Background: Hepcidin is a liver-derived hormone that controls systemic iron homeostasis, by inhibiting the iron exporter ferroportin in the gut and spleen, respective sites of iron absorption and recycling. Hepcidin is also expressed ectopically in the context of cardiovascular disease. However, the precise role of ectopic hepcidin in underlying pathophysiology is unknown.

Machine learning assisted reflectance spectral characterisation of coronary thrombi correlates with microvascular injury in patients with ST-segment elevation acute coronary syndrome.

Aims: We set out to further develop reflectance spectroscopy for the characterisation and quantification of coronary thrombi. Additionally, we explore the potential of our approach for use as a risk stratification tool by exploring the relation of reflectance spectra to indices of coronary microvascular injury.

Methods And Results: We performed hyperspectral imaging of coronary thrombi aspirated from 306 patients presenting with ST-segment elevation acute coronary syndrome (STEACS).

Applications of artificial intelligence for patients with peripheral artery disease.

Objective: Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field.

Methods: We performed a narrative review based on studies reporting applications of AI in patients with PAD.

A Hand-Held Magnetic Acoustic Device With Integrated Real-Time Monitoring for Targeted Drug Delivery.

Advances in magnetic materials have enabled the development of new therapeutic agents that can be localized by external magnetic fields. These agents offer a potential means of improving treatment targeting and reducing the toxicity-related side effects associated with systemic delivery. Achieving sufficiently high magnetic fields at clinically relevant depths in vivo, however, remains a challenge.

Genome-wide association analysis and replication in 810,625 individuals with varicose veins.

Varicose veins affect one-third of Western society, with a significant subset of patients developing venous ulceration, costing $14.9 billion annually in the USA. Current management consists of either compression stockings, or surgical ablation for more advanced disease.

Interferon regulatory factor-5-dependent CD11c+ macrophages contribute to the formation of rupture-prone atherosclerotic plaques.

Aims: Inflammation is a key factor in atherosclerosis. The transcription factor interferon regulatory factor-5 (IRF5) drives macrophages towards a pro-inflammatory state. We investigated the role of IRF5 in human atherosclerosis and plaque stability.

A deep learning pipeline to simulate fluorodeoxyglucose (FDG) uptake in head and neck cancers using non-contrast CT images without the administration of radioactive tracer.

Objectives: Positron emission tomography (PET) imaging is a costly tracer-based imaging modality used to visualise abnormal metabolic activity for the management of malignancies. The objective of this study is to demonstrate that non-contrast CTs alone can be used to differentiate regions with different Fluorodeoxyglucose (FDG) uptake and simulate PET images to guide clinical management.

Methods: Paired FDG-PET and CT images (n = 298 patients) with diagnosed head and neck squamous cell carcinoma (HNSCC) were obtained from The cancer imaging archive.

A histopathological classification scheme for abdominal aortic aneurysm disease.

Objective: Two consensus histopathological classifications for thoracic aortic aneurysms (TAAs) and inflammatory aortic diseases have been issued to facilitate clinical decision-making and inter-study comparison. However, these consensus classifications do not specifically encompass abdominal aortic aneurysms (AAAs). Given its high prevalence and the existing profound pathophysiologic knowledge gaps, extension of the consensus classification scheme to AAAs would be highly instrumental.

A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents.

Background: Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdominal aortic aneurysms. However, the injection is contraindicated in patients with iodine allergy and is associated with renal complications.

Objectives: In this study, we investigate if the raw data acquired from a noncontrast CT image contains sufficient information to differentiate blood and other soft tissue components.

Thymosin β4 protects against aortic aneurysm via endocytic regulation of growth factor signaling.

Vascular stability and tone are maintained by contractile smooth muscle cells (VSMCs). However, injury-induced growth factors stimulate a contractile-synthetic phenotypic modulation which increases susceptibility to abdominal aortic aneurysm (AAA). As a regulator of embryonic VSMC differentiation, we hypothesized that Thymosin β4 (Tβ4) may function to maintain healthy vasculature throughout postnatal life.

Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review.

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis.

Development of a novel index to characterise arterial dynamics using ultrasound imaging.

Abdominal aortic aneurysms (AAA) are associated with systemic inflammation and endothelial dysfunction. We previously reported flow mediated dilatation (FMD) of the brachial artery as a predictor of AAA growth. We hence hypothesised that other physical characteristics of the brachial artery correlate with AAA growth.

Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period.

Objective: We investigated the utility of geometric features for future AAA growth prediction.

Background: Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth.

Editor's Choice - The GermanVasc Score: A Pragmatic Risk Score Predicts Five Year Amputation Free Survival in Patients with Peripheral Arterial Occlusive Disease.

Objective: Patients with peripheral arterial occlusive disease (PAOD) face an increased risk of both lower limb amputation and death. To date, it has been challenging to predict the long term outcomes for PAOD. The aim was to develop a risk score to predict worse five year amputation free survival (AFS).

A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation With or Without Intravenous Contrast.

Background: Existing methods to reconstruct vascular structures from a computerized tomography (CT) angiogram rely on contrast injection to enhance the radio-density within the vessel lumen. However, pathological changes in the vasculature may be present that prevent accurate reconstruction. In aortic aneurysmal disease, a thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in >90% of cases.

Integrated Plasma and Tissue Proteomics Reveals Attractin Release by Intraluminal Thrombus of Abdominal Aortic Aneurysms and Improves Aneurysm Growth Prediction in Humans.

Objective: Discovery of novel biomarkers for AAA growth prediction.

Background: Novel biomarker of AAA growth is a recognized priority in research. Our prior work implicated intraluminal thrombus (ILT) in AAAs to be a potential source of systemic mediators during AAA progression.

Recognizing the Dying Patient, When Less Could be More: A Diagnostic Framework for Shared Decision-Making at the End of Life.

Background: Recognizing dying patients is crucial to produce outcomes that are satisfactory to patients, their families, and clinicians.

Aim: Earlier discussion of and shared decision-making around dying to improve these outcomes.

Design: In this study, we interviewed 16 senior clinicians to develop summaries of palliative care in 4 key specialties: Cardiology, Vascular Surgery, Emergency General Surgery, and Intensive Care.