Publications by authors named "R Sykes"

Introduction: Ischaemic heart disease (IHD) and cerebrovascular disease are leading causes of morbidity and mortality worldwide. Cerebral small vessel disease (CSVD) is a leading cause of dementia and stroke. While coronary small vessel disease (coronary microvascular dysfunction) causes microvascular angina and is associated with increased morbidity and mortality.

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The pathophysiology of myocardial injury following COVID-19 remains uncertain. COVID-HEART was a prospective, multicentre study utilising cardiovascular magnetic resonance (CMR) to characterise COVID-related myocardial injury. In this pre-specified analysis, the objectives were to examine (1) the frequency of myocardial ischaemia following COVID-19, and (2) the association between ischaemia and myocardial injury.

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Causal associations between viral infections and acute myocardial injury are not fully understood, with mechanisms potentially involving direct cardiovascular involvement or systemic inflammation. This review explores plausible mechanisms of vascular fibrosis in patients with post-COVID-19 syndrome, focusing on extracellular matrix remodelling. Despite global attention, significant mechanistic or translational breakthroughs in the management of post-viral syndromes remain limited.

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Article Synopsis
  • The study investigates the link between atherosclerosis (plaque buildup in arteries) and types of myocardial ischemia (insufficient blood flow to the heart) in patients without significant coronary artery blockage (INOCA).
  • It employs advanced invasive tests to assess coronary microvascular function and quantifies plaque burden using the Gensini score, which takes into account the severity of artery blockage.
  • Findings reveal that higher Gensini scores correlate with poorer microvascular function, and different INOCA endotypes (like microvascular angina and vasospastic angina) show variations in plaque scores, indicating the complexity of heart conditions in patients without obvious artery blockage.
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Article Synopsis
  • A weakly supervised AI model called Triagnexia Colorectal was created to detect abnormal colorectal histology, like dysplasia and cancer, and prioritize biopsies based on clinical importance.
  • The model was trained on nearly 25,000 digitized images and evaluated by multiple pathologists, offering a user-friendly interface to enhance decision-making in digital pathology.
  • Validation results show high accuracy for the AI model, with impressive specificity and sensitivity scores, which pathologists found beneficial for detecting and prioritizing abnormal colorectal cases.
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