ABSTRACT
We have identified a rare missense variant on chromosome 9, position 125145990 (GRCh37), in exon 8 in PTGS1 (the gene encoding cyclo-oxygenase 1, COX-1, the target of anti-thrombotic aspirin therapy). We report that in the homozygous state within a large consanguineous family this variant is associated with a bleeding phenotype and alterations in platelet reactivity and eicosanoid production. Western blotting and confocal imaging demonstrated that COX-1 was absent in the platelets of three family members homozygous for the PTGS1 variant but present in their leukocytes. Platelet reactivity, as assessed by aggregometry, lumi-aggregometry and flow cytometry, was impaired in homozygous family members, as were platelet adhesion and spreading. The productions of COX-derived eicosanoids by stimulated platelets were greatly reduced but there were no changes in the levels of urinary metabolites of COX-derived eicosanoids. The proband exhibited additional defects in platelet aggregation and spreading which may explain why her bleeding phenotype was slightly more severe than those of other homozygous affected relatives. This is the first demonstration in humans of the specific loss of platelet COX-1 activity and provides insight into its consequences for platelet function and eicosanoid metabolism. Notably despite the absence of thromboxane A2 (TXA2) formation by platelets, urinary TXA2 metabolites were in the normal range indicating these cannot be assumed as markers of in vivo platelet function. Results from this study are important benchmarks for the effects of aspirin upon platelet COX-1, platelet function and eicosanoid production as they define selective platelet COX-1 ablation within humans.
Subject(s)
Aspirin , Platelet Function Tests , Blood Platelets , Cyclooxygenase 1/genetics , Female , Humans , Platelet Aggregation/genetics , Thromboxane A2Subject(s)
Atrial Fibrillation , Stroke , Anticoagulants , Atrial Fibrillation/drug therapy , Factor Xa Inhibitors , Humans , Registries , WarfarinABSTRACT
Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.
ABSTRACT
It is estimated that missed opportunities for diagnosis occur in 1 in 20 primary care appointments. This is not only detrimental to individual patients, but also to the healthcare system as health outcomes are affected and healthcare expenditure inevitably increases. There are many potential solutions to limit the number of missed opportunities for diagnosis and management, one of which is through the use of artificial intelligence. Artificial intelligence and machine learning research and capabilities have exponentially grown in the past decades, with their applications in medicine showing great promise. As such, this review aims to discuss the possible uses of machine learning in primary care to maximise the quality of care provided.
Subject(s)
Machine Learning , Primary Health Care , Diagnostic Errors , Humans , Public HealthABSTRACT
Ehlers-Danlos syndrome (EDS) is a group of connective tissue disorders that can result in a range of complications during pregnancy. Pregnant EDS patients generally have a favourable outcome, but those with vascular EDS are more likely to suffer from severe maternal complications. Early diagnosis of EDS and subtype characterization can aid in pre-pregnancy counselling, planning of antenatal care, risk assessment of obstetric and neonatal complications, and influence both obstetric and anaesthetic management of these patients. This piece aims to outline the obstetric implications of classical, hypermobile, and vascular EDS, and review the current literature regarding their optimal obstetric management.