Trends in Transcatheter Aortic Valve Implantation in Australia

Aortic valve stenosis is the most common valvular lesion in Australia, with a rising prevalence in line with the ageing population. Recent trials have demonstrated the efficacy of transcatheter aortic valve implantation (TAVI) versus surgical aortic valve replacement in consecutively lower surgical risk patient cohorts. Despite this, the current indication for TAVI in Australia is for the treatment of severe symptomatic aortic stenosis in patients who are of prohibitive or high surgical risk and ultimately deemed suitable by a heart team. This article summarises the trends in TAVI in Australia over the last 5 years in terms of funding, accreditation and service delivery, as well as advances in technique, technology, patient selection and local outcomes

Platelet quiescence in patients with acute coronary syndrome undergoing coronary artery bypass graft surgery

BACKGROUND: The optimal antiplatelet strategy for patients with acute coronary syndromes who require coronary artery bypass surgery remains unclear. While a more potent antiplatelet regimen will predispose to perioperative bleeding, it is hypothesized that through “platelet quiescence,” ischemic protection conferred by such therapy may provide a net clinical benefit. METHODS AND RESULTS: We compared patients undergoing coronary artery bypass surgery who were treated with a more potent antiplatelet inhibition strategy with those with a less potent inhibition through a meta-analysis. The primary outcome was all-cause mortality after bypass surgery. The analysis identified 4 studies in which the antiplatelet regimen was randomized and 6 studies that were nonrandomized. Combining all studies, there was an overall higher mortality with weaker strategies compared with more potent strategies (odds ratio, 1.38; 95% CI, 1.03–1.85; P=0.03). CONCLUSIONS: Our findings support the concept of platelet quiescence, in reducing mortality for patients with acute coronary syndrome requiring coronary artery bypass surgery. This suggests the routine up-front use of potent antiplatelet regimens in acute coronary syndrome, irrespective of likelihood of coronary artery bypass graft

Rituximab therapy in cutaneous infiltration of chronic lymphocytic leukaemia

Chronic lymphocytic leukaemia (CLL) is a common adult leukaemia in patients over 50 years of age, with a median age at diagnosis of around 65 years [1,2]. While it remains an incurable disease, its indolent nature results in a varied prognosis with a median survival of greater than 10 years reported in early-stage disease. However, others may die rapidly even with aggressive treatment strategies [1, 2]. Treatment is typically initiated when patients become symptomatic [2]. Standard treatments for CLL include alkylating agents such as chlorambucil or cyclophosphamide, but combination chemotherapy with vincristine, prednisolone, fludarabine, or rituximab may be required in some patients [3-6]. Rituximab, a chimeric anti-C020 monoclonal antibody, has shown significant activity in a variety of B cell lymphomas [7-15]. The C020 antigen is also found on B cells in CLL and singleagent rituximab has shown activity in these patients [16- 19,22]

A Cross Sectional Study on the Gap in Doctor – Patient Communication in a Tertiary Care Hospital

Introduction: Effective doctor – patient communication is a central clinical function and the resultant rapport is the heart and art of medicine and a central component in the delivery of health care. This communication is essential in achieving the desired outcomes of treatment. A doctor’s communication and interpretational skills encompass the ability to gather information in order to facilitate accurate diagnosis, counsel appropriately, give therapeutic instructions and establish caring relationship with the patients. Methodology: The study population was the people who consulted their doctors in the hospital for any illness of their own or their children. Informed consent was taken from the patients interviewed. Data was collected from a total of 448 patients who came for a consultation to the hospital.Results: The results of our study agrees with other studies that many patients are not satisfied with the consultation and that doctors fail many a time in adequately educating the patients and providing them better compliance. Nearly 25 percent of the patients complain that they are not satisfied with their consultation and couldn’t build compliance with their doctors. Conclusion: The study concludes that many crevices in doctor – patient communication are left un-filled, making most of the patients not satisfied after their consultation with doctors

Predicting entropy and heat capacity of hydrocarbons using machine learning

Chemical substances are essential in all aspects of human life, and understanding their properties is essential for developing chemical systems. The properties of chemical species can be accurately obtained by experiments or ab initio computational calculations; however, these are time-consuming and costly. In this work, machine learning models (ML) for estimating entropy, S, and constant pressure heat capacity, Cp, at 298.15 K, are developed for alkanes, alkenes, and alkynes. The training data for entropy and heat capacity are collected from the literature. Molecular descriptors generated using alvaDesc software are used as input features for the ML models. Support vector regression (SVR), v-support vector regression (v-SVR), and random forest regression (RFR) algorithms were trained with K-fold cross-validation on two levels. The first level assessed the models' performance, and the second level generated the final models. Between the three ML models chosen, SVR shows better performance on the test dataset. The SVR model was then compared against traditional Benson's group additivity to illustrate the advantages of using the ML model. Finally, a sensitivity analysis is performed to find the most critical descriptors in the property estimations

Screening gas‐phase chemical kinetic models: Collision limit compliance and ultrafast timescales

Detailed gas‐phase chemical kinetic models are widely used in combustion research, and many new mechanisms for different fuels and reacting conditions are developed each year. Recent works have highlighted the need for error checking when preparing such models, but a useful community tool to perform such analysis is missing. In this work, we present a simple online tool to screen chemical kinetic mechanisms for bimolecular reactions exceeding collision limits. The tool is implemented on a user‐friendly website, cloudflame.kaust.edu.sa, and checks three different classes of bimolecular reactions; (ie, pressure independent, pressure‐dependent falloff, and pressure‐dependent PLOG). In addition, two other online modules are provided to check thermodynamic properties and transport parameters to help kinetic model developers determine the sources of errors for reactions that are not collision limit compliant. Furthermore, issues related to unphysically fast timescales can remain an issue even if all bimolecular reactions are within collision limits. Therefore, we also present a procedure to screen ultrafast reaction timescales using computational singular perturbation. For demonstration purposes only, three versions of the rigorously developed AramcoMech are screened for collision limit compliance and ultrafast timescales, and recommendations are made for improving the models. Larger models for biodiesel surrogates, tetrahydropyran, and gasoline surrogates are also analyzed for exemplary purposes. Numerical simulations with updated kinetic parameters are presented to show improvements in wall‐clock time when resolving ultrafast timescales

3D Printing for Cardiovascular Applications: From End-to-End Processes to Emerging Developments

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Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community

Evaluation of an in vitro coronary stent thrombosis model for preclinical assessment

Stent thrombosis remains an infrequent but significant complication following percutaneous coronary intervention. Preclinical models to rapidly screen and validate therapeutic compounds for efficacy are lacking. Herein, we describe a reproducible, high throughput and cost-effective method to evaluate candidate therapeutics and devices for either treatment or propensity to develop stent thrombosis in an in vitro bench-top model. Increasing degree of stent malapposition (0.00 mm, 0.10 mm, 0.25 mm and 0.50 mm) was associated with increasing thrombosis and luminal area occlusion (4.1 +/- 0.5%, 6.3 +/- 0.5%, 19.7 +/- 4.5%, and 92.6 +/- 7.4%, p < 0.0001, respectively). Differences in stent design in the form of bare-metal, drug-eluting, and bioresorbable vascular scaffolds demonstrated differences in stent thrombus burden (14.7 +/- 3.8% vs. 20.5 +/- 3.1% vs. 86.8 +/- 5.3%, p < 0.01, respectively). Finally, thrombus burden was significantly reduced when healthy blood samples were incubated with Heparin, ASA/Ticagrelor (DAPT), and Heparin+DAPT compared to control (DMSO) at 4.1 +/- 0.6%, 6.9 +/- 1.7%, 4.5 +/- 1.2%, and 12.1 +/- 1.8%, respectively (p < 0.01). The reported model produces high throughput reproducible thrombosis results across a spectrum of antithrombotic agents, stent design, and degrees of apposition. Importantly, performance recapitulates clinical observations of antiplatelet/antithrombotic regimens as well as device and deployment characteristics. Accordingly, this model may serve as a screening tool for candidate therapies in preclinical evaluation