An effective healthcare monitoring system in an IoMT environment for heart disease detection using the HANN model.

The proposed work aims to develop an automated machine learning based network model for heart disease prediction with better accuracy. In the pre-processed data, the most significant features are selected using the White Shark Optimization based Linear Discriminant Analysis (WSO-LDA) technique, reducing computational complexity. Finally, the selected features are fed to the Hybrid Artificial Neural Network (HANN) with a Multi-Objective Spotted Hyena optimization (MOSHO) based classification stage. This stage classifies heart disease with minimized processing time.

How to perform a percutaneous coronary intervention, when no conventional arterial access site is available: A case report.

Coronary artery disease is one of the leading causes of mortality in the world. The presence of concomitant peripheral artery disease increases the risks of cardiovascular events along with limiting the arterial access for coronary intervention. Invasive management of such cases includes either alternate site access or combined peripheral and coronary revascularization. We hereby report a patient of the infrarenal abdominal aorta and bilateral subclavian arterial occlusion, who presented with acute coronary syndrome. To perform the percutaneous coronary intervention, we first performed the endovascular stenting of occluded aortoiliac disease, followed by stenting of the right coronary artery. We had discussed the limitation of arterial access to perform PCI in such a situation.

Jaya-tunicate swarm algorithm based generative adversarial network for COVID-19 prediction with chest computed tomography images.

A novel corona virus (COVID-19) has materialized as the respiratory syndrome in recent decades. Chest computed tomography scanning is the significant technology for monitoring and predicting COVID-19. To predict the patients of COVID-19 at early stage poses an open challenge in the research community. Therefore, an effective prediction mechanism named Jaya-tunicate swarm algorithm driven generative adversarial network (Jaya-TSA with GAN) is proposed in this research to find patients of COVID-19 infections. The developed Jaya-TSA is the incorporation of Jaya algorithm with tunicate swarm algorithm (TSA). However, lungs lobs are segmented using Bayesian fuzzy clustering, which effectively find the boundary regions of lung lobes. Based on the extracted features, the process of COVID-19 prediction is accomplished using GAN. The optimal solution is obtained by training GAN using proposed Jaya-TSA with respect to fitness measure. The dimensionality of features is reduced by extracting the optimal features, which enable to increase the speed of training process. Moreover, the developed Jaya-TSA based GAN attained outstanding effectiveness by considering the factors, like, specificity, accuracy, and sensitivity that captured the importance as 0.8857, 0.8727, and 0.85 by varying training data.

Epidemiological and clinical profile, management and outcomes of young patients (≤40 years) with acute coronary syndrome: A single tertiary care center study.

OBJECTIVE: To study the epidemiological and clinical profile, angiographic patterns, reasons for the delay in presentation, management, and outcomes of the acute coronary syndrome (ACS) in young patients (≤40yrs) presenting to a tertiary care hospital in North India. METHODS: We included a total of 182 patients aged ≤40 years and presenting with ACS to the cardiology critical care unit of our department from January 2018 to July 2019. RESULTS: The mean age of the study population was 35.5 ± 4.7years. 96.2% were males. Risk factors prevalent were smoking (56%), hypertension (29.7%), family history of premature coronary artery disease (18.2%), and diabetes (15.9%). The median time to first medical contact and revascularization was 300 (10-43200) minutes and 2880 (75-68400) minutes, respectively. ST-elevation ACS (STE-ACS) accounted for 82% and Non-ST-elevation ACS (NSTE-ACS) accounted for 18% of cases. Thrombolysis was done in 51.7% of the cases. Coronary angiography was done in 91.7% and percutaneous coronary intervention (PCI) in 52.2% (95/182) of the total cases. Coronary artery bypass surgery (CABG) was done in 2 patients (1.1%). Among those who underwent coronary angiography, single-vessel disease (SVD) was seen in 53% of the cases. There were no deaths in hospital, and only one patient died during the 30 days follow up. CONCLUSIONS: STE-ACS was the most common presentation of ACS in the young population. Smoking was the most common risk factor. The majority of the patients had single-vessel disease, and there was a significant delay in first medical contact and revascularization.

Footprint of a Bioresorbable Vascular Scaffold in Computed Tomography Coronary Angiogram at 5-Year Follow-up.

The Absorb bioresorbable vascular scaffold (Abbott Vascular) does not have an artifact on computed tomography coronary angiography (CTCA); the extent/location of the stent in situ can only be assessed by localizing its radiopaque platinum markers in a non-contrast CTCA. The characteristic appearance of BVS on CTCA should be interpreted as the footprint of a resorbed BVS, instead of a calcified plaque.