UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Vascular and non-vascular UNet systems dominated with sUNet classes with attention. Most of the studies suffered from a low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings.

Intelligent Forecast of Stock Markets to Handle COVID-19 Economic Crisis by Modified Generative Adversarial Networks

Stock markets have voluminous data and are subjected to uncertainty. The coronavirus disease of 2019 (COVID-19) pandemic has hit the stock markets and the trends of stock markets have accelerated share prices of few companies and has also brought freefall to certain companies. This factor highlights the importance of technical analysis of the stock markets over fundamental analysis. So, the proposed robust model for financial forecasting is built based on the technical indicators and the fake price data generated over a period of time from the stock dataset by a novel architecture of modified generative adversarial network, which uses a dense recurrent neural network as the generator and a dense spectrally normalized convolutional neural network as the discriminator. The hyperparameters used in the network model follow the two-time-scale-update rule and they are tuned by using the Bayesian optimization technique. The feature importance of the technical indicators in predicting the performance by the stock market is enhanced by the XGBoost algorithm. The generative adversarial networks (GAN) used for forecasting in the previous works suffer from problems like mode collapse and non-convergence. So, the proposed work concentrates on building a GAN model, which is stable, robust and converges to Nash equilibrium. The generated GAN model is applied on stock data from the major 100 companies of the S&P 500 stock for a period of 20 years. The modified GAN model predicts prices precise similar to 99 percentage, which maximizes the stock returns. The proposed modified GAN model outperforms the baseline GAN model and other state of the art approaches of forecasting on comparison.

Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review. World journal of diabetes

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between “DM/COVID-19”. This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and “COVID-triggered DM” patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in “Diabetes patients during COVID-19” or “new-onset Diabetes triggered by COVID-19 in non-Diabetes patients”.

Role of herd immunity influencing the propagation of n-COV

This review gives an overview on the correlation of herd immunity with the propagation of the covid disease and the underlying mechanism of the same. A literature review of all the relevant papers related to the topic was conducted from articles ranging from 2000 to 2020. The outbreak of the novel coronavirus, COVID-19, has been declared as a global pandemic by the WHO. Various models, such as the SIR model, epidemiological model, social distancing model, contribute in determining the impact of herd immunity in the propagation of disease, which could be symptomatic or asymptomatic. Herd immunity can be measured by testing a sample of the population for the presence of the chosen immune parameter. These herd immunity effects have also been incorporated to create economical vaccination programmes. This article brings forth the fact that usually, herd immunity does not develop in a year or two. For herd immunity to come into play, an effective, safe vaccine is a must. If we have a vaccine, we may be able to develop herd immunity against the virus in future provided the majority of the population are vaccinated. Overall, this correlation is vital because it provides evidence for the earliest planning of vaccines and helps in the initiation of the herd population and helps protect vulnerable individuals in the community who have low-functioning immune systems.

Polyomavirus, Adenovirus, and Viral Respiratory Diseases

BK polyomavirus (BKPyV) belongs to the genus Polyomavirus of the family Polyomaviridae that comprises 13 different species with human host (Calvignac-Spencer et al. 2016). BKPyV virions are small non-enveloped particles of 40–45 nm in diameter, with an icosahedral symmetry, resistant to heat, and environment exposure (Hirsch and Steiger 2003). Structurally, BKPyV consists of a circular 5.1 kb double-stranded DNA genome within a capsid made of proteins Vp1 on the outside and Vp2 and Vp3 on the inside. The BKPyV genome is divided into three regions: the noncoding control region (NCCR);the early viral gene region (EVGR);the late viral gene region (LVGR). The NCCR is responsible for DNA replication and bidirectional viral gene expression;the EVGR encodes the regulatory nonstructural proteins called small tumor antigen (sTag), large tumor antigen (LTag), and spliced variants called truncated Tag;the LVGR contains the genes for the structural proteins Vp1, Vp2, Vp3, and a small accessory protein of unknown function called agnoprotein. The Vp1 capsid protein is the main target of BKPyV-specific antibodies while LTag is used as target for immunohistochemical diagnosis in tissue samples. BKPyV was isolated for the first time in a patient (B.K.) who underwent a kidney transplant and presented in the urine particular epithelial cells with nuclear viral inclusions called “decoy cells” (Gardner et al. 1971). Subsequently, BKPyV has been associated with hemorrhagic cystitis (HC) after hematopoietic stem cell transplantation (HSCT) (Apperley et al. 1987;Arthur et al. 1986), and nephropathy after kidney transplantation (Binet et al. 1999;Randhawa et al. 1999). Serologic studies showed that up to 90% of the adult population has been exposed to BKPyV during infancy and childhood (Egli et al. 2009). The infection can be asymptomatic or causes flu-like symptoms indistinguishable from other causes of viral community respiratory tract infections. The transmission is thought to be by direct person-to-person contact or by exposure to respiratory secretions. After primary infection, the virus remains latent in renal tubular epithelial and urothelial cells and asymptomatic viruria can be detected in 5–10% of healthy individuals (Hirsch and Steiger 2003;Egli et al. 2009). The urinary shedding increases to 60–80% in patients undergoing HSCT, as well as the BKPyV viruria load increases to less than 3 log10 to >7 log10 copies/mL (Cesaro et al. 2018a;Cesaro et al. 2015). © 2020, Springer Nature Switzerland AG.

Cardiological society of India document on safety measure during echo evaluation of cardiovascular disease in the time of COVID-19.

An echocardiographic investigation is one of the key modalities of diagnosis in cardiology. There has been a rising presence of cardiological comorbidities in patients positive for COVID-19. Hence, it is becoming extremely essential to look into the correct safety precautions, healthcare professionals must take while conducting an echo investigation. The decision matrix formulated for conducting an echocardiographic evaluation is based on presence or absence of cardiological comorbidity vis-à-vis positive, suspected or negative for COVID-19. The safety measures have been constructed keeping in mind the current safety precautions by WHO, CDC and MoHFW, India.

Cardiological Society of India: Document on acute MI care during COVID-19.

The unprecedented and rapidly spreading Coronavirus Disease-19 (COVID-19) pandemic has challenged public health care systems globally. Based on worldwide experience, India has initiated a nationwide lockdown to prevent the exponential surge of cases. During COVID-19, management of cardiovascular emergencies like acute Myocardial Infarction (MI) may be compromised. Cardiological Society of India (CSI) has ventured in this moment of crisis to evolve a consensus document for care of acute MI. However, this care should be individualized, based on local expertise and governmental advisories.