Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks

The N-terminus of IpaB provides a potential anchor to the Shigella type III secretion system tip complex protein IpaD

The type III secretion system (T3SS) is an essential virulence factor for Shigella flexneri, providing a conduit through which host-altering effectors are injected directly into a host cell to promote uptake. The type III secretion apparatus (T3SA) is comprised of a basal body, external needle, and regulatory tip complex. The nascent needle is a polymer of MxiH capped by a pentamer of invasion plasmid antigen D (IpaD). Exposure to bile salts (e.g. deoxycholate) causes a conformational change in IpaD and promotes recruitment of IpaB to the needle tip. It has been proposed that IpaB senses contact with host cell membranes, recruiting IpaC and inducing full secretion of T3SS effectors. While the steps of T3SA maturation and their external triggers have been identified, details of specific protein interactions and mechanisms have remained difficult to study due to the hydrophobic nature of the IpaB and IpaC translocator proteins. Here we explored the ability for a series of soluble N-terminal IpaB peptides to interact with IpaD. We found that DOC is required for the interaction and that a region of IpaB between residues 11–27 is required for maximum binding, which was confirmed in vivo. Furthermore, intramolecular FRET measurements indicated that movement of the IpaD distal domain away from the protein core accompanied the binding of IpaB11-226. Together these new findings provide important new insight into the interactions and potential mechanisms that define the maturation of the Shigella T3SA needle tip complex and provide a foundation for further studies probing T3SS activation

High Rate of Hypothyroidism in Multidrug-Resistant Tuberculosis Patients Co-Infected with HIV in Mumbai, India.

Adverse events (AEs) among HIV-infected patients with multidrug-resistant tuberculosis (MDR-TB) receiving anti-TB and antiretroviral treatments (ART) are under-researched and underreported. Hypothyroidism is a common AE associated with ethionamide, p-aminosalicylic acid (PAS), and stavudine. The aim of this study was to determine the frequency of and risk factors associated with hypothyroidism in HIV/MDR-TB co-infected patients

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients

Downregulation of RKIP Is Associated with Poor Outcome and Malignant Progression in Gliomas

Malignant gliomas are highly infiltrative and invasive tumors, which precludes the few treatment options available. Therefore, there is an urgent need to elucidate the molecular mechanisms underlying gliomas aggressive phenotype and poor prognosis. The Raf Kinase Inhibitory protein (RKIP), besides regulating important intracellular signaling cascades, was described to be associated with progression, metastasis and prognosis in several human neoplasms. Its role in the prognosis and tumourigenesis of gliomas remains unclear

Terrestrial behavior in titi monkeys (Callicebus, Cheracebus, and Plecturocebus) : potential correlates, patterns, and differences between genera

For arboreal primates, ground use may increase dispersal opportunities, tolerance to habitat change, access to ground-based resources, and resilience to human disturbances, and so has conservation implications. We collated published and unpublished data from 86 studies across 65 localities to assess titi monkey (Callicebinae) terrestriality. We examined whether the frequency of terrestrial activity correlated with study duration (a proxy for sampling effort), rainfall level (a proxy for food availability seasonality), and forest height (a proxy for vertical niche dimension). Terrestrial activity was recorded frequently for Callicebus and Plecturocebus spp., but rarely for Cheracebus spp. Terrestrial resting, anti-predator behavior, geophagy, and playing frequencies in Callicebus and Plecturocebus spp., but feeding and moving differed. Callicebus spp. often ate or searched for new leaves terrestrially. Plecturocebus spp. descended primarily to ingest terrestrial invertebrates and soil. Study duration correlated positively and rainfall level negatively with terrestrial activity. Though differences in sampling effort and methods limited comparisons and interpretation, overall, titi monkeys commonly engaged in a variety of terrestrial activities. Terrestrial behavior in Callicebus and Plecturocebus capacities may bolster resistance to habitat fragmentation. However, it is uncertain if the low frequency of terrestriality recorded for Cheracebus spp. is a genus-specific trait associated with a more basal phylogenetic position, or because studies of this genus occurred in pristine habitats. Observations of terrestrial behavior increased with increasing sampling effort and decreasing food availability. Overall, we found a high frequency of terrestrial behavior in titi monkeys, unlike that observed in other pitheciids

Patent filing by multinationals in a newly industrialising country: a study of the Indian pesticides sector

This paper examines the determinants of inter-firm differences in MNCs (Multinational Corporations) patent filing patterns in India, in pesticides. The variables, R&D intensity of the foreign firms, international orientation of the foreign firms, areawise patent filing, role of Indian subsidiary and the effect of firms being world market leaders, are used to explain the inter-firm differences in patent filing pattern. The study finds that the firms with international orientation, but not necessarily Indian orientation, file more patents in India. More R&D-intensive firms are not filing patents in India and also more patents are being filed in herbicides, whose production and consumption is less compared to insecticides in India. Subsidiaries operating in India have no influence on the patent filing pattern.