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

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Differentially regulated splice variants and systems biology analysis of Kaposi's sarcoma-associated herpesvirus-infected lymphatic endothelial cells

Alternative RNA splicing greatly increases proteome diversity, and the possibility of studying genome-wide alternative splicing (AS) events becomes available with the advent of high-throughput genomics tools devoted to this issue. Kaposi's sarcoma associated herpesvirus (KSHV) is the etiological agent of KS, a tumor of lymphatic endothelial cell (LEC) lineage, but little is known about the AS variations induced by KSHV. We analyzed KSHV-controlled AS using high-density microarrays capable of detecting all exons in the human genome. Splicing variants and altered exon–intron usage in infected LEC were found, and these correlated with protein domain modification. The different 3′-UTR used in new transcripts also help isoforms to escape microRNA-mediated surveillance. Exome-level analysis further revealed information that cannot be disclosed using classical gene-level profiling: a significant exon usage difference existed between LEC and CD34+ precursor cells, and KSHV infection resulted in LEC-to-precursor, dedifferentiation-like exon level reprogramming. Our results demonstrate the application of exon arrays in systems biology research, and suggest the regulatory effects of AS in endothelial cells are far more complex than previously observed. This extra layer of molecular diversity helps to account for various aspects of endothelial biology, KSHV life cycle and disease pathogenesis that until now have been unexplored

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

Updated guidance on the management of COVID-19:from an American Thoracic Society/European Respiratory Society coordinated International Task Force (29 July 2020)

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome-coronavirus-2. Consensus suggestions can standardise care, thereby improving outcomes and facilitating future research. METHODS: An International Task Force was composed and agreement regarding courses of action was measured using the Convergence of Opinion on Recommendations and Evidence (CORE) process. 70% agreement was necessary to make a consensus suggestion. RESULTS: The Task Force made consensus suggestions to treat patients with acute COVID-19 pneumonia with remdesivir and dexamethasone but suggested against hydroxychloroquine except in the context of a clinical trial; these are revisions of prior suggestions resulting from the interim publication of several randomised trials. It also suggested that COVID-19 patients with a venous thromboembolic event be treated with therapeutic anticoagulant therapy for 3 months. The Task Force was unable to reach sufficient agreement to yield consensus suggestions for the post-hospital care of COVID-19 survivors. The Task Force fell one vote shy of suggesting routine screening for depression, anxiety and post-traumatic stress disorder. CONCLUSIONS: The Task Force addressed questions related to pharmacotherapy in patients with COVID-19 and the post-hospital care of survivors, yielding several consensus suggestions. Management options for which there is insufficient agreement to formulate a suggestion represent research priorities.status: Published onlin

A Gammaherpesvirus Complement Regulatory Protein Promotes Initiation of Infection by Activation of Protein Kinase Akt/PKB

BACKGROUND: Viruses have evolved to evade the host's complement system. The open reading frames 4 (ORF4) of gammaherpesviruses encode homologs of regulators of complement activation (RCA) proteins, which inhibit complement activation at the level of C3 and C4 deposition. Besides complement regulation, these proteins are involved in heparan sulfate and glycosaminoglycan binding, and in case of MHV-68, also in viral DNA synthesis in macrophages. METHODOLOGY/PRINCIPAL FINDINGS: Here, we made use of MHV-68 to study the role of ORF4 during infection of fibroblasts. While attachment and penetration of virions lacking the RCA protein were not affected, we observed a delayed delivery of the viral genome to the nucleus of infected cells. Analysis of the phosphorylation status of a variety of kinases revealed a significant reduction in phosphorylation of the protein kinase Akt in cells infected with ORF4 mutant virus, when compared to cells infected with wt virus. Consistent with a role of Akt activation in initial stages of infection, inhibition of Akt signaling in wt virus infected cells resulted in a phenotype resembling the phenotype of the ORF4 mutant virus, and activation of Akt by addition of insulin partially reversed the phenotype of the ORF4 mutant virus. Importantly, the homologous ORF4 of KSHV was able to rescue the phenotype of the MHV-68 ORF4 mutant, indicating that ORF4 is functionally conserved and that ORF4 of KSHV might have a similar function in infection initiation. CONCLUSIONS/SIGNIFICANCE: In summary, our studies demonstrate that ORF4 contributes to efficient infection by activation of the protein kinase Akt and thus reveal a novel function of a gammaherpesvirus RCA protein

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted, with the protocol registered on the Open Science Framework. We hypothesized that gene expression analysis could provide a foundation for establishing a clinical research framework for sepsis. A comprehensive search of the PubMed database was conducted with a particular focus on original research and systematic reviews of transcriptomic sepsis studies published between 2012 and 2022. Both coding and non-coding gene expression studies have been included in this review. An effort was made to enhance the understanding of sepsis at the mRNA gene expression level by applying a systems biology approach through transcriptomic analysis. Seven crucial components related to sepsis research were addressed in this study: endotyping (n = 64), biomarker (n = 409), definition (n = 0), diagnosis (n = 1098), progression (n = 124), severity (n = 451), and benchmark (n = 62). These components were classified into two groups, with one focusing on Biomarkers and Endotypes and the other oriented towards clinical aspects. Our review of the selected studies revealed a compelling association between gene transcripts and clinical sepsis, reinforcing the proposed research framework. Nevertheless, challenges have arisen from the lack of consensus in the sepsis terminology employed in research studies and the absence of a comprehensive definition of sepsis. There is a gap in the alignment between the notion of sepsis as a clinical phenomenon and that of laboratory indicators. It is potentially responsible for the variable number of patients within each category. Ideally, future studies should incorporate a transcriptomic perspective. The integration of transcriptomic data with clinical endpoints holds significant potential for advancing sepsis research, facilitating a consensus-driven approach, and enabling the precision management of sepsis