{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T13:39:19Z","timestamp":1792503559649,"version":"4.5.0"},"reference-count":296,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCDD"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jcdd9080268","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T20:58:08Z","timestamp":1660597088000},"page":"268","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report"],"prefix":"10.3390","volume":"9","author":[{"given":"Narendra N.","family":"Khanna","sequence":"first","affiliation":[{"name":"Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0813-4906","authenticated-orcid":false,"given":"Mahesh","family":"Maindarkar","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"},{"name":"Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anudeep","family":"Puvvula","sequence":"additional","affiliation":[{"name":"Stroke Monitoring and Diagnostic Division, AtheroPoint\u2122, Roseville, CA 95661, USA"},{"name":"Annu\u2019s Hospitals for Skin and Diabetes, Nellore 524101, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9856-539X","authenticated-orcid":false,"given":"Sudip","family":"Paul","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, North Eastern Hill 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Sci."},{"key":"ref_2","first-page":"351","article-title":"The impact of the COVID-19 pandemic on Parkinson\u2019s disease: Hidden sorrows and emerging opportunities","volume":"10","author":"Helmich","year":"2020","journal-title":"J. Park. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"590134","DOI":"10.3389\/fpsyt.2020.590134","article-title":"Changes in motor, cognitive, and behavioral symptoms in Parkinson\u2019s disease and mild cognitive impairment during the COVID-19 lockdown","volume":"11","author":"Baschi","year":"2020","journal-title":"Front. Psychiatry"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1001\/jama.2020.1585","article-title":"Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus\u2013infected pneumonia in Wuhan, China","volume":"323","author":"Wang","year":"2020","journal-title":"JAMA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103960","DOI":"10.1016\/j.compbiomed.2020.103960","article-title":"COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review","volume":"124","author":"Suri","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1186\/s13054-020-03062-7","article-title":"The vascular endothelium: The cornerstone of organ dysfunction in severe SARS-CoV-2 infection","volume":"24","author":"Pons","year":"2020","journal-title":"Crit. Care"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1161\/CIRCULATIONAHA.112.093245","article-title":"The assessment of endothelial function: From research into clinical practice","volume":"126","author":"Flammer","year":"2012","journal-title":"Circulation"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1161\/01.ATV.0000051384.43104.FC","article-title":"Endothelial dysfunction: A marker of atherosclerotic risk","volume":"23","author":"Bonetti","year":"2003","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1016\/S0140-6736(20)30937-5","article-title":"Endothelial cell infection and endotheliitis in COVID-19","volume":"395","author":"Varga","year":"2020","journal-title":"Lancet"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41577-020-0343-0","article-title":"COVID-19: The vasculature unleashed","volume":"20","author":"Teuwen","year":"2020","journal-title":"Nat. Rev. Immunol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.atherosclerosis.2020.10.014","article-title":"Endothelial dysfunction in COVID-19: Current findings and therapeutic implications","volume":"314","author":"Haubner","year":"2020","journal-title":"Atheroscler."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1016\/j.cell.2020.04.004","article-title":"Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2","volume":"181","author":"Monteil","year":"2020","journal-title":"Cell"},{"key":"ref_13","first-page":"5063","article-title":"Vaccine-induced severe thrombotic thrombocytopenia following COVID-19 vaccination: A report of an autoptic case and review of the literature","volume":"25","author":"Fanni","year":"2021","journal-title":"Eur. Rev. Med Pharm. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.21037\/cdt-20-561","article-title":"Can COVID-19 trigger the plaque vulnerability\u2014A Kounis syndrome warning for \u201casymptomatic subjects\u201d","volume":"10","author":"Saba","year":"2020","journal-title":"J. Cardiovasc. Diagn. Ther."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"El-Baz, A.S., Acharya, R., Mirmehdi, M., and Suri, J.S. (2011). Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-8195-0"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Suri, J.S., and Rangayyan, R.M. (2006). Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer, SPIE.","DOI":"10.1117\/3.651880"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ejrad.2019.02.038","article-title":"The present and future of deep learning in radiology","volume":"114","author":"Saba","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s10916-017-0797-1","article-title":"Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization","volume":"41","author":"Kuppili","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103804","DOI":"10.1016\/j.compbiomed.2020.103804","article-title":"Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm","volume":"122","author":"Tandel","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_20","unstructured":"Acharya, R., Ng, Y.E., and Suri, J.S. (2008). Image Modeling of the Human Eye, Artech House."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1007\/s10554-020-02099-7","article-title":"Multiclass machine learning vs. conventional calculators for stroke\/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: A 500 participants study","volume":"37","author":"Jamthikar","year":"2021","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1007\/s00296-020-04691-5","article-title":"Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging","volume":"40","author":"Jamthikar","year":"2020","journal-title":"Rheumatol. Int."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"215","DOI":"10.4239\/wjd.v12.i3.215","article-title":"Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review","volume":"12","author":"Viswanathan","year":"2021","journal-title":"World J. Diabetes"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11517-018-1897-x","article-title":"Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: A tool for stroke risk","volume":"57","author":"Biswas","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.ultras.2011.11.003","article-title":"Non-invasive automated 3D thyroid lesion classification in ultrasound: A class of ThyroScan\u2122 systems","volume":"52","author":"Acharya","year":"2012","journal-title":"Ultrasonics"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"245","DOI":"10.7863\/ultra.33.2.245","article-title":"Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population","volume":"33","author":"Acharya","year":"2014","journal-title":"J. Ultrasound Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1007\/s10278-012-9553-8","article-title":"Ovarian tumor characterization and classification using ultrasound\u2014A new online paradigm","volume":"26","author":"Acharya","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"501","DOI":"10.3109\/02652048.2013.879932","article-title":"Evaluation of anticancer activity of celastrol liposomes in prostate cancer cells","volume":"31","author":"Wolfram","year":"2014","journal-title":"J. Microencapsul."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cmpb.2017.07.011","article-title":"A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification","volume":"150","author":"Shrivastava","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6184","DOI":"10.1016\/j.eswa.2015.03.014","article-title":"Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm","volume":"42","author":"Shrivastava","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1109\/TIM.2011.2174897","article-title":"An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans","volume":"61","author":"Acharya","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s10916-017-0745-0","article-title":"Plaque tissue morphology-based stroke risk stratification using carotid ultrasound: A polling-based PCA learning paradigm","volume":"41","author":"Saba","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_33","unstructured":"Liu, K., and Suri, J.S. (2005). Automatic Vessel Indentification for Angiographic Screening. (6,845,260), U.S. Patent."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","article-title":"Heart rate variability: A review","volume":"44","author":"Kannathal","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.cmpb.2016.02.004","article-title":"PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology","volume":"128","author":"Araki","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"208","DOI":"10.21037\/cdt.2016.03.08","article-title":"Numerical analysis of the effect of turbulence transition on the hemodynamic parameters in human coronary arteries","volume":"6","author":"Mahalingam","year":"2016","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Agarwal, S., Carriero, A., Pasch\u00e8, A., Danna, P.S., Columbu, M., Saba, L., Viskovic, K., Mehmedovi\u0107, A., and Agarwal, S. (2021). COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122367"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Battineni, G., Chintalapudi, N., Amenta, F., and Traini, E. (2020). A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer\u2019s Disease (AD) in Older Subjects. J. Clin. Med., 9.","DOI":"10.3390\/jcm9072146"},{"key":"ref_39","unstructured":"Saba, L., and Suri, J.S. (2013). Multi-Detector CT Imaging: Abdomen, Pelvis, and CAD Applications, CRC Press."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sanches, J.M., Laine, A.F., and Suri, J.S. (2012). Ultrasound Imaging, Springer.","DOI":"10.1007\/978-1-4614-1180-2"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s11517-012-1019-0","article-title":"Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: A paradigm for stroke risk assessment","volume":"51","author":"Acharya","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cmpb.2012.09.008","article-title":"Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization","volume":"110","author":"Acharya","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11883-018-0736-8","article-title":"A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography","volume":"20","author":"Boi","year":"2018","journal-title":"Curr. Atheroscler. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sanagala, S.S., Nicolaides, A., Gupta, S.K., Koppula, V.K., Saba, L., Agarwal, S., Johri, A.M., Kalra, M.S., and Suri, J.S. (2021). Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112109"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105131","DOI":"10.1016\/j.compbiomed.2021.105131","article-title":"A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework","volume":"141","author":"Skandha","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s10916-021-01707-w","article-title":"A novel block imaging technique using nine artificial intelligence models for COVID-19 disease classification, characterization and severity measurement in lung computed tomography scans on an Italian cohort","volume":"45","author":"Agarwal","year":"2021","journal-title":"J. Med. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4128","DOI":"10.1109\/JBHI.2021.3103839","article-title":"Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective","volume":"25","author":"Suri","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"109603","DOI":"10.1016\/j.mehy.2020.109603","article-title":"Early diagnosis of Parkinson\u2019s disease using machine learning algorithms","volume":"138","author":"Senturk","year":"2020","journal-title":"Med. Hypotheses"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s13721-017-0155-8","article-title":"A comparison of soft computing models for Parkinson\u2019s disease diagnosis using voice and gait features","volume":"6","author":"Janghel","year":"2017","journal-title":"Netw. Modeling Anal. Health Inform. Bioinform."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3389\/fnagi.2021.633752","article-title":"Machine learning for the diagnosis of parkinson\u2019s disease: A review of literature","volume":"13","author":"Mei","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.virol.2015.03.043","article-title":"Viral membrane fusion","volume":"479","author":"Harrison","year":"2015","journal-title":"Virology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/s41579-018-0118-9","article-title":"Origin and evolution of pathogenic coronaviruses","volume":"17","author":"Cui","year":"2019","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1038\/nrmicro2090","article-title":"The spike protein of SARS-CoV\u2014A target for vaccine and therapeutic development","volume":"7","author":"Du","year":"2009","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_54","unstructured":"Howley, P.M., and Knipe, D.M. (2020). Fields Virology: Emerging Viruses, Lippincott Williams & Wilkins."},{"key":"ref_55","first-page":"1824","article-title":"Pharmacologic treatments for coronavirus disease 2019 (COVID-19): A review","volume":"323","author":"Sanders","year":"2020","journal-title":"JAMA"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1038\/d41573-020-00016-0","article-title":"Therapeutic options for the 2019 novel coronavirus (2019-nCoV)","volume":"19","author":"Li","year":"2020","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1056\/NEJMoa2001282","article-title":"A trial of lopinavir\u2013ritonavir in adults hospitalized with severe COVID-19","volume":"382","author":"Cao","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1086\/375233","article-title":"A review of low-dose ritonavir in protease inhibitor combination therapy","volume":"36","author":"Cooper","year":"2003","journal-title":"Clin. Infect. Dis."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1001\/jama.2020.2648","article-title":"Characteristics of and Important Lessons from the 367 Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 368 72314 Cases from the Chinese Center for Disease Control and Prevention","volume":"323","author":"Wu","year":"2020","journal-title":"JAMA"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1038\/nri2171","article-title":"Evolving functions of endothelial cells in inflammation","volume":"7","author":"Pober","year":"2007","journal-title":"Nat. Rev. Immunol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.1161\/CIRCULATIONAHA.104.510461","article-title":"Effect of angiotensin-converting enzyme inhibition and angiotensin II receptor blockers on cardiac angiotensin-converting enzyme 2","volume":"111","author":"Ferrario","year":"2005","journal-title":"Circulation"},{"key":"ref_62","first-page":"1","article-title":"Bioregulation of kinins: Kallikreins, kininogens, and kininases","volume":"44","author":"Bhoola","year":"1992","journal-title":"Pharmacol. Rev."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"L17","DOI":"10.1152\/ajplung.00498.2016","article-title":"Attenuation of pulmonary ACE2 activity impairs inactivation of des-Arg9 bradykinin\/BKB1R axis and facilitates LPS-induced neutrophil infiltration","volume":"314","author":"Sodhi","year":"2018","journal-title":"Am. J. Physiol. Lung Cell. Mol. Physiol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.cell.2020.02.058","article-title":"Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein","volume":"181","author":"Walls","year":"2020","journal-title":"Cell"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4058","DOI":"10.1038\/s41598-022-07918-6","article-title":"ACE2 protein expression in lung tissues of severe COVID-19 infection","volume":"12","author":"Gheware","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"03009858221079665","DOI":"10.1177\/03009858221079665","article-title":"Histopathology and localization of SARS-CoV-2 and its host cell entry receptor ACE2 in tissues from naturally infected US-farmed mink (Neovison vison)","volume":"59","author":"Ritter","year":"2022","journal-title":"Vet. Pathol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1007\/s12250-020-00207-4","article-title":"Understanding SARS-CoV-2-mediated inflammatory responses: From mechanisms to potential therapeutic tools","volume":"35","author":"Fu","year":"2020","journal-title":"Virol. Sin."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1128\/JVI.01248-09","article-title":"Differential downregulation of ACE2 by the spike proteins of severe acute respiratory syndrome coronavirus and human coronavirus NL63","volume":"84","author":"Glowacka","year":"2010","journal-title":"J. Virol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1002\/rth2.12175","article-title":"Hereditary angioedema: Linking complement regulation to the coagulation system","volume":"3","author":"Levi","year":"2019","journal-title":"Res. Pract. Thromb. Haemost."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"101","DOI":"10.2147\/COPD.S329783","article-title":"SARS-CoV-2 (COVID-19) Adhesion Site Protein Upregulation in Small Airways, Type 2 Pneumocytes, and Alveolar Macrophages of Smokers and COPD\u2013Possible Implications for Interstitial Fibrosis","volume":"17","author":"Brake","year":"2022","journal-title":"Int. J. Chronic Obstr. Pulm. Dis."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e00638-15","DOI":"10.1128\/mBio.00638-15","article-title":"Toll-like receptor 3 signaling via TRIF contributes to a protective innate immune response to severe acute respiratory syndrome coronavirus infection","volume":"6","author":"Totura","year":"2015","journal-title":"MBio"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/S0140-6736(20)30628-0","article-title":"COVID-19: Consider cytokine storm syndromes and immunosuppression","volume":"395","author":"Mehta","year":"2020","journal-title":"Lancet"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1002\/jmv.23354","article-title":"Viral infections and mechanisms of thrombosis and bleeding","volume":"84","author":"Goeijenbier","year":"2012","journal-title":"J. Med. Virol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4188","DOI":"10.1007\/s12035-021-02457-z","article-title":"COVID-19 Infection and Circulating Microparticles\u2014Reviewing Evidence as Microthrombogenic Risk Factor for Cerebral Small Vessel Disease","volume":"58","author":"Hashim","year":"2021","journal-title":"Mol. Neurobiol."},{"key":"ref_75","first-page":"282","article-title":"The clinical pathology of severe acute respiratory syndrome (SARS): A report from China","volume":"200","author":"Ding","year":"2003","journal-title":"J. Pathol. J. Pathol. Soc. Great Br. Irel."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1002\/jmv.25709","article-title":"Overlapping and discrete aspects of the pathology and pathogenesis of the emerging human pathogenic coronaviruses SARS-CoV, MERS-CoV, and 2019-nCoV","volume":"92","author":"Liu","year":"2020","journal-title":"J. Med. Virol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1056\/NEJMoa2015432","article-title":"Pulmonary vascular endothelialitis, thrombosis, and angiogenesis in COVID-19","volume":"383","author":"Ackermann","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1080\/20009666.2021.1921908","article-title":"Resolution of pulmonary artery thrombosis in patients with moderate COVID-19 disease","volume":"11","author":"Zanframundo","year":"2021","journal-title":"J. Community Hosp. Intern. Med. Perspect."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"e069590","DOI":"10.1136\/bmj-2021-069590","article-title":"Risks of deep vein thrombosis, pulmonary embolism, and bleeding after covid-19: Nationwide self-controlled cases series and matched cohort study","volume":"377","author":"Katsoularis","year":"2022","journal-title":"BMJ"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12959-020-00255-6","article-title":"Fatal pulmonary arterial thrombosis in a COVID-19 patient, with asymptomatic history, occurred after swab negativization","volume":"19","author":"Colombo","year":"2021","journal-title":"Thromb. J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"139","DOI":"10.7326\/L20-1275","article-title":"Pulmonary arterial thrombosis in COVID-19 with fatal outcome","volume":"174","author":"Nichols","year":"2021","journal-title":"Ann. Intern. Med."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e200277","DOI":"10.1148\/ryct.2020200277","article-title":"Pulmonary vascular manifestations of COVID-19 pneumonia","volume":"2","author":"Lang","year":"2020","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1148\/rg.282075705","article-title":"Velocity-encoded cine MR imaging in aortic coarctation: Functional assessment of hemodynamic events","volume":"28","author":"Hom","year":"2008","journal-title":"Radiographics"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4640788","DOI":"10.1155\/2022\/4640788","article-title":"Cardiorenal Disease in COVID-19 Patients","volume":"2022","author":"Sifaat","year":"2022","journal-title":"J. Renin-Angiotensin-Aldosterone Syst."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1097\/CM9.0000000000000774","article-title":"Persistence and clearance of viral RNA in 2019 novel coronavirus disease rehabilitation patients","volume":"133","author":"Ling","year":"2020","journal-title":"Chin. Med. J."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s11684-020-0754-0","article-title":"Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection","volume":"14","author":"Zou","year":"2020","journal-title":"Front. Med."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1159\/000342483","article-title":"Serum uric acid in patients with Parkinson\u2019s disease and vascular parkinsonism: A cross-sectional study","volume":"20","author":"Pan","year":"2013","journal-title":"Neuroimmunomodulation"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Diao, B., Feng, Z., Wang, C., Wang, H., Liu, L., Wang, C., Wang, R., Liu, Y., Liu, Y., and Wang, G. (2020). Human kidney is a target for novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. MedRxiv.","DOI":"10.1101\/2020.03.04.20031120"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.kint.2020.04.003","article-title":"Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China","volume":"98","author":"Su","year":"2020","journal-title":"Kidney Int."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1681\/ASN.2020040419","article-title":"Acute kidney injury in COVID-19: Emerging evidence of a distinct pathophysiology","volume":"31","author":"Batlle","year":"2020","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Li, Z., Wu, M., Yao, J., Guo, J., Liao, X., Song, S., Li, J., Duan, G., Zhou, Y., and Wu, X. (2020). Caution on kidney dysfunctions of COVID-19 patients. MedRxiv.","DOI":"10.1101\/2020.02.08.20021212"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tcm.2020.10.005","article-title":"COVID-19\u2014A vascular disease","volume":"31","author":"Siddiqi","year":"2021","journal-title":"Trends Cardiovasc. Med."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.avsg.2020.07.013","article-title":"Femoral arterial thrombosis in a young adult after nonsevere COVID-19","volume":"6","author":"Veyre","year":"2020","journal-title":"Ann. Vasc. Surg."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1016\/j.acra.2020.07.019","article-title":"Assessment of small pulmonary blood vessels in COVID-19 patients using HRCT","volume":"27","author":"Lins","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1186\/s13054-020-02931-5","article-title":"Severe pulmonary embolism in COVID-19 patients: A call for increased awareness","volume":"24","author":"Lebreton","year":"2020","journal-title":"Crit. Care"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1007\/s00330-020-07300-y","article-title":"CT imaging of pulmonary embolism in patients with COVID-19 pneumonia: A retrospective analysis","volume":"31","author":"Espallargas","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1016\/j.ajem.2020.07.054","article-title":"Pulmonary embolism in COVID-19: Clinical characteristics and cardiac implications","volume":"38","author":"Kho","year":"2020","journal-title":"Am. J. Emerg. Med."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1093\/eurheartj\/ehab314","article-title":"Pulmonary embolism in patients with COVID-19: Incidence, risk factors, clinical characteristics, and outcome","volume":"42","author":"Mebazaa","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_99","first-page":"887","article-title":"Pulmonary vascular pathology in COVID-19","volume":"383","author":"Scholkmann","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ijcard.2020.04.028","article-title":"Acute pulmonary embolism in COVID-19 disease: Preliminary report on seven patients","volume":"313","author":"Faggiano","year":"2020","journal-title":"Int. J. Cardiol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1128\/MMBR.05015-11","article-title":"Into the eye of the cytokine storm","volume":"76","author":"Tisoncik","year":"2012","journal-title":"Microbiol. Mol. Biol. Rev."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"153104","DOI":"10.1016\/j.tox.2022.153104","article-title":"Close association between the synergistic toxicity of zearalenone-deoxynivalenol combination and microRNA221-mediated PTEN\/PI3K\/AKT signaling in HepG2 cells","volume":"468","author":"Rong","year":"2022","journal-title":"Toxicology"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"2928","DOI":"10.1080\/15384101.2019.1662678","article-title":"Neutrophil extracellular traps induced by IL-8 aggravate atherosclerosis via activation NF-\u03baB signaling in macrophages","volume":"18","author":"An","year":"2019","journal-title":"Cell Cycle"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"102763","DOI":"10.1016\/j.ebiom.2020.102763","article-title":"Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients","volume":"55","author":"Liu","year":"2020","journal-title":"EBioMedicine"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1038\/nm1633","article-title":"Adaptive immune cells temper initial innate responses","volume":"13","author":"Kim","year":"2007","journal-title":"Nat. Med."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1002\/jmv.25900","article-title":"High IL-6\/IFN-\u03b3 ratio could be associated with severe disease in COVID-19 patients","volume":"92","year":"2020","journal-title":"J. Med. Virol."},{"key":"ref_107","first-page":"137244","article-title":"Clinical and immunologic features in severe and moderate forms of Coronavirus Disease","volume":"82","author":"Chen","year":"2019","journal-title":"J. Clin. Investig."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"102789","DOI":"10.1016\/j.ebiom.2020.102789","article-title":"A glimpse into the eye of the COVID-19 cytokine storm","volume":"55","author":"Kuppalli","year":"2020","journal-title":"EBioMedicine"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/s41584-020-0474-5","article-title":"Emerging evidence of a COVID-19 thrombotic syndrome has treatment implications","volume":"16","author":"Merrill","year":"2020","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"180","DOI":"10.37899\/journallamedihealtico.v3i3.647","article-title":"Some Patterns of Haemostatic Parameters Among Pregnant Women with Hypertensive Disorders in Owerri, Imo State, Nigeria","volume":"3","author":"Nnodim","year":"2022","journal-title":"J. Medihealtico"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/j.juro.2015.03.119","article-title":"Robotic level III inferior vena cava tumor thrombectomy: Initial series","volume":"194","author":"Gill","year":"2015","journal-title":"J. Urol."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"e00968","DOI":"10.1016\/j.idcr.2020.e00968","article-title":"Renal artery thrombosis in COVID-19","volume":"22","author":"Acharya","year":"2020","journal-title":"IDCases"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1093\/ckj\/sfaa141","article-title":"Renal artery thrombosis induced by COVID-19","volume":"13","author":"Philipponnet","year":"2020","journal-title":"Clin. Kidney J."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1007\/s00134-020-06153-9","article-title":"Acute kidney injury in critically ill patients with COVID-19","volume":"46","author":"Gabarre","year":"2020","journal-title":"Intensive Care Med."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"111966","DOI":"10.1016\/j.biopha.2021.111966","article-title":"Kidney injury in COVID-19 patients, drug development and their renal complications: Review study","volume":"142","author":"Yarijani","year":"2021","journal-title":"Biomed. Pharmacother."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"267","DOI":"10.4103\/iju.IJU_76_21","article-title":"Renal artery thrombosis and mucormycosis in a COVID-19 patient","volume":"37","author":"Singh","year":"2021","journal-title":"Indian J. Urol."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.xkme.2020.07.010","article-title":"Bilateral renal artery thrombosis in a patient with COVID-19","volume":"3","author":"Coca","year":"2021","journal-title":"Kidney Med."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1097\/SHK.0000000000001659","article-title":"Critically ill COVID-19 patients with acute kidney injury have reduced renal blood flow and perfusion despite preserved cardiac function: A case-control study using contrast-enhanced ultrasound","volume":"55","author":"Watchorn","year":"2021","journal-title":"Shock"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.clinimag.2020.11.011","article-title":"Renal ultrasound findings secondary to COVID-19 related collapsing focal segmental glomerulosclerosis\u2014A case report","volume":"71","author":"Tancredi","year":"2021","journal-title":"Clin. Imaging"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"E181","DOI":"10.1148\/radiol.2020201623","article-title":"Pulmonary, cerebral, and renal thromboembolic disease in a patient with COVID-19","volume":"296","author":"Lushina","year":"2020","journal-title":"Radiology"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.4269\/ajtmh.20-0869","article-title":"Case Report: COVID-19 Associated renal infarction and ascending aortic thrombosis","volume":"103","author":"Mukherjee","year":"2020","journal-title":"Am. J. Trop. Med. Hyg."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/S0140-6736(20)30566-3","article-title":"Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study","volume":"395","author":"Zhou","year":"2020","journal-title":"Lancet"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"2950","DOI":"10.1016\/j.jacc.2020.04.031","article-title":"COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-Up: JACC State-of-the-Art Review","volume":"75","author":"Bikdeli","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.immuni.2020.06.017","article-title":"Cytokine storms: Understanding COVID-19","volume":"53","author":"Mangalmurti","year":"2020","journal-title":"Immunity"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/S0304-4157(98)00018-5","article-title":"Lipid\u2013protein interactions in blood coagulation","volume":"1376","author":"Zwaal","year":"1998","journal-title":"Biochim. Biophys. Acta Rev. Biomembr."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/nri3345","article-title":"Thrombosis as an intravascular effector of innate immunity","volume":"13","author":"Engelmann","year":"2013","journal-title":"Nat. Rev. Immunol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.2174\/138920111798281171","article-title":"Therapeutic modulation of coagulation and fibrinolysis in acute lung injury and the acute respiratory distress syndrome","volume":"12","author":"Sebag","year":"2011","journal-title":"Curr. Pharm. Biotechnol."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"297","DOI":"10.12688\/f1000research.9692.1","article-title":"Advances in understanding the renin-angiotensin-aldosterone system (RAAS) in blood pressure control and recent pivotal trials of RAAS blockade in heart failure and diabetic nephropathy","volume":"6","author":"Ghazi","year":"2017","journal-title":"F1000Research"},{"key":"ref_129","unstructured":"Fountain, J.H., and Lappin, S.L. (2017). Physiology, Renin Angiotensin System, StatPearls Publishing."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1111\/j.1365-2362.2009.02153.x","article-title":"SARS-coronavirus modulation of myocardial ACE2 expression and inflammation in patients with SARS","volume":"39","author":"Oudit","year":"2009","journal-title":"Eur. J. Clin. Investig."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Baker, A. (2017). Recombinant Human ACE2: Acing out Angiotensin II in ARDS Therapy, Springer.","DOI":"10.1186\/s13054-017-1882-z"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"S12","DOI":"10.1046\/j.1523-1755.62.s82.4.x","article-title":"Angiotensin II regulates the synthesis of proinflammatory cytokines and chemokines in the kidney","volume":"62","author":"Ruperez","year":"2002","journal-title":"Kidney Int."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"El-Arif, G., Khazaal, S., Farhat, A., Harb, J., Annweiler, C., Wu, Y., Cao, Z., Kovacic, H., Abi Khattar, Z., and Fajloun, Z. (2022). Angiotensin II Type I Receptor (AT1R): The Gate towards COVID-19-Associated Diseases. Molecules, 27.","DOI":"10.3390\/molecules27072048"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/S1054-3589(08)60498-6","article-title":"Angiotensin II Recept. Pharmacology","volume":"28","author":"Griendling","year":"1994","journal-title":"Adv. Pharm."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1161\/01.RES.84.9.1043","article-title":"Upregulation of endothelial receptor for oxidized low-density lipoprotein (LOX-1) in cultured human coronary artery endothelial cells by angiotensin II type 1 receptor activation","volume":"84","author":"Li","year":"1999","journal-title":"Circ. Res."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Al-Tamimi, A.O., Yusuf, A.M., Jayakumar, M.N., Ansari, A.W., Elhassan, M., AbdulKarim, F., Kannan, M., Halwani, R., and Ahmad, F. (2022). SARS-CoV-2 infection induces soluble platelet activation markers and PAI-1 in the early moderate stage of COVID-19. Int. J. Lab. Hematol.","DOI":"10.1101\/2021.08.23.457378"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1161\/01.ATV.20.8.2019","article-title":"Plasma levels of tissue plasminogen activator\/plasminogen activator inhibitor-1 complex and von Willebrand factor are significant risk markers for recurrent myocardial infarction in the Stockholm Heart Epidemiology Program (SHEEP) study","volume":"20","author":"Wiman","year":"2000","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1016\/j.jacc.2020.06.080","article-title":"Multimodality imaging in evaluation of cardiovascular complications in patients with COVID-19: JACC scientific expert panel","volume":"76","author":"Rudski","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"E670","DOI":"10.1002\/ccd.29056","article-title":"Severe coronary spasm in a COVID-19 patient","volume":"97","author":"Rivero","year":"2021","journal-title":"Catheter. Cardiovasc. Interv."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1111\/jocs.14538","article-title":"Cardiac involvement in COVID-19 patients: Risk factors, predictors, and complications: A review","volume":"35","author":"Aghagoli","year":"2020","journal-title":"J. Card. Surg."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.clinimag.2021.02.016","article-title":"Coronary artery calcification in COVID-19 patients: An imaging biomarker for adverse clinical outcomes","volume":"77","author":"Gupta","year":"2021","journal-title":"Clin. Imaging"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.1007\/s00259-021-05375-3","article-title":"A comprehensive review of imaging findings in COVID-19-status in early 2021","volume":"48","author":"Prosch","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13244-021-00973-z","article-title":"Role of advanced imaging in COVID-19 cardiovascular complications","volume":"12","author":"Catapano","year":"2021","journal-title":"Insights Into Imaging"},{"key":"ref_144","first-page":"1677","article-title":"Intraluminal carotid artery thrombus in COVID-19: Another danger of cytokine storm?","volume":"41","author":"Mohamud","year":"2020","journal-title":"Am. J. Neuroradiol."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1161\/CIRCULATIONAHA.120.047525","article-title":"ST-elevation myocardial infarction in patients with COVID-19: Clinical and angiographic outcomes","volume":"141","author":"Stefanini","year":"2020","journal-title":"Circulation"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Acharya, U.R., Joseph, K.P., Kannathal, N., Min, L.C., and Suri, J.S. (2007). Heart rate variability. Advances in Cardiac Signal Processing, Springer.","DOI":"10.1007\/978-3-540-36675-1_5"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.wneu.2020.08.154","article-title":"Common carotid artery occlusion in a young patient: Can large-vessel stroke be the initial clinical manifestation of coronavirus disease 2019?","volume":"144","author":"Alkhaibary","year":"2020","journal-title":"World Neurosurg."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.neurad.2020.04.003","article-title":"Acute ischemic stroke complicating common carotid artery thrombosis during a severe COVID-19 infection","volume":"47","author":"Viguier","year":"2020","journal-title":"J. Neuroradiol."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"671669","DOI":"10.3389\/fcvm.2021.671669","article-title":"Case report: Changes of vascular reactivity and arterial stiffness in a patient with covid-19 infection","volume":"8","author":"Jud","year":"2021","journal-title":"Front. Cardiovasc. Med."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.clinimag.2020.07.007","article-title":"Rare presentations of COVID-19: PRES-like leukoencephalopathy and carotid thrombosis","volume":"69","author":"Doo","year":"2021","journal-title":"Clin. Imaging"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1111\/jon.12803","article-title":"Changes in Neuroendovascular Procedural Volume During the COVID-19 Pandemic: An International Multicenter Study","volume":"31","author":"Qureshi","year":"2021","journal-title":"J. Neuroimaging"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"7397480","DOI":"10.1155\/2020\/7397480","article-title":"Acute ischemic stroke in COVID-19: Putative mechanisms, clinical characteristics, and management","volume":"2020","author":"Ojo","year":"2020","journal-title":"Neurol. Res. Int."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.52586\/5026","article-title":"Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: A narrative review","volume":"26","author":"Munjral","year":"2021","journal-title":"Front. Biosci."},{"key":"ref_154","unstructured":"Function, J. (2021). Is Carotid Body Infection Responsible for Silent Hypoxemia in COVID-19 Patients?, Oxford University Press."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1016\/j.jvs.2021.11.064","article-title":"Impact of the first COVID-19 pandemic peak and lockdown on the interventional management of carotid artery stenosis in France","volume":"75","author":"Crespy","year":"2022","journal-title":"J. Vasc. Surg."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.cmpb.2017.12.016","article-title":"Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm","volume":"155","author":"Biswas","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s10916-015-0214-6","article-title":"Automatic lung segmentation using control feedback system: Morphology and texture paradigm","volume":"39","author":"Noor","year":"2015","journal-title":"J. Med. Syst."},{"key":"ref_158","unstructured":"El-Baz, A., and Suri, J.S. (2011). Lung Imaging and Computer Aided Diagnosis, CRC Press."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"289","DOI":"10.7785\/tcrt.2012.500381","article-title":"A review on ultrasound-based thyroid cancer tissue characterization and automated classification","volume":"13","author":"Acharya","year":"2014","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_160","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"392","DOI":"10.2741\/4725","article-title":"State-of-the-art review on deep learning in medical imaging","volume":"24","author":"Biswas","year":"2019","journal-title":"Front. Biosci."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"103065","DOI":"10.1016\/j.nicl.2022.103065","article-title":"Role of Artificial Intelligence in MS Clinical Practice","volume":"35","author":"Bonacchi","year":"2022","journal-title":"NeuroImage Clin."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"19777","DOI":"10.1149\/10701.19777ecst","article-title":"Analysis on Content Based Image Retrieval Using Image Enhancement and Deep Learning Convolutional Neural Networks","volume":"107","author":"Prasad","year":"2022","journal-title":"ECS Trans."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"106332","DOI":"10.1016\/j.cmpb.2021.106332","article-title":"Comparative analysis of pulmonary nodules segmentation using multiscale residual U-Net and fuzzy C-means clustering","volume":"209","author":"Shi","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Panwar, A., Semwal, G., Goel, S., and Gupta, S. (2022). Stratification of the lesions in color fundus images of diabetic retinopathy patients using deep learning models and machine learning classifiers. Edge Analytics, Springer.","DOI":"10.1007\/978-981-19-0019-8_49"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"109041","DOI":"10.1016\/j.ejrad.2020.109041","article-title":"Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study","volume":"128","author":"Wu","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"6517","DOI":"10.1007\/s00330-020-07044-9","article-title":"A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images","volume":"30","author":"Ni","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_169","unstructured":"Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., and Shi, Y. (2020). Lung infection quantification of covid-19 in ct images with deep learning. arXiv."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.3348\/kjr.2020.0536","article-title":"Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19","volume":"21","author":"Hwang","year":"2020","journal-title":"Korean J. Radiol."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"2676","DOI":"10.1109\/TMI.2020.2994459","article-title":"Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound","volume":"39","author":"Roy","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1002\/jmri.26887","article-title":"Differentiating Parkinson\u2019s disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load","volume":"51","author":"Fang","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"e200048","DOI":"10.1148\/ryai.2020200048","article-title":"Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT","volume":"2","author":"Chaganti","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"E65","DOI":"10.1148\/radiol.2020200905","article-title":"Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy","volume":"296","author":"Li","year":"2020","journal-title":"Radiology"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s10096-020-03901-z","article-title":"Classification of COVID-19 patients from chest CT images using multi-objective differential evolution\u2013based convolutional neural networks","volume":"39","author":"Singh","year":"2020","journal-title":"Eur. J. Clin. Microbiol. Infect. Dis."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"450","DOI":"10.21037\/atm.2020.03.132","article-title":"Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: A pilot study","volume":"8","author":"Yang","year":"2020","journal-title":"Ann. Transl. Med."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"118869","DOI":"10.1109\/ACCESS.2020.3005510","article-title":"Weakly supervised deep learning for covid-19 infection detection and classification from ct images","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1016\/j.cell.2020.04.045","article-title":"Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography","volume":"181","author":"Zhang","year":"2020","journal-title":"Cell"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TUFFC.2020.3005512","article-title":"Automatic Pleural Line Extraction and COVID-19 Scoring from Lung Ultrasound Data","volume":"67","author":"Carrer","year":"2020","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_180","unstructured":"Tang, Z., Zhao, W., Xie, X., Zhong, Z., Shi, F., Liu, J., and Shen, D. (2020). Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. arXiv."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Rajaraman, S., Siegelman, J., Alderson, P.O., Folio, L.S., Folio, L.R., and Antani, S.K. (2020). Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays. arXiv.","DOI":"10.1109\/ACCESS.2020.3003810"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"103869","DOI":"10.1016\/j.compbiomed.2020.103869","article-title":"CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization","volume":"122","author":"Mahmud","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"105608","DOI":"10.1016\/j.cmpb.2020.105608","article-title":"Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays","volume":"196","author":"Brunese","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_184","unstructured":"Born, J., Br\u00e4ndle, G., Cossio, M., Disdier, M., Goulet, J., Roulin, J., and Wiedemann, N. (2020). POCOVID-Net: Automatic detection of COVID-19 from a new lung ultrasound imaging dataset (POCUS). arXiv."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"5682","DOI":"10.1080\/07391102.2020.1788642","article-title":"Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning","volume":"39","author":"Jaiswal","year":"2020","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Al-Harrasi, A., Bhatia, S., Behl, T., Kaushik, D., Ahmed, M.M., and Anwer, K. (2022). Effect of COVID-19 on Different Organ Systems. Role of Essential Oils in the Management of COVID-19, CRC Press.","DOI":"10.1201\/9781003175933-8"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/nan.12667","article-title":"Invited Review: The spectrum of neuropathology in COVID-19","volume":"47","author":"Troakes","year":"2021","journal-title":"Neuropathol. Appl. Neurobiol."},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Saida, T., Mori, K., Hoshiai, S., Sakai, M., Urushibara, A., Ishiguro, T., Minami, M., Satoh, T., and Nakajima, T. (2022). Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments. Cancers, 14.","DOI":"10.3390\/cancers14040987"},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cmpb.2017.09.004","article-title":"Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm","volume":"152","author":"Maniruzzaman","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Suri, J.S., and Laxminarayan, S. (2003). Angiography and Plaque Imaging: Advanced Segmentation Techniques, CRC Press.","DOI":"10.1201\/9780203490907"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1002\/mp.14193","article-title":"Ensemble U-net-based method for fully automated detection and segmentation of renal masses on computed tomography images","volume":"47","author":"Fatemeh","year":"2020","journal-title":"Med. Phys."},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"De Bel, T., Hermsen, M., Smeets, B., Hilbrands, L., van der Laak, J., and Litjens, G. (2018, January 10\u201315). Automatic segmentation of histopathological slides of renal tissue using deep learning. Proceedings of the Medical Imaging 2018: Digital Pathology, Houston, TX, USA.","DOI":"10.1117\/12.2293717"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.ekir.2017.11.002","article-title":"Association of pathological fibrosis with renal survival using deep neural networks","volume":"3","author":"Kolachalama","year":"2018","journal-title":"Kidney Int. Rep."},{"key":"ref_194","first-page":"29","article-title":"Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning","volume":"2","author":"Kuo","year":"2019","journal-title":"NPDigit. Med."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Narayanan, R., Kurhanewicz, J., Shinohara, K., Crawford, E.D., Simoneau, A., and Suri, J.S. (July, January 28). MRI-ultrasound registration for targeted prostate biopsy. Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA.","DOI":"10.1109\/ISBI.2009.5193221"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s11517-005-0016-y","article-title":"Breast image registration techniques: A survey","volume":"44","author":"Guo","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.7863\/jum.2009.28.11.1561","article-title":"Image Registration Accuracy of a 3-Dimensional Transrectal Ultrasound\u2013Guided Prostate Biopsy System","volume":"28","author":"Guo","year":"2009","journal-title":"J. Ultrasound Med."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1080\/03772063.2019.1604176","article-title":"Review of set theoretic approaches to magnetic resonance brain image segmentation","volume":"68","author":"Namburu","year":"2022","journal-title":"IETE J. Res."},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Kathuria, C., and Molinari, F. (2010). Atherosclerosis Disease Management, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-7222-4"},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1093\/ajcn\/65.4.1000","article-title":"Influence of lifestyle modification on atherosclerotic progression determined by ultrasonographic change in the common carotid intima-media thickness","volume":"4","author":"Markus","year":"1997","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11883-016-0635-9","article-title":"A review on atherosclerotic biology, wall stiffness, physics of elasticity, and its ultrasound-based measurement","volume":"18","author":"Patel","year":"2016","journal-title":"Curr. Atheroscler. Rep."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"302","DOI":"10.5853\/jos.2017.02922","article-title":"Cerebral small vessel disease: A review focusing on pathophysiology, biomarkers, and machine learning strategies","volume":"20","author":"Dwivedi","year":"2018","journal-title":"J. Stroke"},{"key":"ref_203","unstructured":"Suri, J.S. (2011). Imaging Based Symptomatic Classification and Cardiovascular Stroke Risk Score Estimation. (Application 13\/053,971), U.S. Patent."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.cmpb.2013.07.012","article-title":"Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images","volume":"112","author":"Acharya","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.bspc.2013.08.008","article-title":"Application of higher order statistics for atrial arrhythmia classification","volume":"8","author":"Martis","year":"2013","journal-title":"Biomed. Signal Processing Control"},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_207","first-page":"40","article-title":"Deep convolutional neural networks: Structure, feature extraction and training","volume":"20","year":"2017","journal-title":"Inf. Technol. Manag. Sci."},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"103958","DOI":"10.1016\/j.compbiomed.2020.103958","article-title":"3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular\/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic\u2122 2.0","volume":"125","author":"Skandha","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1016\/j.cmpb.2012.05.008","article-title":"Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods","volume":"108","author":"Molinari","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.compbiomed.2018.05.014","article-title":"Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort","volume":"98","author":"Biswas","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1177\/1544316718806421","article-title":"Geometric total plaque area is an equally powerful phenotype compared with carotid intima-media thickness for stroke risk assessment: A deep learning approach","volume":"42","author":"Srivastava","year":"2018","journal-title":"J. Vasc. Ultrasound"},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"103847","DOI":"10.1016\/j.compbiomed.2020.103847","article-title":"Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular\/stroke risk assessment","volume":"123","author":"Biswas","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_213","doi-asserted-by":"crossref","unstructured":"Jain, P.K., Sharma, N., Saba, L., Paraskevas, K.I., Kalra, M.K., Johri, A., Laird, J.R., Nicolaides, A.N., and Suri, J.S. (2021). Unseen artificial intelligence\u2014Deep learning paradigm for segmentation of low atherosclerotic plaque in carotid ultrasound: A multicenter cardiovascular study. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122257"},{"key":"ref_214","first-page":"9","article-title":"Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: An asymptomatic Japanese cohort study","volume":"41","author":"Jain","year":"2021","journal-title":"Int. Angiol."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.compbiomed.2016.11.011","article-title":"Stroke risk stratification and its validation using ultrasonic echolucent carotid wall plaque morphology: A machine learning paradigm","volume":"80","author":"Araki","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/JBHI.2016.2631401","article-title":"A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound","volume":"21","author":"Lekadir","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"4892","DOI":"10.3934\/mbe.2022229","article-title":"Prediction of atherosclerosis using machine learning based on operations research","volume":"19","author":"Chen","year":"2022","journal-title":"Math. Biosci. Eng."},{"key":"ref_218","first-page":"1538","article-title":"Carotid plaque CTA analysis in symptomatic subjects with bilateral intraplaque hemorrhage: A preliminary analysis","volume":"40","author":"Saba","year":"2019","journal-title":"Am. J. Neuroradiol."},{"key":"ref_219","first-page":"52","article-title":"Transfer learning approach in deep neural networks for uterine fibroid detection","volume":"25","author":"Sundar","year":"2022","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.jacr.2017.12.028","article-title":"Machine learning in medical imaging","volume":"15","author":"Giger","year":"2018","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ejrad.2017.01.031","article-title":"Relationship between white matter hyperintensities volume and the circle of Willis configurations in patients with carotid artery pathology","volume":"89","author":"Saba","year":"2017","journal-title":"Eur. J. Radiol."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.21037\/atm-20-7676","article-title":"Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: A narrative review for stroke application","volume":"9","author":"Saba","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1007\/s10554-020-02124-9","article-title":"Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: A cardiovascular disease\/stroke risk assessment system","volume":"37","author":"Saba","year":"2021","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3052577","article-title":"A Multicenter Study on Carotid Ultrasound Plaque Tissue Characterization and Classification Using Six Deep Artificial Intelligence Models: A Stroke Application","volume":"70","author":"Saba","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Sadowski, J., Targonski, R., Cyganski, P., Nowek, P., Starek-Stelmaszczyk, M., Zajac, K., Juranek, J., Wojtkiewicz, J., and Rynkiewicz, A. (2022). Remodeling of Retinal Arterioles and Carotid Arteries in Heart Failure Development\u2014A Preliminary Study. J. Clin. Med., 11.","DOI":"10.3390\/jcm11133721"},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1049\/el.2020.2102","article-title":"Brain MRI-based Wilson disease tissue classification: An optimised deep transfer learning approach","volume":"56","author":"Saba","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"Micucci, M., and Iula, A. (2022). Recent Advances in Machine Learning Applied to Ultrasound Imaging. Electronics., 11.","DOI":"10.3390\/electronics11111800"},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"105273","DOI":"10.1016\/j.compbiomed.2022.105273","article-title":"An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review","volume":"143","author":"Das","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/RBME.2020.2990959","article-title":"The role of imaging in the detection and management of COVID-19: A review","volume":"14","author":"Dong","year":"2020","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"100965","DOI":"10.1016\/j.preteyeres.2021.100965","article-title":"Artificial intelligence in OCT angiography","volume":"85","author":"Hormel","year":"2021","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"4080","DOI":"10.1038\/s41467-020-17971-2","article-title":"Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets","volume":"11","author":"Harmon","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_232","first-page":"144","article-title":"Artificial Intelligence in COPD: New Venues to Study a Complex Disease","volume":"6","year":"2020","journal-title":"Barc. Respir. Netw. Rev."},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1056\/NEJMc2011400","article-title":"Multiorgan and renal tropism of SARS-CoV-2","volume":"383","author":"Puelles","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.jacc.2020.05.001","article-title":"Association of treatment dose anticoagulation with in-hospital survival among hospitalized patients with COVID-19","volume":"76","author":"Paranjpe","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_235","doi-asserted-by":"crossref","unstructured":"Arnold, F., Westermann, L., Rieg, S., Neumann-Haefelin, E., Biever, P.M., Walz, G., Kalbhenn, J., and Tanriver, Y. (2020). Comparison of different anticoagulation strategies for renal replacement therapy in critically ill patients with COVID-19: A cohort study. BMC Nephrol., 21.","DOI":"10.1186\/s12882-020-02150-8"},{"key":"ref_236","first-page":"e9540","article-title":"Effect of COVID-19 on the Organs","volume":"12","author":"Jain","year":"2020","journal-title":"Cureus"},{"key":"ref_237","doi-asserted-by":"crossref","unstructured":"Kwekha-Rashid, A.S., Abduljabbar, H.N., and Alhayani, B. (2021). Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Appl. Nanosci., 1\u201313.","DOI":"10.1007\/s13204-021-01868-7"},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s11548-021-02317-0","article-title":"Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs","volume":"16","author":"Saba","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol."},{"key":"ref_239","first-page":"249","article-title":"Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data","volume":"13","author":"Winfree","year":"2022","journal-title":"Front. Physiol."},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1002\/cyto.a.24274","article-title":"In situ classification of cell types in human kidney tissue using 3D nuclear staining","volume":"99","author":"Woloshuk","year":"2021","journal-title":"Cytom. Part A"},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"18295","DOI":"10.1038\/s41598-019-54244-5","article-title":"DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data","volume":"9","author":"Dunn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1148\/radiol.2021211483","article-title":"Calcium scoring at coronary CT angiography using deep learning","volume":"302","author":"Mu","year":"2022","journal-title":"Radiology"},{"key":"ref_243","first-page":"715","article-title":"Automated echocardiographic detection of severe coronary artery disease using artificial intelligence","volume":"15","author":"Upton","year":"2022","journal-title":"Cardiovasc. Imaging"},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1681\/ASN.2020050589","article-title":"Remdesivir in patients with acute or chronic kidney disease and COVID-19","volume":"31","author":"Adamsick","year":"2020","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"105949","DOI":"10.1016\/j.ijantimicag.2020.105949","article-title":"Hydroxychloroquine and azithromycin as a treatment of COVID-19: Results of an open-label non-randomized clinical trial","volume":"56","author":"Gautret","year":"2020","journal-title":"Int. J. Antimicrob. Agents"},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1016\/j.mayocp.2020.03.024","article-title":"Urgent guidance for navigating and circumventing the QTc-prolonging and torsadogenic potential of possible pharmacotherapies for coronavirus disease 19 (COVID-19)","volume":"95","author":"Giudicessi","year":"2020","journal-title":"Mayo Clin. Proc."},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"E167","DOI":"10.1148\/radiol.2020203511","article-title":"DeepCOVID-XR: An artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large US clinical data set","volume":"1","author":"Wehbe","year":"2021","journal-title":"Radiology"},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1186\/s13054-020-03179-9","article-title":"Prediction of the development of acute kidney injury following cardiac surgery by machine learning","volume":"24","author":"Tseng","year":"2020","journal-title":"Crit. Care"},{"key":"ref_249","unstructured":"Zhang, C., Schwartz, M., K\u00fcstner, T., Martirosian, P., and Seith, F. Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches. Proceedings of the R\u00f6Fo-Fortschritte auf dem Gebiet der R\u00f6ntgenstrahlen und der Bildgebenden Verfahren."},{"key":"ref_250","doi-asserted-by":"crossref","first-page":"6761364","DOI":"10.1155\/2021\/6761364","article-title":"Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image","volume":"2021","author":"Ying","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1681\/ASN.2020050597","article-title":"Deep learning\u2013based segmentation and quantification in experimental kidney histopathology","volume":"32","author":"Bouteldja","year":"2021","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_252","doi-asserted-by":"crossref","unstructured":"Kalisnik, J.M., Bauer, A., Vogt, F.A., Stickl, F.J., Zibert, J., Fittkau, M., Bertsch, T., Kounev, S., and Fischlein, T. (2022). Artificial intelligence-based early detection of acute kidney injury after cardiac surgery. Eur. J. Cardio-Thorac. Surg.","DOI":"10.1093\/ejcts\/ezac289"},{"key":"ref_253","first-page":"249","article-title":"Predicting coronary artery disease using different artificial neural network models","volume":"8","year":"2008","journal-title":"Anatol. J. Cardiol."},{"key":"ref_254","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.36660\/abc.20200302","article-title":"Validation of an Artificial Intelligence Algorithm for Diagnostic Prediction of Coronary Disease: Comparison with a Traditional Statistical Model","volume":"117","author":"Correia","year":"2021","journal-title":"Arq. Bras. Cardiol."},{"key":"ref_255","doi-asserted-by":"crossref","unstructured":"Cheng, C.-A., and Chiu, H.-W. (2017, January 11\u201315). An artificial neural network model for the evaluation of carotid artery stenting prognosis using a national-wide database. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea.","DOI":"10.1109\/EMBC.2017.8037381"},{"key":"ref_256","doi-asserted-by":"crossref","first-page":"104721","DOI":"10.1016\/j.compbiomed.2021.104721","article-title":"Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound","volume":"136","author":"Jain","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_257","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ejvs.2009.03.013","article-title":"Carotid plaque analysis: Comparison of dual-source computed tomography (CT) findings and histopathological correlation","volume":"38","author":"Das","year":"2009","journal-title":"Eur. J. Vasc. Endovasc. Surg."},{"key":"ref_258","doi-asserted-by":"crossref","first-page":"1764","DOI":"10.3348\/kjr.2021.0148","article-title":"Fully automatic coronary calcium score software empowered by artificial intelligence technology: Validation study using three CT cohorts","volume":"22","author":"Lee","year":"2021","journal-title":"Korean J. Radiol."},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s40520-021-01985-x","article-title":"Identifying peripheral arterial disease in the elderly patients using machine-learning algorithms","volume":"34","author":"Gao","year":"2022","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s00296-021-05062-4","article-title":"Cardiovascular disease detection using machine learning and carotid\/femoral arterial imaging frameworks in rheumatoid arthritis patients","volume":"42","author":"Konstantonis","year":"2022","journal-title":"Rheumatol. Int."},{"key":"ref_261","first-page":"135","article-title":"Heart disease as a risk factor for dementia","volume":"5","author":"Justin","year":"2013","journal-title":"Clin. Epidemiol."},{"key":"ref_262","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1056\/NEJM199412013312202","article-title":"Atherosclerotic disease of the aortic arch and the risk of ischemic stroke","volume":"331","author":"Amarenco","year":"1994","journal-title":"N. Engl. J. Med."},{"key":"ref_263","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/j.ultrasmedbio.2014.12.024","article-title":"Improved correlation between carotid and coronary atherosclerosis SYNTAX score using automated ultrasound carotid bulb plaque IMT measurement","volume":"41","author":"Ikeda","year":"2015","journal-title":"Ultrasound Med. Biol."},{"key":"ref_264","doi-asserted-by":"crossref","first-page":"S3-219","DOI":"10.1227\/01.NEU.0000239895.00373.E4","article-title":"Histopathology of carotid atherosclerotic disease","volume":"59","author":"Virmani","year":"2006","journal-title":"Neurosurg."},{"key":"ref_265","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1681\/ASN.V92231","article-title":"Tubulointerstitial disease in aging: Evidence for underlying peritubular capillary damage, a potential role for renal ischemia","volume":"9","author":"Thomas","year":"1998","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_266","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1165\/rcmb.2007-0441OC","article-title":"Induction of vascular remodeling in the lung by chronic house dust mite exposure","volume":"39","author":"Johnson","year":"2008","journal-title":"Am. J. Respir. Cell Mol. Biol."},{"key":"ref_267","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1097\/MCA.0000000000000934","article-title":"The relationship between coronary artery disease and clinical outcomes in COVID-19: A single-center retrospective analysis","volume":"32","author":"Peterson","year":"2020","journal-title":"Coron. Artery Dis."},{"key":"ref_268","doi-asserted-by":"crossref","unstructured":"Szarpak, L., Mierzejewska, M., Jurek, J., Kochanowska, A., Gasecka, A., Truszewski, Z., Pruc, M., Blek, N., Rafique, Z., and Filipiak, K. (2022). Effect of Coronary Artery Disease on COVID-19\u2014Prognosis and Risk Assessment: A Systematic Review and Meta-Analysis. Biol., 11.","DOI":"10.3390\/biology11020221"},{"key":"ref_269","doi-asserted-by":"crossref","first-page":"151","DOI":"10.2214\/AJR.11.6955","article-title":"Association between carotid artery plaque volume, composition, and ulceration: A retrospective assessment with MDCT","volume":"199","author":"Saba","year":"2012","journal-title":"Am. J. Roentgenol."},{"key":"ref_270","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1097\/MCA.0000000000000914","article-title":"Prognostic significance of cardiac injury in COVID-19 patients with and without coronary artery disease","volume":"32","author":"Barman","year":"2020","journal-title":"Coron. Artery Dis."},{"key":"ref_271","first-page":"8","article-title":"Progress and challenges in metabolic syndrome in children and adolescents: A scientific statement from the American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young; Council on Cardiovascular Nursing; and Council on Nutrition, Physical Activity, and Metabolism","volume":"4","author":"Steinberger","year":"2009","journal-title":"Circulation"},{"key":"ref_272","unstructured":"National Institutes of Health (2020). Coronavirus disease 2019 (COVID-19) treatment guidelines. Natl. Inst. Health."},{"key":"ref_273","doi-asserted-by":"crossref","first-page":"e219","DOI":"10.1097\/CCM.0000000000004899","article-title":"Surviving sepsis campaign guidelines on the management of adults with coronavirus disease 2019 (COVID-19) in the ICU: First update","volume":"49","author":"Alhazzani","year":"2021","journal-title":"Crit. Care Med."},{"key":"ref_274","first-page":"CD003186","article-title":"Antiplatelet agents and anticoagulants for hypertension","volume":"2011","author":"Lip","year":"2011","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_275","doi-asserted-by":"crossref","unstructured":"Paul, S., Maindarkar, M., Saxena, S., Saba, L., Turk, M., Kalra, M., Krishnan, P.R., and Suri, J.S. (2022). Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson\u2019s Disease: A Narrative Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12010166"},{"key":"ref_276","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2016.03.001","article-title":"Speckle reduction in medical ultrasound images using an unbiased non-local means method","volume":"28","author":"Sudeep","year":"2016","journal-title":"Biomed. Signal Processing Control"},{"key":"ref_277","doi-asserted-by":"crossref","first-page":"105204","DOI":"10.1016\/j.compbiomed.2021.105204","article-title":"Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review","volume":"142","author":"Suri","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_278","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Agarwal, S., Jena, B., Saxena, S., El-Baz, A., Agarwal, V., Kalra, M.K., Saba, L., Viskovic, K., and Fatemi, M. (2022). Five Strategies for Bias Estimation in Artificial Intelligence-based Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients using AP(ai)Bias 2.0: A Systematic Review. IEEE TIM, 1.","DOI":"10.1109\/TIM.2022.3174270"},{"key":"ref_279","doi-asserted-by":"crossref","unstructured":"El-Baz, A., and Suri, J.S. (2019). Big Data in Multimodal Medical Imaging, CRC Press.","DOI":"10.1201\/b22410"},{"key":"ref_280","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1016\/S0140-6736(96)07492-2","article-title":"Alternative projections of mortality and disability by cause 1990\u20132020: Global Burden of Disease Study","volume":"349","author":"Murray","year":"1997","journal-title":"Lancet Neurol."},{"key":"ref_281","doi-asserted-by":"crossref","first-page":"105571","DOI":"10.1016\/j.compbiomed.2022.105571","article-title":"Eight Pruning Deep Learning Models for Low Storage and High-Speed COVID-19 Computed Tomography Lung Segmentation and Heatmap-based Lesion Localization: A Multicenter study using COVLIAS 2.0","volume":"146","author":"Agarwal","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_282","first-page":"237","article-title":"Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification","volume":"35","author":"Acharya","year":"2014","journal-title":"Ultraschall Med. Eur. J. Ultrasound"},{"key":"ref_283","doi-asserted-by":"crossref","unstructured":"El-Baz, A., Gimel\u2019farb, G., and Suri, J.S. (2015). Stochastic Modeling for Medical Image Analysis, CRC Press.","DOI":"10.1201\/b19253"},{"key":"ref_284","doi-asserted-by":"crossref","unstructured":"Antonelli, A., Bennardo, F., Brancaccio, Y., Barone, S., Femiano, F., Nucci, L., Minervini, G., Fortunato, L., Attanasio, F., and Giudice, A. (2020). Can Bone Compaction Improve Primary Implant Stability? An In Vitro Comparative Study with Osseodensification Technique. Appl. Sci., 10.","DOI":"10.3390\/app10238623"},{"key":"ref_285","doi-asserted-by":"crossref","first-page":"108509","DOI":"10.1016\/j.clim.2020.108509","article-title":"Early predictors of clinical outcomes of COVID-19 outbreak in Milan, Italy","volume":"217","author":"Ciceri","year":"2020","journal-title":"Clin. Immunol."},{"key":"ref_286","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jpeds.2020.07.039","article-title":"Clinical manifestations and outcomes of critically ill children and adolescents with coronavirus disease 2019 in New York City","volume":"226","author":"Derespina","year":"2020","journal-title":"J. Pediatrics"},{"key":"ref_287","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/BF01907940","article-title":"The role of lipid peroxidation in pathogenesis of ischemic damage and the antioxidant protection of the heart. Cardiology","volume":"77","author":"Meerson","year":"1982","journal-title":"Basic Res. Cardiol."},{"key":"ref_288","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1200\/GO.20.00064","article-title":"Outcome of oncology patients infected with coronavirus","volume":"6","author":"Jazieh","year":"2020","journal-title":"JCO Glob. Oncol."},{"key":"ref_289","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.irbm.2020.05.003","article-title":"Deep transfer learning based classification model for COVID-19 disease","volume":"43","author":"Pathak","year":"2020","journal-title":"IRBM"},{"key":"ref_290","doi-asserted-by":"crossref","first-page":"87","DOI":"10.2214\/AJR.20.23034","article-title":"Coronavirus disease 2019 (COVID-19): A systematic review of imaging findings in 919 patients","volume":"215","author":"Salehi","year":"2020","journal-title":"Am. J. Roentgenol."},{"key":"ref_291","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s11604-021-01120-w","article-title":"Ground-glass opacity (GGO): A review of the differential diagnosis in the era of COVID-19","volume":"39","author":"Cozzi","year":"2021","journal-title":"Jpn. J. Radiol."},{"key":"ref_292","doi-asserted-by":"crossref","unstructured":"Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., and Liu, J. (2020). Chest CT forRadiology typical 2019-nCoV pneumonia: Relationship to negative RT-PCR testing. Radiology.","DOI":"10.1148\/radiol.2020200343"},{"key":"ref_293","unstructured":"Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P.D., Zhang, H., Ji, W., Bernheim, A., and Siegel, E. (2020). Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv."},{"key":"ref_294","doi-asserted-by":"crossref","first-page":"106912","DOI":"10.1016\/j.asoc.2020.106912","article-title":"CNN-based transfer learning\u2013BiLSTM network: A novel approach for COVID-19 infection detection","volume":"98","author":"Aslan","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_295","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1109\/TIP.2021.3058783","article-title":"JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation","volume":"30","author":"Wu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_296","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.1093\/eurheartj\/ehaa399","article-title":"ST-elevation myocardial infarction and pulmonary embolism in a patient with COVID-19 acute respiratory distress syndrome","volume":"41","author":"Ueki","year":"2020","journal-title":"Eur. Heart J."}],"container-title":["Journal of Cardiovascular Development and Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2308-3425\/9\/8\/268\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:09:01Z","timestamp":1760141341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2308-3425\/9\/8\/268"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,15]]},"references-count":296,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["jcdd9080268"],"URL":"https:\/\/doi.org\/10.3390\/jcdd9080268","relation":{},"ISSN":["2308-3425"],"issn-type":[{"value":"2308-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,15]]}}}