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A detailed review of contrast-enhanced fluorescence magnetic resonance imaging techniques for earlier prediction and easy detection of COVID-19
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization ; : 1-13, 2022.
Article in English | Web of Science | ID: covidwho-2123050
ABSTRACT
Omicron, a novel coronavirus disease (COVID-19) variation, just seems to contain hundreds of modifications, with no transition sequences found in their ribonucleic acid (RNA). Omicron has a mutation termed E484A, as well as polymorphisms in the sites where it can attack two kinds of antibodies at one time. Since the nucleotide was highly altered, the well-known gold standard PCR test failed to identify these variants in post-covid patients. Computer tomography (CT) and Lung-X-Ray tests are a little more effective than the real-time reverse transcription-polymerase chain reaction (real-time RT - PCR) test in the diagnosis of coronavirus transmission (usually after 2 weeks). So, it is a need of the hour to define a test that could identify the symptoms, and extend the impact of COVID-19 in the pre-and post-COVID-19 cases. The in vivo MRI and the advanced fluorescent magnetic resonance imaging (F-MRI) diagnostic test for the lungs were recently revamped to identify the essential symbols of viral pneumonia, and pathological alterations in lung nodules, and made it easy to identify the seriousness of respiratory problems. The present work aims to review F-MRI techniques for earlier prediction and easy detection of COVID-19.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Year: 2022 Document Type: Article