Comprehending the morphological disparities between SARS-CoV-2 and SARS-CoV viruses can shed light on the underlying mechanisms of infection and facilitate the development of effective diagnostic tools and treatments. Hence, this study aimed to conduct a comprehensive analysis and comparative assessment of the morphology of SARS-CoV-2 and SARS-CoV using transmission electron microscopy (TEM) images. The dataset encompassed 519 isolated SARS-CoV-2 images obtained from patients in Italy (INMI) and 248 isolated SARS-CoV images from patients in Germany (Frankfurt). In this paper, we employed TEM images to scrutinize morphological features, and the outcomes were contrasted with those of SARS-CoV viruses. The findings reveal disparities in the characteristics of SARS-CoV-2 and SARS-CoV, such as envelope protein (E) 98.6 and 102.2 nm, length of spike protein (S) 10.11 and 9.50 nm, roundness 0.86 and 0.88, circularity 0.78 and 0.76, and area sizes 25145.54 and 38591.35 pixels, respectively. In conclusion, these results will augment the identification of virus subtypes, aid in the study of antiviral medications, and enhance our understanding of disease progression and the virus life cycle. Moreover, these findings have the potential to assist in the development of more accurate epidemiological prediction models for COVID-19, leading to better outbreak management and saving lives.
The worldwide outbreak of COVID-19 disease was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2). The existence of spike proteins, which allow these viruses to infect host cells, is one of the distinctive biological traits of various prior viruses. As a result, the process by which these viruses infect people is largely dependent on spike proteins. The density of SARS-CoV-2 spike proteins must be estimated to better understand and develop diagnostics and vaccines against the COVID-19 pandemic. CT scans and X-rays have three issues: frosted glass, consolidation, and strange roadway layouts. Each of these issues can be graded separately or together. Although CT scan is sensitive to COVID-19, it is not very specific. Therefore, patients who obtain these results should have more comprehensive clinical and laboratory tests to rule out other probable reasons. This work collected 586 SARS-CoV 2 transmission electron microscopy (TEM) images from open source for density estimation of virus spike proteins through a segmentation approach based on the superpixel technique. As a result, the spike density means of SARS-CoV2 and SARS-CoV were 21,97 nm and 22,45 nm, respectively. Furthermore, in the future, we aim to include this model in an intelligent system to enhance the accuracy of viral detection and classification. Moreover, we can remotely connect hospitals and public sites to conduct environmental hazard assessments and data collection.
Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.