Laboratory diagnosis of novel corona virus (2019-nCoV)-present and the future

Efficacy of Transfer Learning-based ResNet models in Chest X-ray image classification for detecting COVID-19 Pneumonia

Because of COVID-19's effect on pulmonary tissues, Chest X-ray(CXR) and Computed Tomography (CT) images have become the preferred imaging modality for detecting COVID-19 infections at the early diagnosis stages, particularly when the symptoms are not specific. A significant fraction of individuals with COVID-19 have negative polymerase chain reaction (PCR) test results; therefore, imaging studies coupled with epidemiological, clinical, and laboratory data assist in the decision making. With the newer variants of COVID-19 emerging, the burden on diagnostic laboratories has increased manifold. Therefore, it is important to employ beyond laboratory measures to solve complex CXR image classification problems. One such tool is Convolutional Neural Network (CNN), one of the most dominant Deep Learning (DL) architectures. DL entails training a CNN for a task such as classification using extensive datasets. However, the labelled data for COVID-19 is scarce, proving to be a prime impediment to applying DL-assisted analysis. The available datasets are either scarce or too diversified to learn effective feature representations; therefore Transfer Learning (TL) approach is utilized. TL-based ResNet architecture has a powerful representational ability, making it popular in Computer Vision. The aim of this study is two-fold- firstly, to assess the performance of ResNet models for classifying Pneumonia cases from CXR images and secondly, to build a customized ResNet model and evaluate its contribution to the performance improvement. The global accuracies achieved by the five models i.e., ResNet18_v1, ResNet34_v1, ResNet50_v1, ResNet101_v1, ResNet152_v1 are 91.35%, 90.87%, 92.63%, 92.95%, and 92.95% respectively. ResNet50_v1 displayed the highest sensitivity of 97.18%, ResNet101_v1 showed the specificity of 94.02%, and ResNet18_v1 had the highest precision of 93.53%. The findings are encouraging, demonstrating the effectiveness of ResNet in the automatic detection of Pneumonia for COVID-19 diagnosis. The customized ResNet model presented in this study achieved 95% global accuracy, 95.65% precision, 92.74% specificity, and 95.9% sensitivity, thereby allowing a reliable analysis of CXR images to facilitate the clinical decision-making process. All simulations were carried in PyTorch utilizing Quadro 4000 GPU with Intel(R) Xeon(R) CPU E5-1650 v4 @ 3.60 ​GHz processor and 63.9 ​GB useable RAM.

COVID-19: A systematic review and update on prevention, diagnosis, and treatment

Since the rapid onset of the COVID-19 or SARS-CoV-2 pandemic in the world in 2019, extensive studies have been conducted to unveil the behavior and emission pattern of the virus in order to determine the best ways to diagnosis of virus and thereof formulate effective drugs or vaccines to combat the disease. The emergence of novel diagnostic and therapeutic techniques considering the multiplicity of reports from one side and contradictions in assessments from the other side necessitates instantaneous updates on the progress of clinical investigations. There is also growing public anxiety from time to time mutation of COVID-19, as reflected in considerable mortality and transmission, respectively, from delta and Omicron variants. We comprehensively review and summarize different aspects of prevention, diagnosis, and treatment of COVID-19. First, biological characteristics of COVID-19 were explained from diagnosis standpoint. Thereafter, the preclinical animal models of COVID-19 were discussed to frame the symptoms and clinical effects of COVID-19 from patient to patient with treatment strategies and in-silico/computational biology. Finally, the opportunities and challenges of nanoscience/nanotechnology in identification, diagnosis, and treatment of COVID-19 were discussed. This review covers almost all SARS-CoV-2-related topics extensively to deepen the understanding of the latest achievements (last updated on January 11, 2022).

Nanobioengineering: A promising approach for early detection of COVID-19

Unique pneumonia due to an unknown source emerged in December 2019 in the city of Wuhan, China. Consequently, the World Health Organization (WHO) declared this condition as a new coronavirus disease-19 also known as COVID-19 on February 11, 2020, which on March 13, 2020 was declared as a pandemic. The virus that causes COVID-19 was found to have a similar genome (80% similarity) with the previously known acute respiratory syndrome also known as SARS-CoV. The novel virus was later named Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 falls in the family of Coronaviridae which is further divided into Nidovirales and another subfamily called Orthocoronavirinae. The four generations of the coronaviruses belongs to the Orthocoronavirinae family that consists of alpha, beta, gamma and delta coronavirus which are denoted as α-CoV, β-CoV, γ-CoV, δ-CoV respectively. The α-CoV and β-CoVs are mainly known to infect mammals whereas γ-CoV and δ-CoV are generally found in birds. The β-CoVs also comprise of SARS-CoV and also include another virus that was found in the Middle East called the Middle East respiratory syndrome virus (MERS-CoV) and the cause of current pandemic SARS-CoV-2. These viruses initially cause the development of pneumonia in the patients and further development of a severe case of acute respiratory distress syndrome (ARDS) and other related symptoms that can be fatal leading to death.