The present state of diagnostic and therapeutic developmental race for vaccines against the SARS CoV-2 (nCOVID-19) focuses on prevention and control of this global pandemic which also represents a critical challenge to the global health community. Although development of novel vaccines can prevent the SARS CoV-2 infections, it is still impeded by several other factors and therefore novel approaches towards treatment and management of this disease is the urgent need. Passive immunotherapy plays a vital role as a possible alternative to meet this challenge and among various antibody sources, chicken egg yolk antibodies (IgY) can be used as an alternative to mammalian antibodies which have been previously studied against SARS CoV outbreak in China. In this review, we discuss the strategies for the use of chicken egg yolk (IgY) antibodies in the development of rapid diagnosis and immunotherapy against SARS CoV-2. Also, IgY antibodies have previously been used against various respiratory bacterial and viral infections in humans and animals. Compared to mammalian antibodies (IgG), chicken egg yolk antibodies (IgY) have greater binding affinity to specific antigens, ease of extraction and lower production costs, hence possessing remarkable pathogen-neutralizing activity of pathogens in respiratory and lungs. We provide an overall importance for the use of monoclonal chicken egg yolk antibodies (IgY) using phage display method describing their potential passive immunotherapeutic application for the treatment and prevention of SARS CoV-2 infection which is simple, fast and safe way of approach for treating patients effectively.

Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income countries due to the rise of automation in healthcare sectors. The already limited resources are being heavily allocated towards controlling other dangerous diseases. Modern digital radiography (DR) machines, used for screening chest X-rays of potential TB victims are very practical. Coupled with computer-aided detection (CAD) with the aid of artificial intelligence, radiologists working in this field can really help potential patients. In this study, progressive resizing is introduced for training models to perform automatic inference of TB using chest X-ray images. ImageNet fine-tuned Normalization-Free Networks (NFNets) are trained for classification and the Score-Cam algorithm is utilized to highlight the regions in the chest X-Rays for detailed inference on the diagnosis. The proposed method is engineered to provide accurate diagnostics for both binary and multiclass classification. The models trained with this method have achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity on a multiclass classification dataset. Moreover, models have also achieved top-1 inference metrics of 96% accuracy and 98% AUC for binary classification. The results obtained demonstrate that the proposed method can be used as a secondary decision tool in a clinical setting for assisting radiologists.

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