CRISPR-based biosensor for the detection of Marburg and Ebola virus

Marburg Virus is one of the most neglected diseases that dated back to 1967. Prior to the 21st century, the outbreak of the virus was recorded trice, In 1967, 1998 and 2004. Accurate and early detection of pathogenic viruses such as Marburg, Ebola, Zika, Human Immunodeficiency Virus (HIV), Dengue, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV-2) etc. is crucial for treatment and prevention. In the Last 2 decades, scientists relied on molecular diagnostic assays which include antigen-antibody and nucleic acid-based testing approaches for the detection of pathogenic viruses, bacteria, fungi and pathogenic surveillances. Despite the widely used of these assays, they are hindered by several factors which includes high cost, the use of expensive tools, long turnaround time, the use of chemicals and the need for trained personnel. In order to counter some of these challenges, scientists developed Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-based biosensor characterized by high specificity. Integration of CRISPR-based biosensor with nanomaterials have shown to increase the performance of the biosensors. Thus, this review provides extensive knowledge on the pathogenicity of Zika and Marburg viruses, conventional diagnosis and the prospect of CRISPR-Cas toolbox in conducting accurate diagnosis of these viruses. The review cover 6 sections which include introduction as section 1, overview of the pathogenicity of Ebola and Marburg viruses in terms of pathology, transmission, number of cases are discussed in section 2, conventional approaches are covered in section 3, CRISPR/Cas systems in both prokaryotic and eukaryotic are overviewed in section 4, the link between CRISPR/Cas system and detection of Ebola and Marburg viruses are presented in section 5, open research and issues are highlighted in section 6 while section 7 covers the concluding remarks

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen–antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19