Abstract The World Health Organization has reported approximately 430 million confirmed cases of coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), worldwide, including nearly 6 million deaths, since its initial appearance in China in 2019. While the number of diagnosed cases continues to increase, the need for technologies that can accurately and rapidly detect SARS‐CoV‐2 virus infection at early phases continues to grow, and the Federal Drug Administration (FDA) has licensed emergency use authorizations (EUAs) for virtually hundreds of diagnostic tests based on nucleic acid molecules and antigen–antibody serology assays. Among them, the quantitative real‐time reverse transcription PCR (qRT‐PCR) assay is considered the gold standard for early phase virus detection. Unfortunately, qRT‐PCR still suffers from disadvantages such as the complex test process and the occurrence of false negatives; therefore, new nucleic acid detection devices and serological testing technologies are being developed. However, because of the emergence of strongly infectious mutants of the new coronavirus, such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529), the need for the specific detection of mutant strains is also increasing. Therefore, this article reviews nucleic acid‐ and antigen–antibody‐based serological assays, and compares the performance of some of the most recent FDA‐approved and literature‐reported assays and associated kits for the specific testing of new coronavirus variants.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9349698

An updated review of SARS‐CoV‐2 detection methods in the context of a novel coronavirus pandemic

Accurate identification at an early stage of infection is critical for effective care of any infectious disease. The “coronavirus disease 2019 (COVID-19)” outbreak, caused by the virus “Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)”, corresponds to the current and global pandemic, characterized by several developing variants, many of which are classified as variants of concern (VOCs) by the “World Health Organization (WHO, Geneva, Switzerland)”. The primary diagnosis of infection is made using either the molecular technique of RT-PCR, which detects parts of the viral genome’s RNA, or immunodiagnostic procedures, which identify viral proteins or antibodies generated by the host. As the demand for the RT-PCR test grew fast, several inexperienced producers joined the market with innovative kits, and an increasing number of laboratories joined the diagnostic field, rendering the test results increasingly prone to mistakes. It is difficult to determine how the outcomes of one unnoticed result could influence decisions about patient quarantine and social isolation, particularly when the patients themselves are health care providers. The development of point-of-care testing helps in the rapid in-field diagnosis of the disease, and such testing can also be used as a bedside monitor for mapping the progression of the disease in critical patients. In this review, we have provided the readers with available molecular diagnostic techniques and their pitfalls in detecting emerging VOCs of SARS-CoV-2, and lastly, we have discussed AI-ML- and nanotechnology-based smart diagnostic techniques for SARS-CoV-2 detection.

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Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants

The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted in the private indoor space. Multiple sensors, with their inputs, are analyzed through the experiments. The experiment environment, installed with microgrids and Internet of Things (IoT) devices, has provided correlating data of various sensors from that special care context during the pandemic. The data is applied to classify user activities and develop a real-time learning and monitoring system to predict the IoT data. The microgrids were operated with the real-time learning system developed by comprehensive experiments on classification learning, regression learning, Error-Correcting Output Codes (ECOC), and deep learning models. With the help of machine learning experiments, data optimization, and the multilayered-tandem organization of the developed neural networks, the efficiency of this real-time monitoring system increases in learning the activity of users and predicting their actions, which are reported as feedback on the monitoring interfaces. The developed learning system predicts the real-time IoT data, accurately, in less than 5 milliseconds and generates big data that can be deployed for different usages in larger-scale facilities, networks, and e-health services.

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Real-Time Learning and Monitoring System in Fighting against SARS-CoV-2 in a Private Indoor Environment

The coronavirus disease 2019 (COVID-19) crisis has driven the development of innovative molecular diagnosis methods, including CRISPR-Cas technology. In this work, we performed a protocol, working with RNA extraction kit-free samples and using RT-LAMP-CRISPR-Cas13a technology; our results place this method at the forefront of rapid and specific diagnostic methods for COVID-19 due to the high specificity (100%), sensitivity (83%), PPVs (100%), and NPVs (81% for high viral loads) obtained with clinical samples. ABSTRACT At the end of 2019, a new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused a pandemic that persists to date and has resulted in more than 6.2 million deaths. In the last couple of years, researchers have made great efforts to develop a diagnostic technique that maintains high levels of sensitivity and specificity, since an accurate and early diagnosis is required to minimize the prevalence of SARS-CoV-2 infection. In this context, CRISPR-Cas systems are proposed as promising tools for development as diagnostic techniques due to their high specificity, highlighting that Cas13 endonuclease discriminates single nucleotide changes and displays collateral activity against single-stranded RNA molecules. With the aim of improving the sensitivity of diagnosis, this technology is usually combined with isothermal preamplification reactions (SHERLOCK, DETECTR). Based on this, we developed a reverse transcription-loop-mediated isothermal amplification (RT-LAMP)-CRISPR-Cas13a method for SARS-CoV-2 virus detection in nasopharyngeal samples without using RNA extraction that exhibits 100% specificity and 83% sensitivity, as well as a positive predictive value (PPV) of 100% and negative predictive values (NPVs) of 100%, 81%, 79.1%, and 66.7% for cycle threshold (CT) values of <20, 20 to 30, >30 and overall, respectively. IMPORTANCE The coronavirus disease 2019 (COVID-19) crisis has driven the development of innovative molecular diagnosis methods, including CRISPR-Cas technology. In this work, we performed a protocol, working with RNA extraction kit-free samples and using RT-LAMP-CRISPR-Cas13a technology; our results place this method at the forefront of rapid and specific diagnostic methods for COVID-19 due to the high specificity (100%), sensitivity (83%), PPVs (100%), and NPVs (81% for high viral loads) obtained with clinical samples.

Reverse Transcription-Loop-Mediated Isothermal Amplification-CRISPR-Cas13a Technology as a Promising Diagnostic Tool for SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has brought a huge threat to public health and the global economy. Rapid identification and isolation of SARS-CoV-2-infected individuals are regarded as one of the most effective measures to control the pandemic. Because of its high sensitivity and specificity, nucleic acid testing has become the major method of SARS-CoV-2 detection. A deep understanding of different diagnosis methods for COVID-19 could help researchers make an optimal choice in detecting COVID-19 at different symptom stages. In this review, we summarize and evaluate the latest developments in current nucleic acid detection methods for SARS-CoV-2. In particular, we discuss biosensors and CRISPR-based diagnostic systems and their characteristics and challenges. Furthermore, the emerging COVID-19 variants and their impact on SARS-CoV-2 diagnosis are systematically introduced and discussed. Considering the disease dynamics, we also recommend optional diagnostic tests for different symptom stages. From sample preparation to results readout, we conclude by pointing out the pain points and future directions of COVID-19 detection.

/pdf/nucleic-acid-testing-of-sars-cov-2-a-review-of-current-3d1pbzz3.pdf

Nucleic acid testing of SARS-CoV-2: A review of current methods, challenges, and prospects

Real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most sensitive and specific assay and, therefore, is the "gold standard" diagnostic method for the diagnosis of SARS-CoV-2 infection. The aim of this study was to compare and analyze the detection performance of three different commercially available SARS-CoV-2 nucleic acid detection kits: Sansure Biotech, GeneFinderTM, and TaqPathTM on 354 randomly selected samples from hospitalized COVID-19 patients. All PCR reactions were performed using the same RNA isolates and one real-time PCR machine. The final result of the three evaluated kits was not statistically different (p = 0.107), and also had a strong positive association and high Cohen's κ coefficient. In contrast, the average Ct values that refer to the ORF1ab and N gene amplification were significantly different (p < 0.001 and p < 0.001, respectively), with the lowest obtained by the TaqPathTM for the ORF1ab and by the Sansure Biotech for the N gene. The results show a high similarity in the analytical sensitivities for SARS-CoV-2 detection, which indicates that the diagnostic accuracy of the three assays is comparable. However, the SanSure Biotech kit showed a bit better diagnostic performance. Our findings suggest that the imperative for improvement should address the determination of cut-off Ct values and rapid modification of the primer sets along with the appearance of new variants.

https://www.mdpi.com/1999-4915/13/7/1321/pdf

Gordana Petrovic

Papers

Comparison and Sensitivity Evaluation of Three Different Commercial Real-Time Quantitative PCR Kits for SARS-CoV-2 Detection.