A comprehensive review and clinical guide to molecular and serological diagnostic tests and future development: In vitro diagnostic testing for COVID-19

COVID-19 is a contagious syndrome caused by SARS Coronavirus 2 (SARS-CoV-2) that requires rapid diagnostic testing to identify and manage in the affected persons, characterize epidemiology, and promptly make public health decisions and manage the virus present in the affected person and promptly make public health decisions by characterizing the epidemiology. Technical problems, especially contamination occurring during manual real-time polymerase chain reaction (RT-PCR), can result in false-positive NAAT results. In some cases, RNA detection technology and antigen testing are alternatives to RT-PCR. Sequencing is vital for tracking the SARS-CoV-2 genome's evolution, while antibody testing is beneficial for epidemiology. SARS-CoV-2 testing can be made safer, faster, and easier without losing accuracy. Continued technological advancements, including smartphone integration, will help in the current epidemic and prepare for the next. Nanotechnology-enabled progress in the health sector has aided disease and pandemic management at an early stage. These nanotechnology-based analytical tools can be used to quickly diagnose COVID-19. The SPOT system is used to diagnose the coronavirus quickly, sensibly, accurately, and with portability. The SPOT assay consists of RT-LAMP, followed by pfAgo-based target sequence detection. In addition, SPOT system was used to detect both positive and negative SARS-CoV-2 samples. This combination of speed, precision, sensitivity, and mobility will allow for cost-effective and high-volume COVID-19 testing. © 2023 the author(s), published by De Gruyter.

An Interdisciplinary Design Project for Undergraduate Engineering Training - Portable Real-Time Polymerase Chain Reaction System

Our world has been permanently changed by the pandemic outbreak of COVID-19 starts around the end of 2019. In the first few months of 2020, the whole world was in urgent need of an effective, easy, and quick method for the identification of the infection of the new virus. Polymerase Chain Reaction (PCR) machine, which can test DNA samples by rapidly making millions of copies of a specific DNA sample through the PCR process, including the COVID-19 virus, can perfectly fit this demand. In this study, a design project on PCR is introduced for undergraduate education in electrical and mechanical engineering. The objective of this project is to develop a low-cost, ease-of-use, wallet-size, portable real-time PCR (RT-PCR) machine for accurate testing of various bacteria or viruses. The key function of the PT-PCR system is to precisely control and maintain the temperature of the bio-sample solution within a range between 55℃ and 95℃. The RT-PCR system is centrally controlled by a microcontroller Raspberry Pi 3. It receives temperature measurements from thermistors and operates the heating lid, the thermoelectric module, and the cooling fan to regulate the temperatures required in repetitive thermal cycles. This project provides students opportunities in studying and practicing a wide range of engineering technics and skills, including mechanical design, electronics design, microcomputer programming, system control, power electronics, sensors and actuators, data acquisition and processing, cellphone app development. Students can gain comprehensive understanding of the design of multiphysics system after they overcome various challenges emerging in the project. From the view of engineering education, the process of this project development has demonstrated the importance and benefits of adopting complex interdisciplinary engineering problems for student teams to solve, especially those involve contemporary issues. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.

POSTER: Diagnosis of COVID-19 through Transfer Learning Techniques on CT Scans: A Comparison of Deep Learning Models

The novel coronavirus disease (COVID-19) constitutes a public health emergency globally. It is a deadly disease which has infected more than 230 million people worldwide. Therefore, early and unswerving detection of COVID-19 is necessary. Evidence of this virus is most commonly being tested by RT-PCR test. This test is not 100% reliable as it is known to give false positives and false negatives. Other methods like X-Ray images or CT scans show the detailed imaging of lungs and have been proven more reliable. This paper compares different deep learning models used to detect COVID-19 through transfer learning technique on CT scan dataset. VGG-16 outperforms all the other models achieving an accuracy of 85.33 % on the dataset. © 2022 IEEE.

Clinical validation of SERS metasurface SARS-CoV-2 biosensor

The real-Time polymerase chain reaction (RT-PCR) analysis using nasal swab samples is the gold standard approach for COVID-19 diagnosis. However, due to the high false-negative rate at lower viral loads and complex test procedure, PCR is not suitable for fast mass screening. Therefore, the need for a highly sensitive and rapid detection system based on easily collected fluids such as saliva during the pandemic has emerged. In this study, we present a surface-enhanced Raman spectroscopy (SERS) metasurface optimized with genetic algorithm (GA) to detect SARS-CoV-2 directly using unprocessed saliva samples. During the GA optimization, the electromagnetic field profiles were used to calculate the field enhancement of each structure and the fitness values to determine the performance of the generated substrates. The obtained design was fabricated using electron beam lithography, and the simulation results were compared with the test results using methylene blue fluorescence dye. After the performance of the system was validated, the SERS substrate was tested with inactivated SARS-CoV-2 virus for virus detection, viral load analysis, cross-reactivity, and variant detection using machine learning models. After the inactivated virus tests are completed, with 36 PCR positive and 33 negative clinical samples, we were able to detect the SARS-CoV-2 positive samples from Raman spectra with 95.2% sensitivity and specificity. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Detection of COVID-19 and Classification of Pneumonia Using Deep Neural Networks Algorithms

COVID-19 which is a subclass of severe acute respiratory syndrome (SARS) is a viral disease which emerged from China in 2019. At first, there are shorthand of test kits available to diagnose the COVID-19 disease. The tests available to diagnose the COVID-19 are RT-PCR (real-time polymerase chain reaction), Rapid Antigen test and Antibody test. But in these, only RT-PCR has the high accuracy, and it is a time-taking process. It takes nearly from 4 to 48 h. Here, AI plays an important role in diagnosing the disease. In the recent years, AI becomes a part of medical field and is widely used in classification. The chest X-Rays are used to detect the COVID-19 using deep learning and the model used to detect the COVID-19 is ResNet18 which is a residual network containing 18 layers. In this work, we classified four types of classes to make sure that our model performance is better and classify accurately. The data set contain a total of 5365 images. In this, we used 80% of data for the training and 20% for validation. The accuracy obtained in classification of three classes is 96.67% and for four classes, the accuracy is 91%. We have also used another model for comparison which ResNet50 and achieved an accuracy of 75%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

mRNA expression of the severe acute respiratory syndrome-coronavirus 2 angiotensin-converting enzyme 2 receptor in the lung tissue of Wistar rats according to age.

Background and Aim: Angiotensin-converting enzyme 2 (ACE2) is expressed and plays functional and physiological roles in different tissues of the body. This study aimed to distinguish the levels of expression of ACE2 in the lung tissue at different ages of rats. Materials and Methods: In this study, 18 male rats were used and divided into three groups according to age. Real-time quantitative polymerase chain reaction (RT-qPCR) was conducted to determine the levels of the quantification of eosinophil cationic protein mRNA transcript. In addition, tissue specimens of the lung were stained with routine hematoxylin and eosin stains. Results: This study confirmed that RT-qPCR amplification plots of ACE2 gene exhibited clearly expression of the lung tissue of rats in the different groups and there are strong different threshold cycles numbers according to the age at 2 weeks, 2 months, and 6-8 months. Consequently, the expression of ACE2 was completely different between groups depending on the age of the rats. The RT-qPCR results showed that the older animal group (age of 6-8 months) had a significantly higher expression of ACE2 than the other animal groups (ages of 2 weeks and 2 months). In the same way, the second group (age of 2 months) had a significantly higher expression of ACE2 than the first group (age of 2 weeks). This study confirmed that the ACE2 expression is influenced by the age of rats. Conclusion: This study concluded that the expression of the ACE2 receptor of coronavirus disease 2019 would be different according to the age of rats, and this result suggested that expression of ACE2 in lung tissue could determine infection and pathogenesis of COVID-19 during different ages of rats or some individual differences.

Covid-19 Detection Based on the Fine-Tuned MobileNetv2 Through Lung X-rays

Covid-19 is has become an epidemic, which is affecting millions of people around the world. The common symptoms of Covid-19 are cough and fever, which are very similar to the normal Flu. Covid-19 spreads fast and is devastating for people of all ages especially elderly and people having weak immune system. The standard technique used for Covid-19 detection is real-time polymerase chain reaction (RT-PCR) test. However, RT-PCR is unreliable for Covid-19 detection as it takes long time to detect the disease and it produces considerable number of false positive cases. Therefore, we need to propose an automated and reliable method for Covid-19 detection. Radiographic images are widely used for the detection of various pulmonary diseases such as lung cancer, asthma, pneumonia, etc. We also used chest x-rays for the diagnosis of Covid-19. In this paper, we employed two deep learning models such as SqueezeNet and MobileNetv2 and fine-tuned to check the classification performance. Moreover, we performed data augmentation technique to increase the amount of data and avoid the overfitting of model. We evaluated the performance of the proposed system on standard dataset Covid-19 Radiography dataset that is publicly available. More specifically, we achieved remarkable accuracy of 97%, precision of 95.19%, recall of 100%, specificity of 95%, area under the curve of 98.93%, and F1-score of 97.06% on MobileNetv2. Experimental results and comparative analysis with other existing methods demonstrate that our method is reliable than PT-PCR and other existing state-of-the-art methods for Covid-19 detection. © 2021 IEEE.