Infection-Mediated Clinical Biomarkers for a COVID-19 Electrical Biosensing Platform

Gold nanostructures modified carbon-based electrode enhanced with methylene blue for point-of-care COVID-19 tests using isothermal amplification

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) envelope (E) and RNA-dependent RNA polymerase (RdRP) genes were detected via electrochemical measurements using a screen-printed carbon electrode (SPCE) (3-electrode system) coupled with a battery-operated thin-film heater based on the loop-mediated isothermal amplification (LAMP) technique. The working electrodes of the SPCE sensor were decorated with synthesized gold nanostars (AuNSs) to obtain a large surface area and improve sensitivity. The LAMP assay was enhanced using a real-time amplification reaction system to detect the optimal target genes (E and RdRP) of SARS-CoV-2. The optimized LAMP assay was performed with diluted concentrations (from 0 to 10⁹ copies) of the target DNA using 30 μM of methylene blue as a redox indicator. Target DNA amplification was conducted for 30 min at a constant temperature using a thin-film heater, and the final amplicon electrical signals were detected based on cyclic voltammetry curves. Our electrochemical LAMP analysis of SARS-CoV-2 clinical samples showed an excellent correlation with the Ct value of real-time reverse transcriptase-polymerase chain reaction, indicating successful validation of results. A linear relationship between the peak current response and the amplified DNA was observed for both genes. The AuNS-decorated SPCE sensor with the optimized LAMP primer enabled accurate analysis of both SARS-CoV-2-positive and -negative clinical samples. Therefore, the developed device is suitable for use as a point-of-care test DNA-based sensor for the diagnosis of SARS-CoV-2.

Apoptosis and Phagocytosis as Antiviral Mechanisms

Viruses are infectious entities that make use of the replication machinery of their hosts to produce more progenies, causing disease and sometimes death. To counter viral infection, metazoan hosts are equipped with various defense mechanisms, from the rapid-evoking innate immune responses to the most advanced adaptive immune responses. Previous research demonstrated that cells in fruit flies and mice infected with Drosophila C virus and influenza, respectively, undergo apoptosis, which triggers the engulfment of apoptotic virus-infected cells by phagocytes. This process involves the recognition of eat-me signals on the surface of virus-infected cells by receptors of specialized phagocytes, such as macrophages and neutrophils in mice and hemocytes in fruit flies, to facilitate the phagocytic elimination of virus-infected cells. Inhibition of phagocytosis led to severe pathologies and death in both species, indicating that apoptosis-dependent phagocytosis of virus-infected cells is a conserved antiviral mechanism in multicellular organisms. Indeed, our understanding of the mechanisms underlying apoptosis-dependent phagocytosis of virus-infected cells has shed a new perspective on how hosts defend themselves against viral infection. This chapter explores the mechanisms of this process and its potential for developing new treatments for viral diseases.

Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.