The Virological, Immunological, and Imaging Approaches for COVID-19 Diagnosis and Research

A multi-organ map of the human immune system across age, sex and ethnicity

Understanding tissue biology's heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

Multiplex Detection of RNA Viruses Based on Ligation Reaction and Universal PCR Amplification

To detect several RNA viruses simultaneously, a method based on multiplex ligation reaction combined with multiplex qPCR or multiplex PCR+capillary electrophoresis was established to detect four RNA viruses: human immunodeficiency virus (HIV), hepatitis C (HCV), influenza A virus (IAV) H1N1 and H5N1. The experimental conditions including ligation probe concentration, annealing procedure, ligation temperature and ligase dosage were optimized intensively. We found that the specificity of the ligation reaction was affected by the probe concentration predominantly, high-probe concentration (100 nM) resulted in splint-independent ligation with efficiency comparable to that with RNA splint. The sensitivity of the ligation reaction was affected by the annealing mode apparently as the sensitivity of the step-down annealing mode was 100 times higher than that of the isothermal annealing at 37 °C. Under the optimized condition, this assay could detect virus RNA as low as 16 viral copies per reaction in doubleplex and triplex real-time quantitative PCR detection with satisfactory specificity and precision. By multiplex PCR+capillary electrophoresis, four RNA viruses could be detected in one tube with the sensitivity of 10 copies per reaction.

COVID-19: detection methods in post-mortem samples

COVID-19 identification is routinely performed on fresh samples, such as nasopharyngeal and oropharyngeal swabs, even if, the detection of the virus in formalin-fixed paraffin-embedded (FFPE) autopsy tissues could help to underlie mechanisms of the pathogenesis that are not well understood.

The gold standard for COVID-19 detection in FFPE samples remains the qRT-PCR as in swab samples, contextually other methods have been developed, including immunohistochemistry (IHC), and in situ hybridization (ISH). In this manuscript, we summarize the main data regarding the methods of COVID-19 detection in pulmonary and extra-pulmonary post-mortem samples, and especially the sensitivity and specificity of these assays will be discussed.

Immunological Studies to Understand Hybrid/Recombinant Variants of SARS-CoV-2

The zoonotic SARS-CoV-2 virus was present before the onset of the pandemic. It undergoes evolution, adaptation, and selection to develop variants that gain high transmission rates and virulence, resulting in the pandemic. Structurally, the spike protein of the virus is required for binding to ACE2 receptors of the host cells. The gene coding for the spike is known to have a high propensity of mutations, as a result generating numerous variants. The variants can be generated by random point mutations or recombination during replication. However, SARS-CoV-2 can also produce hybrid variants on co-infection of the host by two distinct lineages of the virus. The genomic sequences of the two variants undergo recombination to produce the hybrid variants. Additionally, these sub-variants also contain numerous mutations from both the parent variants, as well as some novel mutations unique to the hybrids. The hybrid variants (XD, XE, and XF) can be identified through numerous techniques, such as peak PCR, NAAT, and hybrid capture SARS-CoV-2 NGS (next generation sequencing) assay, etc., but the most accurate approach is genome sequencing. There are numerous immunological diagnostic assays, such as ELISA, chemiluminescence immunoassay, flow-cytometry-based approaches, electrochemiluminescence immunoassays, neutralization assays, etc., that are also designed and developed to provide an understanding of the hybrid variants, their pathogenesis, and other reactions. The objective of our study is to comprehensively analyze the variants of SARS-CoV-2, especially the hybrid variants. We have also discussed the techniques available for the identification of hybrids, as well as the immunological assays and studies for analyzing the hybrid variants.

Comparison of the SARS-CoV-2 BD Veritor Nasal Antigen Test with Nasopharyngeal Reverse Transcription-PCR in Symptomatic Patients

This study evaluated the BD Veritor system for rapid detection of SARS-CoV-2, an immunochromatographic point-of-care test, by comparing it with a standard reverse transcription-PCR (RT-PCR) methodology using samples from symptomatic patients. Samples from 146 symptomatic and 2 asymptomatic patients between the 1st and the 40th day of infection were evaluated. The nasopharyngeal and/or oropharyngeal swabs were inserted in a tube containing 0.9% saline solution and stored at refrigerator temperature until the moment of use. The samples were first tested with the Xpert Xpress SARS-CoV-2 (GeneXpert) kit (RT-PCR method), and the cycle thresholds (C_Ts) for the E and N2 genes encoding the SARS-CoV-2 envelope and nucleoprotein, respectively, were established. Subsequently, the same samples were tested using the Veritor rapid test. We analyzed the C_Ts of the N2 gene, which is detected in both methodologies, and observed sensitivities of 100%, 98.8%, 89.6%, and 82.7% for the C_Ts of <25, <27, and <30 and all the C_Ts, respectively. The greatest sensitivity was observed when we performed the test on patients within 5 days of symptom onset. The BD Veritor system’s workflow is simple and fast, taking approximately 16 min from sample preparation to obtaining the test result. In addition to its satisfactory sensitivity, with results that correlate with those of the RT-PCR, the BD Veritor analyzer instrument reduces the subjectivity of unaided visual readings and consequent potential variation in result interpretation. Therefore, our results showed that the BD Veritor diagnostic test can provide a rapid and accurate diagnosis for SARS-CoV-2.

IMPORTANCE This study provides important and useful information, especially for diagnostic laboratories, since the results show that the BD Veritor system can provide a fast and safe point-of-care antigen diagnostic test for rapid detection of COVID-19 that has high sensitivity, reproducibility, and accuracy.

COVID-19 Diagnostics: A Panoramic View on Its Present Scenario, Challenges and Solutions

The initial strategy to curb the surge of novel coronavirus disease, COVID-19, is prevention and quarantine, which are dependent on early diagnosis. The latest commercial diagnostic methods include AI/ML-based imaging methods and laboratory diagnosis, which differ in their efficiency. The former requires lung imaging and is useful for last stage patients. It was ensured to overcome the limitation of availability of laboratory-based kits, while the latter involves the collection of the suitable sample from an individual (blood sample, nasal or oral swab). Laboratory methods include methods like RT-PCR which is contemporarily contemplated as the benchmark for its quick and efficient SARS-CoV-2 infection detection. Other diagnosis alternatives include Serum Viral Neutralization (SVN) assays involving antigen-antibody reaction with much lower efficiency contrasted to RT-PCR. Apart from these methods, early detection has been key to the treatment of COVID-19, but the lack of sensitive assays to detect low viral titers acts as an impediment. This review presents an overview of detecting COVID-19 with the aid of several diagnostic techniques along with their benefits and limitations.

Microfluidic Point-of-Care (POC) Devices in Early Diagnosis: A Review of Opportunities and Challenges

The early diagnosis of infectious diseases is critical because it can greatly increase recovery rates and prevent the spread of diseases such as COVID-19; however, in many areas with insufficient medical facilities, the timely detection of diseases is challenging. Conventional medical testing methods require specialized laboratory equipment and well-trained operators, limiting the applicability of these tests. Microfluidic point-of-care (POC) equipment can rapidly detect diseases at low cost. This technology could be used to detect diseases in underdeveloped areas to reduce the effects of disease and improve quality of life in these areas. This review details microfluidic POC equipment and its applications. First, the concept of microfluidic POC devices is discussed. We then describe applications of microfluidic POC devices for infectious diseases, cardiovascular diseases, tumors (cancer), and chronic diseases, and discuss the future incorporation of microfluidic POC devices into applications such as wearable devices and telemedicine. Finally, the review concludes by analyzing the present state of the microfluidic field, and suggestions are made. This review is intended to call attention to the status of disease treatment in underdeveloped areas and to encourage the researchers of microfluidics to develop standards for these devices.

A Review on the Development of Gold and Silver Nanoparticles-Based Biosensor as a Detection Strategy of Emerging and Pathogenic RNA Virus

The emergence of highly pathogenic and deadly human coronaviruses, namely SARS-CoV and MERS-CoV within the past two decades and currently SARS-CoV-2, have resulted in millions of human death across the world. In addition, other human viral diseases, such as mosquito borne-viral diseases and blood-borne viruses, also contribute to a higher risk of death in severe cases. To date, there is no specific drug or medicine available to cure these human viral diseases. Therefore, the early and rapid detection without compromising the test accuracy is required in order to provide a suitable treatment for the containment of the diseases. Recently, nanomaterials-based biosensors have attracted enormous interest due to their biological activities and unique sensing properties, which enable the detection of analytes such as nucleic acid (DNA or RNA), aptamers, and proteins in clinical samples. In addition, the advances of nanotechnologies also enable the development of miniaturized detection systems for point-of-care (POC) biosensors, which could be a new strategy for detecting human viral diseases. The detection of virus-specific genes by using single-stranded DNA (ssDNA) probes has become a particular interest due to their higher sensitivity and specificity compared to immunological methods based on antibody or antigen for early diagnosis of viral infection. Hence, this review has been developed to provide an overview of the current development of nanoparticles-based biosensors that target pathogenic RNA viruses, toward a robust and effective detection strategy of the existing or newly emerging human viral diseases such as SARS-CoV-2. This review emphasizes the nanoparticles-based biosensors developed using noble metals such as gold (Au) and silver (Ag) by virtue of their powerful characteristics as a signal amplifier or enhancer in the detection of nucleic acid. In addition, this review provides a broad knowledge with respect to several analytical methods involved in the development of nanoparticles-based biosensors for the detection of viral nucleic acid using both optical and electrochemical techniques.

Urgent care center wait times increase for COVID-19 results in August 2020, with rapid testing availability limited

BACKGROUND

In a response to the pandemic, urgent care centers (UCCs) have gained a critical role as a common location for COVID-19 testing. We sought to characterize the changes in testing accessibility at UCCs between March and August 2020 on the basis of testing availability (including rapid antigen testing), wait time for test results, cost of visits, and cost of tests.

METHODS

Data were collected using a secret shopper methodology. Researchers contacted 250 UCCs in 10 states. Investigators used a standardized script to survey centers on their COVID-19 testing availability and policies. UCCs were initially contacted in March and re-called in August. T-tests and chi-square tests were conducted to identify differences between March and August data and differences by center classification.

RESULTS

Our results indicate that both polymerase chain reaction (PCR) tests to detect COVID-19 genetic material and rapid antigen COVID-19 tests have increased in availability. However, wait times for PCR test results have significantly increased to an average of 5.79 days. Additionally, a high proportion of UCCs continue to charge for tests and visits and no significant decrease was found in the proportion of UCCs that charge for COVID-19 testing from March to August. Further, no state reported a majority of UCCs with rapid testing available, indicating an overall lack of rapid testing.

CONCLUSIONS

From March to August, COVID-19 testing availability gradually improved. However, many barriers lie in access to COVID-19 testing, including testing costs, visit costs, and overall lack of availability of rapid testing in the majority of UCCs. Despite the passage of the CARES Act, these results suggest that there is room for additional policy to improve accessibility to testing, specifically rapid testing.

Screening of COVID-19 based on the extracted radiomics features from chest CT images

BACKGROUND AND OBJECTIVE

Radiomics has been widely used in quantitative analysis of medical images for disease diagnosis and prognosis assessment. The objective of this study is to test a machine-learning (ML) method based on radiomics features extracted from chest CT images for screening COVID-19 cases.

METHODS

The study is carried out on two groups of patients, including 138 patients with confirmed and 140 patients with suspected COVID-19. We focus on distinguishing pneumonia caused by COVID-19 from the suspected cases by segmentation of whole lung volume and extraction of 86 radiomics features. Followed by feature extraction, nine feature-selection procedures are used to identify valuable features. Then, ten ML classifiers are applied to classify and predict COVID-19 cases. Each ML models is trained and tested using a ten-fold cross-validation method. The predictive performance of each ML model is evaluated using the area under the curve (AUC) and accuracy.

RESULTS

The range of accuracy and AUC is from 0.32 (recursive feature elimination [RFE]+Multinomial Naive Bayes [MNB] classifier) to 0.984 (RFE+bagging [BAG], RFE+decision tree [DT] classifiers) and 0.27 (mutual information [MI]+MNB classifier) to 0.997 (RFE+k-nearest neighborhood [KNN] classifier), respectively. There is no direct correlation among the number of the selected features, accuracy, and AUC, however, with changes in the number of the selected features, the accuracy and AUC values will change. Feature selection procedure RFE+BAG classifier and RFE+DT classifier achieve the highest prediction accuracy (accuracy: 0.984), followed by MI+Gaussian Naive Bayes (GNB) and logistic regression (LGR)+DT classifiers (accuracy: 0.976). RFE+KNN classifier as a feature selection procedure achieve the highest AUC (AUC: 0.997), followed by RFE+BAG classifier (AUC: 0.991) and RFE+gradient boosting decision tree (GBDT) classifier (AUC: 0.99).

CONCLUSION

This study demonstrates that the ML model based on RFE+KNN classifier achieves the highest performance to differentiate patients with a confirmed infection caused by COVID-19 from the suspected cases.