COVID-19 Diagnostics Research Articles

Portable microfluidic impedance biosensor for SARS-CoV-2 detection

Pandemics as the one we are currently facing, where fast-spreading viruses present a threat to humanity, call for simple and reliable methods to perform early diagnosis, enabling detection of very low pathogen loads even before symptoms start showing in the host. So far, standard polymerase chain reaction (PCR) is the most reliable method for doing so, but it is rather slow and needs specialized reagents and trained personnel to operate it. Additionally, it is expensive and not easily accessible. Therefore, developing miniaturized and portable sensors which perform early detection of pathogens with high reliability is necessary to not only prevent the spreading of the disease but also to monitor the effectiveness of the developed vaccines and the appearance of new pathogenic variants. Thus, in this work we develop a sensitive microfluidic impedance biosensor for the direct detection of SARS-CoV-2, towards a mobile point-of-care (POC) platform. The operational parameters are optimized with the aid of design-of-experiment (DoE), for an accurate detection of the viral antigens using electrochemical impedance spectroscopy (EIS). We perform the biodetection of buffer samples spiked with fM concentration levels and validate the biosensor in a clinical context of relevance by analyzing 15 real patient samples up to a Ct value (cycle threshold) of 27. Finally, we demonstrate the versatility of the developed platform using different settings, including a small portable potentiostat, using multiple channels for self-validation, as well as with single biosensors for a smartphone-based readout. This work contributes to the rapid and reliable diagnostics of COVID-19 and can be extended to other infectious diseases, allowing the monitoring of viral load in vaccinated and unvaccinated people to anticipate a potential relapse of the disease.

Factors Contributing to False Negative Results when Performing Diagnostics of COVID-19 by Reverse Transcription-Polymerase Chain Reaction in Real Time

Reverse transcription-real-time polymerase chain reaction (RT-PCR) is currently the leading laboratory diagnostic tool for COVID-19. With the help of RT-PCR-RT, diagnostics is carried out at the early stages of the development of the disease. The studied clinical samples, as a rule, are swabs from the pharynx and nasopharynx. The aim of the work is to identify and analyze the factors that contribute to false negative results when diagnosing COVID-19 by RT-PCR-RT. It is shown in the article, that the analytical sensitivity of the real-time RT-PCR method is not the main factor in assessing the possibility of obtaining false negative results. The paper considers the effect of drinking hot drinks (tea, coffee), alcohol and smoking (immediately before sampling) on the results of the analysis of swabs from the pharynx and nasopharynx by RT-PCR RT. It was found that the intake of hot drinks before sampling (the probability of obtaining a false negative result is at least 80%) influences most of all on the possibility of obtaining a false negative result in RT-PCR-RT in swabs from the pharynx. The use of small doses of alcohol (0.25–0.33 g of ethanol per 1 kg of body weight) has a lesser effect. Smoking immediately prior to sample collection does not lead to a false negative result in real-time RT-PCR. None of the considered influences affects the possibility of obtaining false negative results in the analysis of swabs from the nasopharynx. Recommendations were made on the need for simultaneous examination of both throat swabs and nasopharyngeal swabs in the ongoing diagnosis of COVID-19.

Multisector Collaboration Model for COVID-19 Diagnostics in the Philippines

Multisector Collaboration Model for COVID-19 Diagnostics in the Philippines

Enhancing physicians' radiology diagnostics of COVID-19's effects on lung health by leveraging artificial intelligence.

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19's effects on patients' lung health. Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU). Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians' diagnosis, and test for improvements on physicians' performance when using the prediction algorithm. Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%.

Development of a Module for COVID-19 Diagnostics Based on Computed Tomography Images of the Chest Based on Computer Vision Methods

The implementation of a module of a medical decision support system for diagnosing COVID-19 using chest CT images is considered. The U-Net architecture is used for segmentation of lung parenchyma and pathological areas in chest CT images, the DSC and IoU values for parenchyma are 0.951 and 0.933, for pathological areas — 0.97 and 0.959, respectively. A method for image pre-processing based on adaptive histogram equalization is described. Methods for segmentation masks post­processing are also proposed. The first of them is necessary to separate masks into masks of the left and right lungs; it is based on the analysis of areas and mutual positions of contours. The second one is needed to eliminate artifacts. In addition to image processing methods, approaches that generate the data necessary for radiologists to make a diagnosis are also implemented (the volumes of both lungs and pathological findings in them are calculated, percentages of parenchymal tissue involvement in the pathological process are determined, the severity of the disease is assessed). The algorithms for generating a processed series of images and a DICOM SR are described. The average time spent by the module on processing one CT study containing about 600 slices, with a video memory limit of 6 GB, is 68 s, and with a limit of 8 GB — 56 s. Considering that the approximate time spent by a radiologist to process a study is about 6 minutes, the developed module can be effectively used in medical practice to reduce the burden on medical personnel.

Identification of Common Dysregulated Genes in COVID-19 and Hypersensitivity Pneumonitis: A Systems Biology and Machine Learning Approach.

A comprehensive knowledge on systems biology of severe acute respiratory syndrome coronavirus 2 is crucial for differential diagnosis of COVID-19. Interestingly, the radiological and pathological features of COVID-19 mimic that of hypersensitivity pneumonitis (HP), another pulmonary fibrotic phenotype. This motivated us to explore the overlapping pathophysiology of COVID-19 and HP, if any, and using a systems biology approach. Two datasets were obtained from the Gene Expression Omnibus database (GSE147507 and GSE150910) and common differentially expressed genes (DEGs) for both diseases identified. Fourteen common DEGs, significantly altered in both diseases, were found to be implicated in complement activation and growth factor activity. A total of five microRNAs (hsa-miR-1-3p, hsa-miR-20a-5p, hsa-miR-107, hsa-miR-16-5p, and hsa-miR-34b-5p) and five transcription factors (KLF6, ZBTB7A, ELF1, NFIL3, and ZBT33) exhibited highest interaction with these common genes. Next, C3, CFB, MMP-9, and IL1A were identified as common hub genes for both COVID-19 and HP. Finally, these top-ranked genes (hub genes) were evaluated using random forest classifier to discriminate between the disease and control group (coronavirus disease 2019 [COVID-19] vs. controls, and HP vs. controls). This supervised machine learning approach demonstrated 100% and 87.6% accuracy in differentiating COVID-19 from controls, and HP from controls, respectively. These findings provide new molecular leads that inform COVID-19 and HP diagnostics and therapeutics research and innovation.

Portable Surface Plasmon Resonance Detector for COVID-19 Infection.

Methods based on nucleic acid detection are currently the most commonly used technique in COVID-19 diagnostics. Although generally considered adequate, these methods are characterised by quite a long time-to-result and the necessity to prepare the material taken from the examined person-RNA isolation. For this reason, new detection methods are being sought, especially those characterised by the high speed of the analysis process from the moment of sampling to the result. Currently, serological methods of detecting antibodies against the virus in the patient's blood plasma have attracted much attention. Although they are less precise in determining the current infection, such methods shorten the analysis time to several minutes, making it possible to consider them a promising method for screening tests in people with suspected infection. The described study investigated the feasibility of a surface plasmon resonance (SPR)-based detection system for on-site COVID-19 diagnostics. A simple-to-use portable device was proposed for the fast detection of anti-SARS-CoV-2 antibodies in human plasma. SARS-CoV-2-positive and -negative patient blood plasma samples were investigated and compared with the ELISA test. The receptor-binding domain (RBD) of spike protein from SARS-CoV-2 was selected as a binding molecule for the study. Then, the process of antibody detection using this peptide was examined under laboratory conditions on a commercially available SPR device. The portable device was prepared and tested on plasma samples from humans. The results were compared with those obtained in the same patients using the reference diagnostic method. The detection system is effective in the detection of anti-SARS-CoV-2 with the detection limit of 40 ng/mL. It was shown that it is a portable device that can correctly examine human plasma samples within a 10 min timeframe.

The critical role of engineering in the rapid development of COVID-19 diagnostics: Lessons from the RADx Tech Test Verification Core.

Engineering plays a critical role in the development of medical devices, and this has been magnified since 2020 as severe acute respiratory syndrome coronavirus 2 swept over the globe. In response to the coronavirus disease 2019, the National Institutes of Health launched the Rapid Acceleration of Diagnostics (RADx) initiative to help meet the testing needs of the United States and effectively manage the pandemic. As the Engineering and Human Factors team for the RADx Tech Test Verification Core, we directly assessed more than 30 technologies that ultimately contributed to an increase of the country's total testing capacity by 1.7 billion tests to date. In this review, we present central lessons learned from this "apples-to-apples" comparison of novel, rapidly developed diagnostic devices. Overall, the evaluation framework and lessons learned presented in this review may serve as a blueprint for engineers developing point-of-care diagnostics, leaving us better prepared to respond to the next global public health crisis rapidly and effectively.

Harnessing Deep Learning for Omics in an Era of COVID-19

Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.

Sniffer dogs performance is stable over time in detecting COVID-19 positive samples and agrees with the rapid antigen test in the field

Rapid antigen diagnostic (RAD) tests have been developed for the identification of the SARS-CoV-2 infection. However, they require nasopharyngeal or nasal swab, which is invasive, uncomfortable, and aerosolising. The use of saliva test was also proposed but has not yet been validated. Trained dogs may efficiently smell the presence of SARS-CoV-2 in biological samples of infected people, but further validation is needed both in laboratory and in field. The present study aimed to (1) assess and validate the stability over a specific time period of COVID-19 detection in humans’ armpit sweat by trained dogs thanks to a double-blind laboratory test–retest design, and (2) assess this ability when sniffing people directly. Dogs were not trained to discriminate against other infections. For all dogs (n. 3), the laboratory test on 360 samples yielded 93% sensitivity and 99% specificity, an 88% agreement with the Rt-PCR, and a moderate to strong test–retest correlation. When sniffing people directly (n. 97), dogs’ (n. 5) overall sensitivity (89%) and specificity (95%) were significantly above chance level. An almost perfect agreement with RAD results was found (kappa 0.83, SE 0.05, p = 0.001). Therefore, sniffer dogs met appropriate criteria (e.g., repeatability) and WHO's target product profiles for COVID-19 diagnostics and produced very promising results in laboratory and field settings, respectively. These findings support the idea that biodetection dogs could help reduce the spread of the virus in high-risk environments, including airports, schools, and public transport.

Electrochemical and Bioelectrochemical Sensing Platforms for Diagnostics of COVID-19.

Rapid transmission and high mortality rates caused by the SARS-CoV-2 virus showed that the best way to fight against the pandemic was through rapid, accurate diagnosis in parallel with vaccination. In this context, several research groups around the world have endeavored to develop new diagnostic methods due to the disadvantages of the gold standard method, reverse transcriptase polymerase chain reaction (RT-PCR), in terms of cost and time consumption. Electrochemical and bioelectrochemical platforms have been important tools for overcoming the limitations of conventional diagnostic platforms, including accuracy, accessibility, portability, and response time. In this review, we report on several electrochemical sensors and biosensors developed for SARS-CoV-2 detection, presenting the concepts, fabrication, advantages, and disadvantages of the different approaches. The focus is devoted to highlighting the recent progress of electrochemical devices developed as next-generation field-deployable analytical tools as well as guiding future researchers in the manufacture of devices for disease diagnosis.

Discovery of DNA aptamers targeting SARS-CoV-2 nucleocapsid protein and protein-binding epitopes for label-free COVID-19 diagnostics

The spread of COVID-19 has affected billions of people across the globe, and the diagnosis of viral infection still needs improvement. Because of high immunogenicity and abundant expression during viral infection, SARS-CoV-2 nucleocapsid (N)protein could be an important diagnostic marker. This study aimed to develop a label-free optical aptasensor fabricated with a novel single-stranded DNA aptamer to detect the N protein. The N-binding aptamers selected using asymmetric-emulsion PCR-SELEX and their binding affinity and cross-reactivity were characterized by biolayer interferometry. The tNSP3 aptamer (44 nt) was identified to bind the N protein of wild type and Delta and Omicron variants with high affinity (KD in the range of 0.6-3.5nM). Utilizing tNSP3 to detect the N protein spiked in human saliva evinced the potential of this aptamer with a limit of detection of 4.5nM. Mass spectrometry analysis was performed along with molecular dynamics simulation to obtain an insight into how tNSP3 binds to the N protein. The identified epitope peptides are localized within the RNA-binding domain and C terminus of the N protein. Hence, we confirmed the performance of this aptamer as an analytical tool for COVID-19 diagnosis.

Cytometry and analytical methods in COVID‐19 diagnostics and potential treatment: A special issue on Cytometry Part A

Cytometry and analytical methods in <scp>COVID</scp>‐19 diagnostics and potential treatment: A special issue on Cytometry Part A

Innovative technology for rapid molecular diagnostics: COVID-19 and other respiratory tract infections

The outbreak of the COVID-19 pandemic presented the world with many new challenges such as rapid and accurate diagnosis of infected individuals. RT-PCR has become the gold standard in COVID-19 diagnostics, but its limitations are: long turnaround time and the need to be conducted by specialized staff. The need for rapid and easy-to-use diagnostic tests led to the development of ID NOW — a rapid molecular test that provides a COVID-19 diagnosis in less than 15 minutes and can be performed by support staff in point-of-care (POC) locations. It can also detect other infections with similar symptoms, such as influenza or RSV. Due to rapid differentiation between COVID-19 and other infections patients can be isolated quickly and hospital departments operate efficiently. In this publication we present the recommendations for the use of the diagnostic test ID NOW based on clinical research results and opinions of experts in different medical fields.

Effect of COVID-19 pandemic on vaccination and treatment pattern of prostate cancer: a comprehensive literature review, phase 2

During the COVID-19 pandemic, overall screening, diagnostics, and treatment have faced a downtrend, especially during the first wave and lockdown. Statistics showed improvements starting from the late era of the pandemic and the end of 2020. To improvise, the risk of delayed curative treatment was measured and treatment plans were amended accordingly in order to lower the number of hospital visits needed while the results were still contradictory. The protective role of androgen deprivation therapy on COVID-19 triggered many debates, but the majority of clinical studies found no significant association. Concerns about a reduced immune response to vaccination in patients with prostate cancer occurred, but additional research is needed. The pandemic added additional burdens to patients with prostate cancer and different aspects of the quality of life of patients were assessed. While we anticipate that we are reaching the end of the pandemic, it is essential to re-examine how the pandemic has changed the overall care of patients with prostate cancer and how to proceed even further.

Technological Innovations in COVID-19 Diagnostics in Singapore

Technological Innovations in COVID-19 Diagnostics in Singapore

Prepared Oral Statement of Professors David S. Levine and Joshua D. Sarnoff in ITC Investigation 332-996, on Covid-19 Diagnostics and Therapeutics: Supply, Demand, and Trips Agreement Flexibilities

Prepared Oral Statement of Professors David S. Levine and Joshua D. Sarnoff in ITC Investigation 332-996, on Covid-19 Diagnostics and Therapeutics: Supply, Demand, and Trips Agreement Flexibilities

ПАРАМЕТРИ ЛІПІДНОГО ПРОФІЛЮ У ДОНОРІВ, ЯКІ ПЕРЕХВОРІЛИ НА COVID-19 ТА МАЮТЬ РІЗНІ ТИТРИ АНТИ-SARS-CoV-2 IgG У ПЛАЗМІ КРОВІ

COVID-19, caused by SARS-CoV-2, is a systemic disorder of with possible pathological complications in human organism. One of the critical targets of SARS-CoV-2 is the metabolism of lipids and products, which may influence the changes in patients with illness. Currently, the mechanism of binding between SARS-CoV-2 and lipids, as well as the consequences of these processes, is unknown. In addition, the long post-COVID-19 period has been confirmed in the majority of people who have suffered from COVID-19; therefore, additional attention should be paid to studying the biochemical parameters of lipid metabolism after SARS-CoV-2 infection. Our work is aimed the studying of lipid profile including concentration of total cholesterol, high-density lipoproteins (HDL), low-density lipoproteins (LDL), very low-density lipoproteins (VLDL) and triglycerides (TG) in donor groups who had suffered from COVID-19 and had different titers of anti-SARS-CoV-2 IgG in blood plasma. We selected donor groups with maximum and minimum changes of parameters among donor groups with titers of anti-SARS-CoV-2 IgG ≥ 10 Index (S/C), than we compared these groups to donor group with titer of anti-SARS-CoV-2 IgG 0 Index (S/C). We established that the donor group with titer of anti-SARS-CoV-2 95 ± 5 Index (S/C) was characterized by the maximum concentration of total cholesterol, LDL, VLDL, TG, while the donor group with titer of anti-SARS-CoV-2 IgG 75 ± 5 Index (S/C) was determined as the minimum concentration of total cholesterol, HDL, LDL. In addition, donor group with titer of anti-SARS-CoV-2 IgG 85 ± 5 Index (S/C) was described a lower concentration of VLDL and TG then in donors – 0 Index (S/C). These changes may be linked to a potential risk of cardiovascular disease (CVD). Our work can be useful for future research targeting the study of the changes in lipid metabolism during SARS-CoV-2 infection and in the development of the treatment of COVID-19 or laboratory diagnostics of physiological processes in the post-COVID-19 period.

Design and implementation of a digital site-less clinical study of serial rapid antigen testing to identify asymptomatic SARS-CoV-2 infection.

Rapid antigen detection tests (Ag-RDT) for SARS-CoV-2 with emergency use authorization generally include a condition of authorization to evaluate the test's performance in asymptomatic individuals when used serially. We aim to describe a novel study design that was used to generate regulatory-quality data to evaluate the serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals. This prospective cohort study used a siteless, digital approach to assess longitudinal performance of Ag-RDT. Individuals over 2 years old from across the USA with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Participants throughout the mainland USA were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported. A total of 7361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 US states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide. The digital site-less approach employed in the "Test Us At Home" study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19 and can be adapted across research disciplines to optimize study enrollment and accessibility.

Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms.

The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest. In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months. We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data. We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection. The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.