Diagnostic techniques for COVID-19: A mini-review

Development of a unit conversion tool for five quantitative anti-spike assays and agreement analysis of three qualitative anti-nucleocapsid assays for SARS-CoV-2

Commercially available assays for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike (S) or anti-nucleocapsid (N) antibodies differ in units, making results comparisons challenging. This study aimed to develop conversion equations between five quantitative anti-S antibody tests and to assess the agreement over time between three qualitative anti-N antibody tests. Blood samples from 24 216 vaccinated healthcare workers in Hiroshima Prefecture, Japan, were analyzed for anti-S antibodies using five quantitative tests (Abbott, Fujirebio, Ortho, Sysmex, Roche) and for anti-N antibodies using three qualitative tests (Abbott, Sysmex, Roche). Geometric mean regression was performed to establish equations for converting measured values between the five quantitative tests. Fleiss κ statistic was used to assess the agreement between the three qualitative tests. A strong correlation (Pearson's coefficient r > 0.9) was found for each pair of the five quantitative tests measuring anti-S antibodies, enabling the development of equations to convert values between each pair. Using these equations, which are based on the original output unit of each test, values obtained from one test can be transformed to be equivalent to the corresponding values in another test. For the three tests for anti-N antibodies, the agreement was substantial in the total sample (Fleiss' κ, 0.74) and moderate among those with self-reported past coronavirus disease 2019 (COVID-19) infection (Fleiss' κ, 0.39). The agreement decreased with time after infection. Reduced agreement between anti-N antibodies tests over time suggests caution in comparing seroepidemiological studies of COVID-19 exposure based on anti-N antibodies measurement. The findings could help improve antibody measurement systems and inform public health decision-makers.

UbiNAAT: a multiplexed point-of-care nucleic acid diagnostic platform for rapid at-home pathogen detection

The COVID-19 pandemic increased demands for respiratory disease testing to facilitate treatment and limit transmission, demonstrating in the process that most existing test options were too complex and expensive to perform in point-of-care or home scenarios. Lab-based molecular techniques can detect viral RNA in respiratory illnesses but are expensive and require trained personnel, while affordable antigen-based home tests lack sensitivity for early detection in newly infected or asymptomatic individuals. The few home RNA detection tests deployed were prohibitively expensive. Here, we demonstrate a point-of-care, paper-based rapid analysis device that simultaneously detects multiple viral RNAs; it is demonstrated on two common respiratory viruses (COVID-19 and influenza A) spiked onto a commercial nasal swab. The automated device requires no sample preparation by the user after insertion of the swab, minimizing user operation steps. We incorporated lyophilized amplification reagents immobilized in a porous matrix, a novel thermally actuated valve for multiplexed fluidic control, a printed circuit board that performs on-device lysis and amplification within a cell-phone-sized disposable device. Reverse transcription loop-mediated isothermal amplification (RT-LAMP) products are visualized via fluorescent dyes using a modified cell phone, resulting in detection of as few as 104 viral copies per swab across both pathogens within 30 minutes. This integrated platform could be commercialized in a form that would be inexpensive, portable, and sensitive; it can readily be multiplexed to detect as many as 8 different RNA or DNA sequences, and adapted to any desired RNA or DNA detection assays.

Experimental design for the development of a multiplex antigen lateral flow immunoassay detecting the Southern African Territory (SAT) serotypes of foot-and-mouth disease virus

Antigenic lateral flow immunoassays (LFIAs) rely on the non-competitive sandwich format, including a detection (labelled) antibody and a capture antibody immobilised onto the analytical membrane. When the same antibody is used for the capture and the detection (single epitope immunoassay), the saturation of analyte epitopes by the probe compromises the capture and lowers the sensitivity. Hence, several factors, including the amount of the probe, the antibody-to-label ratio, and the contact time between the probe and the analyte before reaching the capture antibody, must be adjusted. We explored different designs of experiments (full-factorial, optimal, sub-optimal models) to optimise a multiplex sandwich-type LFIA for the diagnosis and serotyping of two Southern African Territory (SAT) serotypes of the foot-and-mouth disease virus, and to evaluate the reduction of the number of experiments in the development. Both assays employed single epitope sandwich, so most influencing variables on the sensitivity were studied and individuated. We upgraded a previous device increasing the sensitivity by a factor of two and reached the visual limit of detection of 103.7 and 104.0 (TCID/mL) for SAT 1 and SAT 2, respectively. The positioning of the capture region along the LFIA strip was the most influent variable to increase the detectability. Furthermore, we confirmed that the 13-optimal DoE was the most convenient approach for designing the device.

Adaptive group testing strategy for infectious diseases using social contact graph partitions

Mass testing is essential for identifying infected individuals during an epidemic and allowing healthy individuals to return to normal social activities. However, testing capacity is often insufficient to meet global health needs, especially during newly emerging epidemics. Dorfman's method, a classic group testing technique, helps reduce the number of tests required by pooling the samples of multiple individuals into a single sample for analysis. Dorfman's method does not consider the time dynamics or limits on testing capacity involved in infection detection, and it assumes that individuals are infected independently, ignoring community correlations. To address these limitations, we present an adaptive group testing (AGT) strategy based on graph partitioning, which divides a physical contact network into subgraphs (groups of individuals) and assigns testing priorities based on the social contact characteristics of each subgraph. Our AGT aims to maximize the number of infected individuals detected and minimize the number of tests required. After each testing round (perhaps on a daily basis), the testing priority is increased for each neighboring group of known infected individuals. We also present an enhanced infectious disease transmission model that simulates the dynamic spread of a pathogen and evaluate our AGT strategy using the simulation results. When applied to 13 social contact networks, AGT demonstrates significant performance improvements compared to Dorfman's method and its variations. Our AGT strategy requires fewer tests overall, reduces disease spread, and retains robustness under changes in group size, testing capacity, and other parameters. Testing plays a crucial role in containing and mitigating pandemics by identifying infected individuals and helping to prevent further transmission in families and communities. By identifying infected individuals and helping to prevent further transmission in families and communities, our AGT strategy can have significant implications for public health, providing guidance for policymakers trying to balance economic activity with the need to manage the spread of infection.

Visualized Lateral Flow Assay for Dual Viral RNA Fragment Detection

In this technical note, we report an easy-to-produce, reverse-transcription-free, and protein-enzyme-free lateral flow assay for detection of viral RNA fragments by taking SARS-CoV-2 ORF1ab and N as target models. Catalytic hairpin assembly is utilized for dual RNA fragment orthogonal reaction to generate copious amounts of opened hairpin duplexes, which bridge DNA-modified gold nanoparticles and capture strands on the strip to induce coloration. The dual RNA fragments are simultaneously visualized during one time of sample flow, and single-base-mismatched nontarget sequences can be differentiated. The test strip can be flexibly adapted to detect evolutional SARS-CoV-2 variants such as Delta and Omicron. It also shows potential in visually detecting long-sequence virus simulants and achieves a sensitivity comparable to that of RT-qPCR by incorporation with upstream sample amplification. The lateral flow assay should offer a convenient and reliable technique for viral nucleic acid detection.

Colorimetric detection of viral RNA fragments based on an integrated logic-operated three-dimensional DNA walker

Timely and accurate detection of virus is crucial for preventing spread of disease and early treatment of the infected cases. Herein we design an integrated logic-operated three-dimensional DNA walker for colorimetric detection of viral RNA fragments, by taking SARS-CoV-2 as an example. The DNA walker is composed of small amounts of dually-blocked walking strands and large amounts of dual-stem-loop track strands on gold nanoparticles. The walking strand contains a swing arm domain and a DNAzyme domain blocked at both sides of catalytic core, while the track strand contains a substrate domain located at the peripheral larger loop. Only the presence of both ORF1ab and N RNA fragments can fully de-block the walking strand, which then continuously hybridizes with track strands and cleaves them by DNAzyme-catalyzed hydrolysis. As the cleavage of track strands from long-stranded, double stem-loop structure to short-stranded, linear sequence, the DNA walker shows much lowered stability due to decreased negative charge density and diminished steric repulsion, which then gets aggregated at high salt concentration, accompanied by a visible color change. The colorimetric DNA walker detects RNA fragments down to 1 nM, responds dual viral genes in a "AND" logic way, and shows high specificity to target sequence. It can further detect large nucleic acids containing ORF1ab and N sequences, and reach 200 copies/mL detection limit by coupling a simple upstream amplification of sample. The method may provide a convenient way for reliable detection of viral RNA.

Visual Detection of COVID-19 from Materials Aspect

ABSTRACT

In the recent COVID-19 pandemic, World Health Organization emphasized that early detection is an effective strategy to reduce the spread of SARS-CoV-2 viruses. Several diagnostic methods, such as reverse transcription-polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA), have been applied based on the mechanism of specific recognition and binding of the probes to viruses or viral antigens. Although the remarkable progress, these methods still suffer from inadequate cellular materials or errors in the detection and sampling procedure of nasopharyngeal/oropharyngeal swab collection. Therefore, developing accurate, ultrafast, and visualized detection calls for more advanced materials and technology urgently to fight against the epidemic. In this review, we first summarize the current methodologies for SARS-CoV-2 diagnosis. Then, recent representative examples are introduced based on various output signals (e.g., colorimetric, fluorometric, electronic, acoustic). Finally, we discuss the limitations of the methods and provide our perspectives on priorities for future test development.

Colorimetric detection of RNA fragments based on associated toehold-mediated reaction and gold nanoparticles

Herein we report a reverse transcription-free, label-free and enzyme-free colorimetric method for RNA nucleic acid fragments detection. The method can conveniently determine the presence of dual gene targets and distinguish single nucleotide polymorphism by visual observation.

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.