Amplified parallel antigen rapid test for point-of-care salivary detection of SARS-CoV-2 with improved sensitivity

Nucleic acid based point-of-care diagnostic technology for infectious disease detection using machine learning empowered smartphone-interfaced quantitative colorimetry

We report a nucleic acid-based point of care testing technology for infectious disease detection at resource limited settings by integrating a low-cost portable device with machine learning-empowered quantitative colorimetric analytics that can be interfaced via a smartphone application. We substantiate our proposition by demonstrating the efficacy of this technology in detecting COVID-19 infection from human swab samples, using the RT-LAMP protocol. Comparison with gold standard results from real-time PCR evidences high sensitivity and specificity, ensuring simplicity, portability, and user-friendliness of the technology at the same time. Colorimetric analytics of the reaction output without necessitating the opening of the reaction microchambers enables execution of the complete test workflow without any laboratory control that may otherwise be required stringently for safeguarding against carryover contamination. Seamless sample-to-answer workflow and machine learning-based readout further assures minimal human intervention for the test readout, thus eliminating inevitable inaccuracies stemming from erroneous execution of the test as well as subjectivity in interpreting the outcome. Our results further indicate the possibilities of upgrading the technology to predict the pathogenic load on the infected patients akin to the cyclic threshold value of the real-time PCR, when calibrated with reference to a wide range of 'training' data for the machine learner, thereby putting forward the same as viable alternative to the resource-intensive PCR tests that cannot be made readily accessible at underserved community settings.

Immunochromatographic enhancement strategy for SARS-CoV-2 detection based on nanotechnology

The global outbreak of coronavirus disease 2019 (COVID-19) has been catastrophic to both human health and social development. Therefore, developing highly reliable and sensitive point-of-care testing (POCT) for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a priority. Among all available POCTs, the lateral flow immunoassay (LFIA, also known as immunochromatography) has proved to be effective due to its accuracy, portability, convenience, and speed. In areas with a scarcity of laboratory resources and medical personnel, the LFIA provides an affordable option for the diagnosis of COVID-19. This review offers a comprehensive overview of methods for improving the sensitivity of SARS-CoV-2 detection using immunochromatography based on nanotechnology, sorted according to the different detection targets (antigens, antibodies, and nucleic acids). It also looks into the performance and properties of the various sensitivity enhancement strategies, before delving into the remaining challenges in COVID-19 diagnosis through LFIA. Ultimately, it seeks to provide helpful guidance in selecting an appropriate strategy for SARS-CoV-2 immunochromatographic detection based on nanotechnology.

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Comparison of Saliva and Midturbinate Swabs for Detection of SARS-CoV-2

Saliva is an attractive sample for detecting SARS-CoV-2. However, contradictory reports exist concerning the sensitivity of saliva versus nasal swabs. We followed close contacts of COVID-19 cases for up to 14 days from the last exposure and collected self-reported symptoms, midturbinate swabs (MTS), and saliva every 2 or 3 days. Ct values, viral load, and frequency of viral detection by MTS and saliva were compared. Fifty-eight contacts provided 200 saliva-MTS pairs, and 14 contacts (13 with symptoms) had one or more positive samples. Saliva and MTS had similar rates of viral detection (P = 0.78) and substantial agreement (κ = 0.83). However, sensitivity varied significantly with time since symptom onset. Early on (days -3 to 2), saliva had 12 times (95% CI: 1.2, 130) greater likelihood of viral detection and 3.2 times (95% CI: 2.8, 3.8) higher RNA copy numbers compared to MTS. After day 2 of symptoms, there was a nonsignificant trend toward greater sensitivity using MTS. Saliva and MTS demonstrated high agreement making saliva a suitable alternative to MTS for SARS-CoV-2 detection. Saliva was more sensitive early in the infection when the transmission was most likely to occur, suggesting that it may be a superior and cost-effective screening tool for COVID-19. IMPORTANCE The findings of this manuscript are increasingly important with new variants that appear to have shorter incubation periods emerging, which may be more prone to detection in saliva before detection in nasal swabs. Therefore, there is an urgent need to provide the science to support the use of a detection method that is highly sensitive and widely acceptable to the public to improve screening rates and early detection. The manuscript presents the first evidence that saliva-based RT-PCR is more sensitive than MTS-based RT-PCR in detecting SARS-CoV-2 during the presymptomatic period - the critical period for unwitting onward transmission. Considering other advantages of saliva samples, including the lower cost, greater acceptability within the general population, and less risk to health care workers, our findings further supported the use of saliva to identify presymptomatic infection and prevent transmission of the virus.

False-positivity results in rapid antigen tests for SARS-CoV-2: an umbrella review of meta-analyses and systematic reviews

INTRODUCTION

The rapid antigen detection tests (RADTs) for SARS-CoV-2 infection could contribute to the clinical and public health strategies for managing COVID-19. This umbrella review aimed to explore the accuracy and sensitivity of RADTs for SARS-CoV-2 by assessing the incidence of false positivity associated with them.

AREAS COVERED

Meta-analyses and systematic reviews on the sensitivity and specificity of commercially available RADTs with data on false-positive results were identified by searching the PubMed, EMBASE, Cochrane Library, and Web of Science databases from inception to March 31, 2022. All meta-analyses and systematic reviews on the sensitivity and specificity of rapid antigen tests were included. Data on the author and year, included studies, index tests, sample size, false negatives, false positives, and study quality based on AMSTAR 2 (Assessing the Methodological Quality of Systematic Reviews) rating were extracted from the included meta-analyses and systematic reviews.

EXPERT OPINION

We identified 12 meta-analyses and systematic review that presented data on the false-positive results in RADTs. The false positivity rates in the included studies ranged from 0.0% - 4.0%. This study summarizes the available evidence on the incidence of false positivity in RADTs and shows it is less than 4.0%. Therefore, our findings imply that RADTs can be an appropriate, economic, and rapid detection method for mass screening of COVID-19.