Smartphone-Based High-Throughput Fiber-Integrated Immunosensing System for Point-of-Care Testing of the SARS-CoV-2 Nucleocapsid Protein

Abstract

To control the coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need for simple, rapid, and reliable detection methods to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, especially in community hospitals or clinical centers. The SARS-CoV-2 nucleocapsid protein (NP) is an important index for diagnosis of COVID-19. Here, we proposed a smartphone-based high-throughput fiber-integrated immunosensing system (HFIS) for detecting the SARS-CoV-2 NP in serum samples within 45 min. For the testing of NP standards, the linear detection range was 7.8−1000 pg/mL, the limit of detection was 7.5 pg/mL, and the cut-off value was 8.923 pg/mL. Twenty-five serum samples from clinically diagnosed COVID-19 patients and 100 negative control samples from healthy blood donors were tested for SARS-CoV-2 NP by HFIS, and the obtained results were compared with those of ELISA and Simple Western analysis. The results showed that the HFIS sensitivity and specificity were 72% [95% confidence interval (CI): 52.42−85.72%] and 100% (95% CI: 96.11−100%), respectively, which significantly correlated with those from the commercial ELISA kit and Simple Western analysis. This portable high-throughput HFIS assay could be an alternative test for detecting SARS-CoV-2 NP in blood samples on site.

CESSAT: A chemical additive-enhanced single-step accurate CRISPR/Cas13 testing system for field-deployable ultrasensitive detection and genotyping of SARS-CoV-2 variants of concern

“…Fiber-integrated devices can allow a flexible and compact design for cost-effective, highly sensitive, and stable optical signal readout ( Brown et al, 2020 ; Kong et al, 2017 ; Wu et al, 2022 ). In the present study, a smartphone-based fiber-integrated homemade device was developed to enable thermal control of viral lysis, single-step SHERLOCK reaction and quantitative analysis of SARS-CoV-2 genotyping results.…”

Achieving broad availability of SARS-CoV-2 detections via smartphone-based analysis

“… Sensor Samples Mechanisms Detection limit; time; sensitivity/accuracy Data analysis Ref. NPs transfer biosensors Virus in face masks A polymer-modified filter paper stored antibody-decorated Au NPs for recognizing NP NP (3 ng mL −1 ); <10 min; 96.2% sensitivity and 100% specificity The smartphone camera with a commercial reader [ 61 ] NLICS Clinical samples LFA for color reaction on the test strip for recognizing NP 0.026 ng/mL NP; 25 min; 76.2% sensitivity and 95.1% accuracy Smartphone with author-designed App and portable photometer [ 58 ] Colorimetric immunosensor Saliva samples Antibody conjugated magnetic beads to recognize SP and a 96-well wax-printed paper plate for color visualization 100 fg/mL SP, 1.6 × 10 1 PFU/mL SARS-CoV-2; 45 min; 100% accurate for 6 negative and 6 positive saliva samples Smartphone with Spotxel free-charge app for image analysis [ 59 ] HFIS Clinical serum samples PTEM-coated microplate for the immunoassay and a sandwich recognition method for analysis of NP NP (7.5 pg/mL); 45 min; 72% sensitivity and 95% accuracy Optical fibers for light transmission and collection; Designed App for data processing [ 62 ] TEMFIS Patient samples, vaccinees and healthy blood TEM-microplate with optical fibers transmission immunosensing of Nab Nab; 45 min; positivity (sensitivity) in 92.68% and 76% vaccinees, negativity (specificity) in 100% Optical fibers for light transmission and collection; Designed App for data processing [ 63 ] MSAA Clinical swab samples Sandwich complexes formed between magnetic bead/NP/PtNP and bright field images of oxygen microbubbles generated through catalysis of H 2 O 2 decomposition NP (0.5 pg/mL); 30 min; PPA = 97%, 53%, 26%, 45 for symptom onset <7, 7–12, >12 days and Asymptomatic, 97% for negative cases Computer vision image recognition and ML-based algorithms on smartphones [ 64 ] QD-LFIA Human serum or whole blood samples …”

Engineered two-dimensional nanomaterials based diagnostics integrated with internet of medical things (IoMT) for COVID-19

“…83 To clarify, cellphones possess the capability to gather data from diagnostic devices, execute a diverse range of operations, and relay statistics from nearby nano-systems over both connected and wireless networks. 84 Smartphone-based diagnosis technologies are attracting attention as a communication tool for healthcare professionals and patients. 85 These technologies can store demographic, terrestrial, and analytical information, improve disease identification and treatment, and enhance communication across fast networks like Wi-Fi and online networks.…”