ArticleSensors and actuators. B, Chemical2021
Nanoparticle transfer biosensors for the non-invasive detection of SARS-CoV-2 antigens trapped in surgical face masks.
Article in Sensors and actuators. B, Chemical, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the advancement on colorimetric nanobiosensors for SARS-CoV-2 detection.Journal of pharmaceutical and biomedical analysis · 2023Pooled it
- Smart Face Masks as Wearable Respiratory Sensors: A Review of Sensor Technologies, Materials, and Future Directions.Advanced healthcare materials · 2026Review
- A Nanoparticle-Based Immunoassay on Facemasks for Evaluating Neutrophilic Airway Inflammation in COPD Patients.Biosensors · 2025Article
- Development of Rapid Aptamer-Based Screening Assay for the Detection of Covid-19 Variants.ACS omega · 2023Article
- Nano-biosensor for SARS-CoV-2/COVID-19 detection: methods, mechanism and interface design.RSC advances · 2023Review
- Advancement in COVID-19 detection using nanomaterial-based biosensors.Exploration (Beijing, China) · 2023Review
- Achieving broad availability of SARS-CoV-2 detections via smartphone-based analysis.Trends in analytical chemistry : TRAC · 2023Review
- Exhaled breath condensate as bioanalyte: from collection considerations to biomarker sensing.Analytical and bioanalytical chemistry · 2023Article
- Therapeutic and diagnostic applications of nanoparticles in the management of COVID-19: a comprehensive overview.Virology journal · 2022Review
- Outlook of various diagnostics and nanodiagnostic techniques for COVID-19.Biosensors & bioelectronics: X · 2022Article
- Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure.Materials (Basel, Switzerland) · 2022Review
- Improving the Quantification of Colorimetric Signals in Paper-Based Immunosensors with an Open-Source Reader.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning Models for Multiple Face Mask Detection under a Complex Big Data Environment.Procedia computer science · 2022Article
- Nanotechnology-based approaches in the fight against SARS-CoV-2.AIMS microbiology · 2021Review
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting SARS-CoV-2 antigens in respiratory tract samples has become a widespread method for screening new SARS-CoV-2 infections. This requires a nasopharyngeal swab performed by a trained healthcare worker, which puts strain on saturated healthcare services. In this manuscript we describe a new approach for non-invasive COVID-19 diagnosis. It consists of using mobile biosensors for detecting viral antigens trapped in surgical face masks worn by patients. The biosensors are made of filter paper containing a nanoparticle reservoir. The nanoparticles transfer from the biosensor to the mask on contact, where they generate colorimetric signals that are quantified with a smartphone app. Sample collection requires wearing a surgical mask for 30 min, and the total assay time is shorter than 10 min. When tested in a cohort of 27 patients with mild or no symptoms, an area under the receiving operating curve (AUROC) of 0.99 was obtained (96.2 % sensitivity and 100 % specificity). Serial measurements revealed a high sensitivity and specificity when masks were worn up to 6 days after diagnosis. Surgical face masks are inexpensive and widely available, which makes this approach easy to implement anywhere. The excellent sensitivity, even when tested with asymptomatic patient samples, along with the mobile detection scheme and non-invasive sampling procedure, makes this biosensor design ideal for mass screening.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.