ArticleScience advances2020
Virus detection using nanoparticles and deep neural network-enabled smartphone system.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 76 citations in OpenAlex.
- Trends of nucleic acid - based point-of-care diagnostics for infectious diseases.Journal of biological engineering · 2026Review
- Next-generation viral detection through AI-enhanced nanotechnology: advances, challenges, and future directions.Frontiers in molecular biosciences · 2026Article
- Automated, Deep Learning-Enabled Immunoassay Microfluidic Cartridge for Viral Pathogen Detection.Advanced materials technologies · 2025Article
- Review
- Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics.Nature communications · 2025Article
- Self-Assembly of Nanogold Triplets on Trimeric Viral Proteins for Infectious Disease Diagnosis.ACS nano · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.Science advances · 2025Article
- Polyethylene Glycol-Mediated Directional Conjugation of Biological Molecules for Enhanced Immunoassays at the Point-of-Care.Polymers · 2023Article
- Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics.Communications medicine · 2023Article
- Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay.Nature communications · 2023Article
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- Review
- Review
- Virus Detection: From State-of-the-Art Laboratories to Smartphone-Based Point-of-Care Testing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Noble Metal Nanoparticle Biosensors: From Fundamental Studies toward Point-of-Care Diagnostics.Accounts of chemical research · 2022Review
- Advantages of Using Extremophilic Bacteria for the Biosynthesis of Metallic Nanoparticles and Its Potential for Rare Earth Element Recovery.Frontiers in microbiology · 2022Review
- SARS-CoV-2 RNA Detection by a Cellphone-Based Amplification-Free System with CRISPR/CAS-Dependent Enzymatic (CASCADE) Assay.Advanced materials technologies · 2021Article
- Glyconanomaterials for Human Virus Detection and Inhibition.Nanomaterials (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
Emerging and reemerging infections present an ever-increasing challenge to global health. Here, we report a nanoparticle-enabled smartphone (NES) system for rapid and sensitive virus detection. The virus is captured on a microchip and labeled with specifically designed platinum nanoprobes to induce gas bubble formation in the presence of hydrogen peroxide. The formed bubbles are controlled to make distinct visual patterns, allowing simple and sensitive virus detection using a convolutional neural network (CNN)-enabled smartphone system and without using any optical hardware smartphone attachment. We evaluated the developed CNN-NES for testing viruses such as hepatitis B virus (HBV), HCV, and Zika virus (ZIKV). The CNN-NES was tested with 134 ZIKV- and HBV-spiked and ZIKV- and HCV-infected patient plasma/serum samples. The sensitivity of the system in qualitatively detecting viral-infected samples with a clinically relevant virus concentration threshold of 250 copies/ml was 98.97% with a confidence interval of 94.39 to 99.97%.
Identifiers
What OpenQuestion holds
Registered trials
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.