ArticleBiosensors & bioelectronics: X2022
'All In One' SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnostics.
Article in Biosensors & bioelectronics: X, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The trial behind it
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Who cites it
22 citing papers in PubMed.
- A comprehensive review of AI innovations for tackling antimicrobial resistance.Le infezioni in medicina · 2026Review
- AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring.Biosensors · 2025Review
- Machine-Learning-Aided Advanced Electrochemical Biosensors.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.ACS sensors · 2024Review
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Improving high throughput manufacture of laser-inscribed graphene electrodes via hierarchical clustering.Scientific reports · 2024Article
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Advances in nanobiosensors during the COVID-19 pandemic and future perspectives for the post-COVID era.Nano convergence · 2024Review
- Multiplexed discrimination of SARS-CoV-2 variants via plasmonic-enhanced fluorescence in a portable and automated device.Nature biomedical engineering · 2023Article
- Development of practical techniques for simultaneous detection and distinction of current and emerging SARS-CoV-2 variants.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2023Review
- Smartphone-based point-of-care testing of the SARS-CoV-2: A systematic review.Scientific African · 2023Article
- Smart Multiplex Point-of-Care Platform for Simultaneous Drug Monitoring.ACS applied materials & interfaces · 2023Article
- Advancements in SARS-CoV-2 Testing: Enhancing Accessibility through Machine Learning-Enhanced Biosensors.Micromachines · 2023Review
- Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial intelligence-driven electrochemical immunosensing biochips in multi-component detection.Biomicrofluidics · 2023Article
- Nanotechnology in the COVID-19 era: Carbon-based nanomaterials as a promising solution.Carbon · 2023Review
- Double-antibody-based nano-biosensing system for the onsite monitoring of SARS-CoV-2 variants.Microsystems & nanoengineering · 2023Article
- Achieving broad availability of SARS-CoV-2 detections via smartphone-based analysis.Trends in analytical chemistry : TRAC · 2023Review
- Ruthenium and Nickel Molybdate-Decorated 2D Porous Graphitic Carbon Nitrides for Highly Sensitive Cardiac Troponin Biosensor.Biosensors · 2022Article
- Smartphone-Based Multiplexed Biosensing Tools for Health Monitoring.Biosensors · 2022Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Point of care (PoC) devices are highly demanding to control current pandemic, originated from severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Though nucleic acid-based methods such as RT-PCR are widely available, they require sample preparation and long processing time. PoC diagnostic devices provide relatively faster and stable results. However they require further investigation to provide high accuracy and be adaptable for the new variants. In this study, laser-scribed graphene (LSG) sensors are coupled with gold nanoparticles (AuNPs) as stable promising biosensing platforms. Angiotensin Converting Enzyme 2 (ACE2), an enzymatic receptor, is chosen to be the biorecognition unit due to its high binding affinity towards spike proteins as a key-lock model. The sensor was integrated to a homemade and portable potentistat device, wirelessly connected to a smartphone having a customized application for easy operation. LODs of 5.14 and 2.09 ng/mL was achieved for S1 and S2 protein in the linear range of 1.0-200 ng/mL, respectively. Clinical study has been conducted with nasopharyngeal swabs from 63 patients having alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2) variants, patients without mutation and negative patients. A machine learning model was developed with accuracy of 99.37% for the identification of the SARS-Cov-2 variants under 1 min. With the increasing need for rapid and improved disease diagnosis and monitoring, the PoC platform proved its potential for real time monitoring by providing accurate and fast variant identification without any expertise and pre sample preparation, which is exactly what societies need in this time of pandemic.
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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.