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A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR.
Cavallo, Francesca Romana; Mirza, Khalid Baig; de Mateo, Sara; Miglietta, Luca; Rodriguez-Manzano, Jesus; Nikolic, Konstantin; Toumazou, Christofer.
  • Cavallo FR; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
  • Mirza KB; Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela 769008, India.
  • de Mateo S; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
  • Miglietta L; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
  • Rodriguez-Manzano J; Department of Infectious Disease, Imperial College London, London SW7 2AZ, UK.
  • Nikolic K; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
  • Toumazou C; School of Computing and Engineering, University of West London, London W5 5RF, UK.
Biosensors (Basel) ; 12(7)2022 Jul 19.
Article in English | MEDLINE | ID: covidwho-1963725
ABSTRACT
This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system's modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Point-of-Care Systems / Aptamers, Nucleotide Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: Bios12070537

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Point-of-Care Systems / Aptamers, Nucleotide Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: Bios12070537