Aptamer-Based Point of Care Testing Schemes

Murtaza, Ghulam; Rizvi, Aysha Sarfraz; Qu, Feng

doi:10.1007/978-981-33-4838-7_3

Cited by 2 publications

(1 citation statement)

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“…Aptamers are in many ways more suited to POC platforms than antibodies, as their affinity and sensitivity can be extremely high, and they are more stable and robust to ambient conditions than antibodies. Since aptamers can be labelled with a fluorophore, they have been mainly used as a direct replacement of antibodies in ELISAs and biosensors [ 24 ]. Aptamer-based colourimetric and rapid lateral flow assays are routinely used in low-resource settings for food safety [ 25 ], drug abuse testing, pregnancy tests and cancer biomarkers [ 26 ]; however, they provide qualitative monitoring and are not suitable for highly sensitive applications since they rely on visual quantification.…”

Section: Introductionmentioning

confidence: 99%

A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR

et al. 2022

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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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Section: Introductionmentioning

confidence: 99%