Lateral flow assay ruler for quantitative and rapid point-of-care testing

Li, Zhao; Chen, Hui; Wang, Ping

doi:10.1039/c9an00374f

Cited by 39 publications

(16 citation statements)

References 33 publications

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“…This rapid result of qualitative tests helps make immediate decisions. However, qualitative analysis provides subjective judgement and may give a wrong interpretation [16]. Hence, quantitative methods are needed where the results are not clear with qualitative analysis.…”

Section: Introductionmentioning

confidence: 99%

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Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone

Foysal

Seo

Kim

et al. 2019

Sensors

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Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin—10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time.

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Section: Introductionmentioning

confidence: 99%

“…Hence, quantitative methods are needed where the results are not clear with qualitative analysis. Quantitative analysis of LFA is shown to facilitate point-of-care testing compared to qualitative analysis [16,17,18,19]. Quantitative approaches require a method that can readily deliver quantitative measurements in a fast and accurate way.…”

Section: Introductionmentioning

confidence: 99%

Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone

Foysal

Seo

Kim

et al. 2019

Sensors

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“…Li et el. introduced microfluidic devices that employ a lateral flow assay for quantitative and rapid point-of-caring tests ( Figure 5 b) [ 85 ]. This device used antibody-conjugated platinum nanoparticles to capture prostate-specific antigens.…”

Section: Optical Analysis Platform Of Ctcs and Ccscs For Phenotypimentioning

confidence: 99%

Microfluidic Chip-Based Cancer Diagnosis and Prediction of Relapse by Detecting Circulating Tumor Cells and Circulating Cancer Stem Cells

Cho

Choi

Lim

et al. 2021

Cancers

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Detecting circulating tumor cells (CTCs) has been considered one of the best biomarkers in liquid biopsy for early diagnosis and prognosis monitoring in cancer. A major challenge of using CTCs is detecting extremely low-concentrated targets in the presence of high noise factors such as serum and hematopoietic cells. This review provides a selective overview of the recent progress in the design of microfluidic devices with optical sensing tools and their application in the detection and analysis of CTCs and their small malignant subset, circulating cancer stem cells (CCSCs). Moreover, discussion of novel strategies to analyze the differentiation of circulating cancer stem cells will contribute to an understanding of metastatic cancer, which can help clinicians to make a better assessment. We believe that the topic discussed in this review can provide brief guideline for the development of microfluidic-based optical biosensors in cancer prognosis monitoring and clinical applications.

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“…This barcode‐style LFA system has multiple T‐lines on the strip and the concentration of analyte is correlated to the number of positive T‐lines. An “LFA ruler” has also been developed recently for quantitative detection of LFA results (Li, Chen, & Wang, 2019). These approaches should also be evaluated for quantitative meat species identification.…”

Section: Limitations and Future Perspectivementioning

confidence: 99%

Rapid point‐of‐care testing methods/devices for meat species identification: A review

Kumar

Narsaiah

2020

Comp Rev Food Sci Food Safe

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The authentication of animal species is an important issue due to an increasing trend of adulteration and mislabeling of animal species in processed meat products. Polymerase chain reaction is the most sensitive and specific technique for nucleic acid‐based animal species detection. However, it is a time‐consuming technique that requires costly thermocyclers and sophisticated labs. In recent times, there is a need of on‐site detection by point‐of‐care (POC) testing methods and devices under low‐resource settings. These POC devices must be affordable, sensitive, specific, user‐friendly, rapid and robust, equipment free, and delivered to the end users. POC devices should also confirm the concept of micro total analysis system. This review discusses POC testing methods and devices that have been developed for meat species identification. Recent developments in lateral flow assay‐based devices for the identification of animal species in meat products are also reviewed. Advancements in increasing the efficiency of lateral flow detection are also discussed.

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Lateral flow assay ruler for quantitative and rapid point-of-care testing

Abstract: A microfluidic chip for quantitative and rapid readout of LFA results.

Cited by 39 publications

References 33 publications

Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone

Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone

Microfluidic Chip-Based Cancer Diagnosis and Prediction of Relapse by Detecting Circulating Tumor Cells and Circulating Cancer Stem Cells

Rapid point‐of‐care testing methods/devices for meat species identification: A review

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