Distance-based microfluidic quantitative detection methods for point-of-care testing.

Lab Chip

MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Collaborative Innovation Center of Chemistry for Energy Materials, Key Laboratory for Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.

Published: April 2016

Equipment-free devices with quantitative readout are of great significance to point-of-care testing (POCT), which provides real-time readout to users and is especially important in low-resource settings. Among various equipment-free approaches, distance-based visual quantitative detection methods rely on reading the visual signal length for corresponding target concentrations, thus eliminating the need for sophisticated instruments. The distance-based methods are low-cost, user-friendly and can be integrated into portable analytical devices. Moreover, such methods enable quantitative detection of various targets by the naked eye. In this review, we first introduce the concept and history of distance-based visual quantitative detection methods. Then, we summarize the main methods for translation of molecular signals to distance-based readout and discuss different microfluidic platforms (glass, PDMS, paper and thread) in terms of applications in biomedical diagnostics, food safety monitoring, and environmental analysis. Finally, the potential and future perspectives are discussed.

Download full-text PDF

Source
http://dx.doi.org/10.1039/c5lc01562fDOI Listing

Publication Analysis

Top Keywords

quantitative detection
16
detection methods
12
point-of-care testing
8
distance-based visual
8
visual quantitative
8
methods
6
distance-based
5
quantitative
5
distance-based microfluidic
4
microfluidic quantitative
4

Similar Publications

Understanding the genetic basis of drought tolerance in safflower (Carthamus tinctorius L.) is essential for developing resilient varieties. In this study, we performed a genome-wide association study (GWAS) using DArTseq markers to identify marker-trait associations (MTAs) linked to drought tolerance across 90 globally diverse safflower genotypes.

View Article and Find Full Text PDF

Despite numerous studies investigating the correlation between the serum uric acid and high-density lipoprotein cholesterol ratio (UHR) and fatty liver disease, the evidence for the dose-response relationship between UHR and liver fat content (LFC) remains uncertain. This study employs quantitative computed tomography (CT) to quantify LFC and aims to investigate the correlation and dose-response relationship between UHR levels and LFC in Chinese adults. Based on the health check-up data from 2021 at Henan Provincial People's Hospital, China, the objective of this cross-sectional study was to investigate the association between UHR levels and LFC among individuals of different genders.

View Article and Find Full Text PDF

Exhaled breath metabolites reveal postmenopausal gut-bone cross-talk and non-invasive markers for osteoporosis.

Commun Med (Lond)

December 2024

Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Rostock University Medical Center, Rostock, Germany.

Background: Menopause driven decline in estrogen exposes women to risk of osteoporosis. Detection of early onset and silent progression are keys to prevent fractures and associated burdens.

Methods: In a discovery cohort of 120 postmenopausal women, we combined repeated quantitative pulse-echo ultrasonography of bone, assessment of grip strength and serum bone markers with mass-spectrometric analysis of exhaled metabolites to find breath volatile markers and quantitative cutoff levels for osteoporosis.

View Article and Find Full Text PDF

Classification and regression problems can be challenging when the relevant input features are diluted in noisy datasets, in particular when the sample size is limited. Traditional Feature Selection (FS) methods address this issue by relying on some assumptions such as the linear or additive relationship between features. Recently, a proliferation of Deep Learning (DL) models has emerged to tackle both FS and prediction at the same time, allowing non-linear modeling of the selected features.

View Article and Find Full Text PDF

The Restriction Spectrum Imaging restriction score (RSIrs) has been shown to improve the accuracy for diagnosis of clinically significant prostate cancer (csPCa) compared to standard DWI. Both diffusion and T properties of prostate tissue contribute to the signal measured in DWI, and studies have demonstrated that each may be valuable for distinguishing csPCa from benign tissue. The purpose of this retrospective study was to (1) determine whether prostate T varies across RSI compartments and in the presence of csPCa, and (2) evaluate whether csPCa detection with RSIrs is improved by acquiring multiple scans at different TEs to measure compartmental T (cT).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!