A fully integrated microdevice for capturing, amplification, and colorimetric detection of foodborne pathogens
Source abstract
Abstract
In the present study, we integrated laboratory functionalities of sample preparation, nucleic acid amplification, and colorimetric detection into a microdevice for the facile foodborne pathogen detection from bulk liquid samples. The developed microdevice consists of micropillars engraved in a microchamber. In order to capture pathogens from the high throughput sample, boronic acid was decorated on the surface of micropillars to exploit the high affinity of this functional group toward cis-diols of moieties constructing the bacterial outer membrane. Using disposable syringes, the surface modification process for the dense distribution of the boronic acid group was readily achieved at room temperature within 3 h. The success of surface modification was verified by the color changes and the fluorescence signals of the remnant alizarin red sodium. Escherichia coli O157:H7 involving green fluorescence protein (E. coli O157:H7 GFP) was used to evaluate the capture capability of the device at three inoculum bacterial concentrations. The temperature in the reaction zone was carefully investigated to ensure appropriate thermal conditions for performing chamber-type polymerase chain reaction. According to the results, the 584-bp sequence of the Shiga toxin gene of the pathogen was successfully amplified within 2 h. The amplicon was then visualized by the brown color of NaBH4-induced silver nanoparticles. Based on the results, it can be concluded that the proposed microdevice is a promising platform for robust detection of the foodborne pathogens in bulk liquid samples and is applicable in food industry, public healthcare, and environmental monitoring.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Biosensors and Analytical Detection · Advanced biosensing and bioanalysis techniques · Advanced Chemical Sensor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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