Multiplex
detection of viable foodborne pathogens is critical for
food safety and public health, yet current assays suffer trade-offs
between cost, assay complexity, sensitivities, and the specificity
between live and dead bacteria. We herein developed a sensing method
using artificial intelligence transcoding (SMART) for rapid, sensitive,
and multiplex profiling of foodborne pathogens. The assay utilizes
the programmable polystyrene (PS) microspheres to encode different
pathogens, inducing subsequent visible signals under conventional
microscopy that can be analyzed using a customized, artificial intelligence-computer
vision, which was trained to decode the intrinsic properties of PS
microspheres to reveal the numbers and types of pathogens. Our approach
enabled the rapid and simultaneous detection of multiple bacteria
from egg samples of <102 CFU/mL without DNA amplification
and showed strong consistency with the standard microbiologic and
genotypic methods. We adopted our assay through phage-guided targeting
to enable the discrimination between live and dead bacteria.
An electrical resistance-based particle counter (ERPC) with simple operation and high resolution has proved to be a promising biosensing toolkit, whereas amplification-free ERPC biosensors are incapable of analyzing trace small molecules due to their relatively low sensitivity. In this work, click chemistrymediated particle counting sensing of small-molecule hazards in food samples with high sensitivity was developed. In this strategy, unbound alkyne-functionalized polystyrene microspheres were collected by magnetic separation from the copperion-mediated click reaction between alkyne-functionalized polystyrene microspheres and azido-functionalized magnetic beads, which could be used as signal probes for the readout. This click chemistry-mediated ERPC biosensor converts the detection of targets to the quantification of copper ions or ascorbic acid by performing competitive immunoassay-based coordination chemistry and enzymatic reaction, respectively. The sensitivity of the ERPC biosensor has been improved by an order of magnitude due to the signal amplification effects of click chemistry, coordination adsorption, and enzyme catalysis. Furthermore, because of the efficient separation and enrichment of immunomagnetic beads and the robustness of click chemistry, the interference from food matrixes and immunoassay is effectively reduced, and thus, our strategy is exceedingly suitable for detecting trace targets in complex samples.
Pursuing convenient operations and precise testing have become an urgent requirement in clinical diagnosis, treatment, and prognosis. Label-free detection is desirable for obviating the labeling process while maintaining high sensitivity and efficiency. Here, we used the dual properties of highly selective antibody−antigen recognition and potential signaling of biomolecules to construct a label-free electroosmotic flow-driven microchannel (LF-EMB) biosensor based on an antibody−antigen biorecognition-induced charge quenching theory proposed herein. The LF-EMB consists of a one-step immune-reaction, onebutton portable device, and supporting microfluidic chip, providing a highpowered tool for rapid on-site testing. The LF-EMB quantified interleukin-6 and kanamycin levels down to 1 pg/mL and 5 pg/mL, respectively, with the whole analysis completed within 35 min. The outstanding sensitivity and detection speed of the constructed LF-EMB provide a convenient option for the quantitative detection of inflammatory markers and antibiotics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.