Electrochemiluminescence Detection of <i>Escherichia coli</i> O157:H7 Based on a Novel Polydopamine Surface Imprinted Polymer Biosensor

In this paper, a facilely prepared electrochemiluminescence (ECL) biosensor was developed for <i>Escherichia coli</i> O157:H7 quantitative detection based on a polydopamine (PDA) surface imprinted polymer (SIP) and nitrogen-doped graphene quantum dots (N-GQDs). N-GQDs with a high quantum yield of 43.2% were synthesized. The uniform PDA SIP film for <i>E. coli</i> O157:H7 was established successfully with a facile route. The dopamine and target bacteria were electropolymerized directly on the electrode. After removal of the <i>E. coli</i> O157:H7 template, the established PDA SIP can selectively recognize <i>E. coli</i> O157:H7. Accordingly, <i>E. coli</i> O157:H7 polyclonal antibody (pAb) was labeled with N-GQDs. The bioconjugation of SIP–<i>E. coli</i> O157:H7/pAb-N-GQDs can generate intensive ECL irradiation with K₂S₂O₈. As a result, <i>E. coli</i> O157:H7 was detected with the ECL sensing system. Under optimal conditions, the linear relationships between the ECL intensity and <i>E. coli</i> O157:H7 concentration were obtained from 10¹ colony-forming units (CFU) mL^–1 to 10⁷ CFU mL^–1 with a limit of detection of 8 CFU mL^–1. The biosensor based on this SIP film was applied in water sample detection successfully. The N-GQD-based ECL analytical method for <i>E. coli</i> O157:H7 was reported for the first time. The sensing system had high selectivity to the target analyte, provided new opportunities for use, and increased the rate of disease diagnosis and treatment and the prevention of pathogens

Liver-Targeted Near-Infrared Fluorescence/Photoacoustic Dual-Modal Probe for Real-Time Imaging of <i>In Situ</i> Hepatic Inflammation

Early diagnosis of hepatic inflammation is the key to timely treatment and avoid the worsening of liver inflammation. Near-infrared fluorescence (NIRF) probes have high sensitivity but low spatial resolution in lesion imaging, while photoacoustic (PA) imaging has good spatial location information. Therefore, the development of a NIRF/PA dual-modal probe integrated with high sensitivity and spatial location feedback can achieve an accurate early diagnosis of hepatic inflammation. Here, we report an activatable NIRF/PA dual-modal probe (hCy-Tf-CA) for the detection of the superoxide anion (O2·–) in early hepatic inflammation. hCy-Tf-CA showed high selectivity and sensitivity for detecting O2·– fluctuation in vitro. More importantly, by introducing hepatocyte-targeting cholic acid (CA), the probe successfully achieved accurate in situ imaging of acute inflammatory liver injury (AILI) and autoimmune hepatitis (AIH) in vivo. The introduced CA not only promotes the hepatic targeting accumulation of probes but also improves the performance of low background dual-modal imaging in vivo. Therefore, hCy-Tf-CA provides an effective strategy for significantly improving in situ imaging performance and holds great potential for early, sensitive, and accurate diagnosis of hepatic inflammation

Machine Learning Color Feature Analysis of a High Throughput Nanoparticle Conjugate Sensing Assay

Plasmonic nanoparticles are finding applications within the single molecule sensing field in a “dimer” format, where interaction of the target with hairpin DNA causes a decrease in the interparticle distance, leading to a localized surface plasmon resonance shift. While this shift may be detected using spectroscopy, achieving statistical relevance requires the measurement of thousands of nanoparticle dimers and the timescales required for spectroscopic analysis are incompatible with point-of-care devices. However, using dark-field imaging of the dimer structures, simultaneous digital analysis of the plasmonic resonance shift after target interaction of thousands of dimer structures may be achieved in minutes. The main challenge of this digital analysis on the single-molecule scale was the occurrence of false signals caused by non-specifically bound clusters of nanoparticles. This effect may be reduced by digitally separating dimers from other nanoconjugate types. Variation in image intensity was observed to have a discernible impact on the color analysis of the nanoconjugate constructs and thus the accuracy of the digital separation. Color spaces wherein intensity may be uncoupled from the color information (hue, saturation, and value (HSV) and luminance, a* vector, and b* vector (LAB) were contrasted to a color space which cannot uncouple intensity (RGB) to train a classifier algorithm. Each classifier algorithm was validated to determine which color space produced the most accurate digital separation of the nanoconjugate types. The LAB-based learning classifier demonstrated the highest accuracy for digitally separating nanoparticles. Using this classifier, nanoparticle conjugates were monitored for their plasmonic color shift after interaction with a synthetic RNA target, resulting in a platform with a highly accurate yes/no response with a true positive rate of 88% and a true negative rate of 100%. The sensor response of tested single stranded RNA (ssRNA) samples was well above control responses for target concentrations in the range of 10 aM–1 pM