Universal Artificial Urinary Biomarker Probe Enabled by an Aptamer-DNAzyme-Nanozyme Construct.

Artificial urinary biomarker probes (AUBPs) have recently emerged as a new class of diagnostic tools. However, they are applicable only to the biomarkers with catalytic activities or high reactivities. Here, we present a general modular design of AUBPs, integrating an aptamer for target recognition, DNAzyme for triggerable signal transduction, and nanozyme as a urinary artificial biomarker.

Auxiliary Electrode Tunes Wet-Electrospun Bundle Stiffness to Modulate Cell Phenotype.

The stiffness of tissue-engineered scaffolds significantly influences cell behaviour and phenotype. However, current approaches to tuning stiffness often introduce unintended variations and compromise topographical consistency. In this study, an innovative wet-electrospinning set-up, incorporating a positively charged auxiliary electrode was developed to fabricate bundles with adjustable stiffness.

Opto-digital molecular analytics.

In contrast to conventional ensemble-average-based methods, opto-digital molecular analytic approaches digitize detection by physically partitioning individual detection events into discrete compartments or directly locating and analyzing the signals from single molecules. The sensitivity can be enhanced by signal amplification reactions, signal enhancement interactions, labelling by strong signal emitters, advanced optics, image processing, and machine learning, while specificity can be improved by designing target-selective probes and profiling molecular dynamics. With the capabilities to attain a limit of detection several orders lower than the conventional methods, reveal intrinsic molecular information, and achieve multiplexed analysis using a small-volume sample, the emerging opto-digital molecular analytics may be revolutionarily instrumental to clinical diagnosis, molecular chemistry and science, drug discovery, and environment monitoring.

Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution.

Determination of nanoparticle size and size distribution is important because these key parameters dictate nanomaterials' properties and applications. Yet, it is only accomplishable using low-throughput electron microscopy. Herein, we incorporate plasmonic-domain-driven feature engineering with machine learning (ML) for accurate and bidirectional prediction of both parameters for complete characterization of nanoparticle ensembles.