Ultrasensitive detection of tyrosinase with click reaction-combined dark-field imaging platform.

Tyrosinase (TYR) is an essential oxidase that is responsible for the regulation of multiple physiological processes and diseases. Achieving the trace and reliable detection of TYR in complex biological samples is of great significance for the diagnosis of TYR-related diseases, but which faces a great challenge. In this study, we developed an ingenious and powerful method for the ultrasensitive detection of TYR by click reaction-combined dark-field microscopy. This method begins with the formation of cuprous ions (Cu+) based on the reduction of copper ions (Cu2+) by ascorbic acid (AA). Subsequently, the formed Cu+ can catalyze the crosslinking between azide- and alkyne-functionalized gold nanoparticles, causing a significant red-shift in the scattering spectrum. However, AA can chelate with TYR, which inhibits the generation of Cu+ and subsequent click reaction, thus achieving TYR-controlled scattering spectral shift. The proposed sensing platform shows a good linear detection range of 0.01-0.8 U/L with a low detection limit of 0.003 U/L, which is three orders of magnitude lower than the best performance of TYR sensing probes reported to date. Most importantly, the strategy has the ability to reliably and accurately detect TYR in serum sample, suggesting its potential clinical application in diagnosing TYR-related diseases. This visual sensing platform offers promising prospects for future research in enzymatic analysis and biomedical diagnostics.

Holistic Prediction of AuNP Aggregation in Diverse Aqueous Suspensions Based on Machine Vision and Dark-Field Scattering Imaging.

The localized surface-plasmon resonance of the AuNP in aqueous media is extremely sensitive to environmental changes. By measuring the signal of plasmon scattering light, the dark-field microscopic (DFM) imaging technique has been used to monitor the aggregation of AuNPs, which has attracted great attention because of its simplicity, low cost, high sensitivity, and universal applicability. However, it is still challenging to interpret DFM images of AuNP aggregation due to the heterogeneous characteristics of the isolated and discontinuous color distribution. Herein, we introduce machine vision algorithms for the training of DFM images of AuNPs in different saline aqueous media. A visual deep learning framework based on AlexNet is constructed for studying the aggregation patterns of AuNPs in aqueous suspensions, which allows for rapid and accurate identification of the aggregation extent of AuNPs, with a prediction accuracy higher than 0.96. With the aid of machine learning analysis, we further demonstrate the prediction ability of various aggregation phenomena induced by both cation species and the concentration of the external saline solution. Our results suggest the great potential of machine vision frameworks in the accurate recognition of subtle pattern changes in DFM images, which can help researchers build predictive analytics based on DFM imaging data.