Hollow prussian blue nanozyme-richened liposome for artificial neural network-assisted multimodal colorimetric-photothermal immunoassay on smartphone

Multi-signal output biosensor technologies based on optical visualization and electrochemical or other sophisticated signal transduction are flourishing. However, sensors with multiple signal outputs still exhibit some limitations, such as the additional requirement for multiple regression equation construction and control of results. Herein, we developed a sensitive cascade of colorimetric-photothermal biosensor models for prognostic management of patients with myocardial infarction with the assistance of an artificial neural network (ANN) normalization process. A cascade enzymatic reaction device based on hollow prussian blue nanoparticles (h-PB NPs), and a portable smartphone-adapted signal visualization platform were integrated into the all-in-one 3D printed assay device. Specifically, liposomes encapsulated with h-PB were confined to the test cell using a classical immunoassay. Based on the peroxidase-like activity of h-PB, the h-PB obtained by the immunization process was further transferred to the TMB-H2O2 system and used as a cascade of signal amplification for sensitive determination of cTnI protein. The target concentration was converted into a measurable temperature signal readout under 808 nm NIR laser excitation, and the absorbance of the TMB (ox-TMB) system at 650 nm was recorded simultaneously as a reference during this process. Interestingly, a parallel 3-layer, 64-neuron ANN learning model was built for bimodal signal processing and regression. Under optimal conditions, the bimodal machine learning-assisted co-immunoassay exhibited an ultra-wide dynamic range of 0.02-20 ng mL-1 and a detection limit of 10.8 pg mL-1. This work creatively presents a theoretical study of machine learning-assisted multimodal biosensors, providing new insights for the development of ultrasensitive non-enzymatic biosensors.