Nanotechnology is improving the development of wearable and implantable biosensors. In 2025, researchers at Caltech developed a new method for inkjet-printing nanoparticles that enable mass production of these devices. The core-shell cubic nanoparticles have dual functions: they facilitate electrochemical signal transduction and can bind to target molecules in biological fluids.1
The nanoparticle core consists of a Prussian blue analog (PBA), a redox-active material capable of sending electrochemical signals. The shell is made of molecularly imprinted polymer (MIP) nickel hexa-cyanoferrate (NiHCF), which allows precise molecular recognition. These printable nanoparticles could enable the large-scale production of biosensors to monitor critical biomarkers.
To test their functionality, researchers developed an inkjet-printed biosensor designed to monitor AA, CPK, and Trp levels. The sensor exhibited high reproducibility and accuracy, maintaining mechanical flexibility and stability even after 1,200 bending cycles. This adaptability allows manufacturers to create biosensors in various shapes for different applications.
Additionally, the biosensor was used to track liver cancer treatment drugs in biological fluids, helping monitor how the body absorbs and processes them. The integration of this nanomaterial made the biosensor stronger, more stable, and more precise, improving targeted healthcare monitoring.2
Single-Cell Profiling (SCP) of Nanocarriers: AI-Powered Monitoring Technology
Nanocarriers are widely used in drug delivery, but tracking their distribution at the cellular level has remained a challenge. German researchers have now developed Single-Cell Profiling (SCP) of Nanocarriers, a method that precisely monitors and detects nanocarriers within individual cells.3
SCP enables high-resolution mapping of nanocarriers at the cellular level, allowing researchers to quantify their bio-distribution with exceptional precision and sensitivity.
The team applied a deep learning (DL) approach to analyze large-scale image datasets, optimizing nanocarrier imaging for more accurate quantification. The method was demonstrated in a mouse model, providing new insights into nanomedicine at the cellular level.
The AI-based nanotechnology framework can segment cells based on different parameters like shape and size, which was achieved by optimizing the DL algorithm via training on high-quality 3D data.
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