Molecularly Precise, Bright, Photostable, and Biocompatible Cyanine Nanodots as Alternatives to Quantum Dots for Biomedical Applications.

Fluorescent imaging with fluorophores has become a powerful way to explore complex biological systems and visualize nanoparticles for drug delivery. However, it is challenging to develop fluorophores with ideal physical and optical properties. We report a method to synthesize cyanine nanodots with a single-molecule structure, well-defined particle size, customizable fluorescent spectrum, and bright and stable fluorescence. These cyanine nanodots are acquired by the divergent synthesis of cyanine-dye-cored polylysine (PLL) dendrimers. We demonstrated the feasibility of the method by synthesizing cyanine 3 (Cy3), cyanine 5 (Cy5), or cyanine 7 (Cy7) cored single-molecule nanodots up to eight generations with a size of around 11 nm. We show that these cyanine nanodots are capable of multiple biomedical applications, including multicolor cellular tracing and cancer imaging. These cyanine nanodots possess many merits of organic dots and quantum dots that are promising for future application.

Intelligent Occlusion Stabilization Splint with Stress-Sensor System for Bruxism Diagnosis and Treatment.

Bruxism is a masticatory muscle activity characterized by high prevalence, widespread complications, and serious consequences but without specific guidelines for its diagnosis and treatment. Although occlusal force-based biofeedback therapy is proven to be safe, effective, and with few side effects in improving bruxism, its mechanism and key technologies remain unclear. The purpose of this study was to research a real-time, quantitative, intelligent, and precise force-based biofeedback detection device based on artificial intelligence (AI) algorithms for the diagnosis and treatment of bruxism. Stress sensors were integrated and embedded into a resin-based occlusion stabilization splint by using a layering technique (sandwich method). The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm was leveraged for occlusal force data processing and parameter configuration. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent splint. Experiment results showed reasonable parameter metrics for the sensors system and demonstrated the feasibility of the proposed scheme for bruxism treatment. The intelligent occlusion stabilization splint with a stress sensor system is a promising approach to bruxism diagnosis and treatment.