Magnetomotive Ultrasound Shear Wave Elastography (MMUS-SWE): A Validation Study From Simulations to Experiments.

Ultrasound elastography is a functional imaging method that enables the measurement of soft tissue elasticity, which is associated with the pathological process of many diseases. However, the measurement area of the conventional elastography method is subjectively selected. Inspired by the targeted imaging technology, we propose a method of magnetomotive ultrasound shear wave elastography (MMUS-SWE). This method utilizes the magnetic force between the magnetic nanoparticles (MNPs) and the external magnetic field to generate shear waves. Then, it can detect the distribution of MNPs and the elasticity of the tissue around the MNPs. As MNPs have been widely used for targeted labeling, the strategy to induce local vibration by MNPs will be more specific than that of the conventional SWE. In this study, the theoretical feasibility was verified by the finite element simulation model. Then, an experimental system was built, and the experimental feasibility of the method was demonstrated through phantom experiments, in vitro tissue experiments, and in vivo experiments. The results show that the distribution of the MNPs and the elastic information of tissues surrounding the MNPs can be detected simultaneously. This technology is expected to realize targeted elasticity measurement based on the MNPs and has potential applications for disease diagnosis.

Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging.

Due to the excellent biocompatible physicochemical performance, luminogens with aggregation-induced emission (AIEgens) characteristics have played a significant role in biomedical fluorescence imaging recently. However, screening AIEgens for special applications takes a lot of time and efforts by using conventional chemical synthesis route. Fortunately, artificial intelligence techniques that could predict the properties of AIEgen molecules would be helpful and valuable for novel AIEgens design and synthesis. In this work, we applied machine learning (ML) techniques to screen AIEgens with expected excitation and emission wavelength for biomedical deep fluorescence imaging. First, a database of various AIEgens collected from the literature was established. Then, by extracting key features using molecular descriptors and training various state-of-the-art ML models, a multi-modal molecular descriptors strategy has been proposed to extract the structure-property relationships of AIEgens and predict molecular absorption and emission wavelength peaks. Compared to the first principles calculations, the proposed strategy provided greater accuracy at a lower computational cost. Finally, three newly predicted AIEgens with desired absorption and emission wavelength peaks were synthesized successfully and applied for cellular fluorescence imaging and deep penetration imaging. All the results were consistent successfully with our expectations, which demonstrated the above ML has a great potential for screening AIEgens with suitable wavelengths, which could boost the design and development of novel organic fluorescent materials.