Early Predicting Osteogenic Differentiation of Mesenchymal Stem Cells Based on Deep Learning Within One Day.

Osteogenic differentiation of mesenchymal stem cells (MSCs) is proposed to be critical for bone tissue engineering and regenerative medicine. However, the current approach for evaluating osteogenic differentiation mainly involves immunohistochemical staining of specific markers which often can be detected at day 5-7 of osteogenic inducing. Deep learning (DL) is a significant technology for realizing artificial intelligence (AI). Computer vision, a branch of AI, has been proved to achieve high-precision image recognition using convolutional neural networks (CNNs). Our goal was to train CNNs to quantitatively measure the osteogenic differentiation of MSCs. To this end, bright-field images of MSCs during early osteogenic differentiation (day 0, 1, 3, 5, and 7) were captured using a simple optical phase contrast microscope to train CNNs. The results showed that the CNNs could be trained to recognize undifferentiated cells and differentiating cells with an accuracy of 0.961 on the independent test set. In addition, we found that CNNs successfully distinguished differentiated cells at a very early stage (only 1 day). Further analysis showed that overall morphological features of MSCs were the main basis for the CNN classification. In conclusion, MSCs differentiation detection can be achieved early and accurately through simple bright-field images and DL networks, which may also provide a potential and novel method for the field of cell detection in the near future.

Aggregation-induced Emission Properties of Triphenylamine Chalcone Compounds.

Two triphenylamine chalcone derivatives 1 and 2 were synthesized through the Vilsmeier-Haack reaction and Claisen-Schmidt condensation reaction. Through ultraviolet absorption spectroscopy and fluorescence emission spectroscopy experiments, it was confirmed that these two compounds exhibited good aggregation-induced emission (AIE) behavior in ethanol/water mixtures. The solvent effect test showed with the increase of the orientation polarizability of the solvent, the Stokes shift in the solvent of compound 1 and compound 2 shows a linear change trend. Through solid state fluorescence test and universal density function theory (DFT), the existence of π-π stacking interaction in the solid state of the compound has been studied, resulting in weak fluorescence emission. pH has no effect on the fluorescence intensity of the aggregate state of excited state intramolecular proton transfer (ESIPT) molecules in an acidic environment, but greatly weakens its fluorescence intensity in an alkaline environment. Cyclic voltammetry (CV) test shows that compound 1 was more prone to oxidation reaction than compound 2. The results of thermal stability test show that the thermal stability of compound 1 was better than that of compound 2, indicating that triphenylamine chalcone derivatives can improve the thermal stability of compounds by increasing the number of branches.