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Stem cell-based therapy plays a significant role in the repair of bone defects. However, traditional stem cell transplantation strategies in bone tissue engineering are characterized by low survival rates and unstable treatment outcomes. In this study, we propose a timely delivery strategy for inflammatory changes in the setting of bone injury to improve the survival rate of transplanted cells and bone repair. The results of cell tracing in vivo showed that this strategy could effectively improve the survival rate of low-dose exogenous transplanted cells in bone defect areas, and CD31 immunofluorescence and histological sections suggested that this strategy effectively promoted vascularization and new bone formation in the calvarial defect area. Subsequently, we analyzed the mechanism of action of the “Two-step” strategy from the perspective of inflammatory microenvironment regulation, and the results suggested that the first batch transplanted stem cells caused localized and transient increases in tissue apoptosis levels and inflammatory factors, and recruited macrophage chemotaxis, and the second batch of cells may promote pro-inflammatory - anti-inflammatory transformation of the tissue. Finally, mRNA sequencing results suggest that the first batch cells in the “Two-step” strategy are important initiators in bone repair, which not only actively regulate the immune microenvironment at the bone defect, but also guide richer cellular activity and more positive biochemical responses. Therefore, the “Two-step” strategy leads to efficient inflammatory environment regulation and superior bone repair effects, which may provide an alternative option for the treatment of bone defects in the future.

Molecular generation (MG) via machine learning (ML) has speeded drug structural optimization, especially for targets with a large amount of reported bioactivity data. However, molecular generation for structural optimization is often powerless for new targets. DNA-encoded library (DEL) can generate systematic, target-specific activity data, including novel targets with few or unknown activity data. Therefore, this study aims to overcome the limitation of molecular generation in the structural optimization for the new target. Firstly, we generated molecules using the structure-affinity data (2.96 million samples) for 3C-like protease (3CLpro) from our own-built DEL platform to get rid of using public databases (e.g., CHEMBL and ZINC). Subsequently, to analyze the effect of transfer learning on the positive rate of the molecule generation model, molecular docking and affinity model based on DEL data were applied to explore the enhanced impact of transfer learning on molecule generation. In addition, the generated molecules are subjected to multiple filtering, including physicochemical properties, drug-like properties, and pharmacophore evaluation, molecular docking to determine the molecules for further study and verified by molecular dynamics simulation.

Drug discovery has entered a new period of vigorous development with advanced technologies such as DNA-encoded library (DEL) and artificial intelligence (AI). The previous DEL-AI combination has been successfully applied in the drug discovery of classical kinase and receptor targets mainly based on the known scaffold. So far, there is no report of the DEL-AI combination on inhibitors targeting protein-protein interaction, including those undruggable targets with few or unknown active scaffolds. Here, we applied DEL technology on the T cell immunoglobulin and ITIM domain (TIGIT) target, resulting in the unique hit compound 1 (IC50 = 20.7 μM). Based on the screening data from DEL and hit derivatives a1-a34, a machine learning (ML) modeling process was established to address the challenge of poor sample distribution uniformity, which is also frequently encountered in DEL screening on new targets. In the end, the established ML model achieved a satisfactory hit rate of about 75% for derivatives in a high-scored area.

The zebrafish has become an established model organism for the study of hearing and balance systems in the past two decades. The classical approach to examine hair cells is to use dye to conduct selective staining, which shows the number and morphology of hair cells but does not reveal their function. Startle response is a behavior closely related to the auditory function of hair cells; therefore it can be used to measure the function of hair cells. In this study, we developed a device to measure the startle response of zebrafish larvae. By applying various levels of stimulus, it showed that the system can discern a 10 dB difference. The hair cell in zebrafish can regenerate after damage due to noise exposure or drug treatment. With this device, we measured the startle response of zebrafish larvae during and after drug treatment. The results show a similar trend to the classical hair cell staining method. The startle response was reduced with drug treatment and recovered after removal of the drug. Together it demonstrated the capability of this behavioral assay in evaluating the hair cell functions of fish larvae and its potential as a high-throughput screening tool for auditory-related gene and drug discovery.