Injectable recombinant collagen hydrogels (RCHs) are crucial in biomedical applications. Culture conditions play an important role in the preparation of hydrogels. However, determining the characteristics of hydrogels under certain conditions and determining the optimal conditions swiftly still remain challenging tasks. In this study, a machine learning approach was introduced to explore the correlation between hydrogel characteristics and culture conditions and determine the optimal culture conditions. The study focused on four key factors as independent variables: initial substrate concentration, reaction temperature, pH level, and reaction time, while the dependent variable was the elastic modulus of the hydrogels. To analyze the impact of these factors on the elastic modulus, four mathematical models were employed, including multiple linear regression (ML), decision tree (DT), support vector machine (SVM), and neural network (NN). The theoretical outputs of NN were closest to the actual values. Therefore, NN proved to be the most suitable model. Subsequently, the optimal culture conditions were identified as a substrate concentration of 15% (W/V), a reaction temperature of 4 °C, a pH of 7.0, and a reaction time of 12 h. The hydrogels prepared under these specific conditions exhibited a predicted elastic modulus of 15,340 Pa, approaching that of natural elastic cartilage.

Antimicrobial delivery systems have undergone extensive development, yet conventional carriers still exhibit limitations such as low loading capacity, inadequate controlled release mechanisms, and cytotoxicity. Recent studies have increasingly demonstrated the potential of Hydrogel Microspheres (HMSs) for antimicrobial delivery. These microspheres exhibit small dimensions, high drug-loading capacity, and the ability to achieve deep-targeted delivery, complemented by adjustable physicochemical properties and biocompatibility that create favorable conditions for antimicrobial transportation. This review systematically examines HMS preparation strategies, characteristic properties, transported antimicrobials, and therapeutic applications. Particular emphasis is placed on critical preparation parameters governing HMS performance, especially those influencing drug delivery dynamics. We conclude by addressing current challenges and proposing actionable strategies for material optimization and clinical translation. This work aims to advance HMS-based antimicrobial delivery systems for more effective infection control.