The application of orthopedic implants for bone tissue reconstruction and functional restoration is crucial for patients with severe bone fractures and defects. However, the abiotic nature of orthopedic implants allows bacterial adhesion and colonization, leading to the formation of bacterial biofilms on the implant surface. This can result in implant failure and severe complications such as osteomyelitis and septic arthritis. The emergence of antibiotic-resistant bacteria and the limited efficacy of drugs against biofilms have increased the risk of orthopedic implant-associated infections (OIAI), necessitating the development of alternative therapeutics. In this regard, antibacterial hydrogels based on bacteria repelling, contact killing, drug delivery, or external assistance strategies have been extensively investigated for coating orthopedic implants through surface modification, offering a promising approach to target biofilm formation and prevent OIAI. This review provides an overview of recent advancements in the application of antibacterial hydrogel coatings for preventing OIAI by targeting biofilm formation. The topics covered include: (1) the mechanisms underlying OIAI occurrence and the role of biofilms in exacerbating OIAI development; (2) current strategies to impart anti-biofilm properties to hydrogel coatings and the mechanisms involved in treating OIAI. This article aims to summarize the progress in antibacterial hydrogel coatings for OIAI prevention, providing valuable insights and facilitating the development of prognostic markers for the design of effective antibacterial orthopedic implants.

Introduction: The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the excessive use of antibiotics and the development of biofilms have been a growing threat to global public health. Nanoparticles as substitutes for antibiotics were proven to possess substantial abilities for tackling MRB infections via new antimicrobial mechanisms. Particularly, carbon dots (CDs) with unique (bio)physicochemical characteristics have been receiving considerable attention in combating MRB by damaging the bacterial wall, binding to DNA or enzymes, inducing hyperthermia locally, or forming reactive oxygen species. Methods: Herein, how the physicochemical features of various CDs affect their antimicrobial capacity is investigated with the assistance of machine learning (ML) tools. Results: The synthetic conditions and intrinsic properties of CDs from 121 samples are initially gathered to form the raw dataset, with Minimum inhibitory concentration (MIC) being the output. Four classification algorithms (KNN, SVM, RF, and XGBoost) are trained and validated with the input data. It is found that the ensemble learning methods turn out to be the best on our data. Also, ϵ-poly(L-lysine) CDs (PL-CDs) were developed to validate the practical application ability of the well-trained ML models in a laboratory with two ensemble models managing the prediction. Discussion: Thus, our results demonstrate that ML-based high-throughput theoretical calculation could be used to predict and decode the relationship between CD properties and the anti-bacterial effect, accelerating the development of high-performance nanoparticles and potential clinical translation.