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Introduction: Chronic osteomyelitis is a complex clinical condition that is associated with a high recurrence rate. Traditional surgical interventions often face challenges in achieving a balance between thorough debridement and managing resultant bone defects. Radiomics is an emerging technique that extracts quantitative features from medical images to reveal pathological information imperceptible to the naked eye. This study aims to investigate the potential of radiomics in optimizing osteomyelitis diagnosis and surgical treatment.Methods: Magnetic resonance imaging (MRI) scans of 93 suspected osteomyelitis patients were analyzed. Radiomics features were extracted from the original lesion region of interest (ROI) and an expanded ROI delineated by enlarging the original by 5 mm. Feature selection was performed and support vector machine (SVM) models were developed using the two ROI datasets. To assess the diagnostic efficacy of the established models, we conducted receiver operating characteristic (ROC) curve analysis, employing histopathological results as the reference standard. The model’s performance was evaluated by calculating the area under the curve (AUC), sensitivity, specificity, and accuracy. Discrepancies in the ROC between the two models were evaluated using the DeLong method. All statistical analyses were carried out using Python, and a significance threshold of p < 0.05 was employed to determine statistical significance.Results and Discussion: A total of 1,037 radiomics features were extracted from each ROI. The expanded ROI model achieved significantly higher accuracy (0.894 vs. 0.821), sensitivity (0.947 vs. 0.857), specificity (0.842 vs. 0.785) and AUC (0.920 vs. 0.859) than the original ROI model. Key discriminative features included shape metrics and wavelet-filtered texture features. Radiomics analysis of MRI exhibits promising clinical translational potential in enhancing the diagnosis of chronic osteomyelitis by accurately delineating lesions and identifying surgical margins. The inclusion of an expanded ROI that encompasses perilesional tissue significantly improves diagnostic performance compared to solely focusing on the lesions. This study provides clinicians with a more precise and effective tool for diagnosis and surgical decision-making, ultimately leading to improved outcomes in this patient population.

BackgroundPorcine deltacoronavirus (PDCoV), a novel swine enteropathogenic coronavirus, challenges the global swine industry. Currently, there are no approaches preventing swine from PDCoV infection.MethodsA new PDCoV strain named JS2211 was isolated. Next, the dimer receptor binding domain of PDCoV spike protein (RBD-dimer) was expressed using the prokaryotic expression system, and a novel nanoparticle containing RBD-dimer and ferritin (SC-Fe) was constructed using the SpyTag/SpyCatcher system. Finally, the immunoprotection of RBD-Fe nanoparticles was evaluated in mice.ResultsThe novel PDCoV strain was located in the clade of the late Chinese isolate strains and close to the United States strains. The RBD-Fe nanoparticles were successfully established. Immune responses of the homologous prime-boost regime showed that RBD-Fe nanoparticles efficiently elicited specific humoral and cellular immune responses in mice. Notably, high level PDCoV RBD-specific IgG and neutralizing antibody (NA) could be detected, and the histopathological results showed that PDCoV infection was dramatically reduced in mice immunized with RBD-Fe nanoparticles.ConclusionThis study effectively developed a candidate nanoparticle with receptor binding domain of PDCoV spike protein that offers protection against PDCoV infection in mice.