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Mega scientific conferences increasingly suffer from the need for short and poster presentations without discussion. An alternative is to organize workshops in hotels large enough to accommodate all participants. This significantly increases the opportunities for constructive discussion during breakfasts, lunches, dinners and long evenings that can bring together experts of scientific and clinical sub-specialties and young fellows. Time for groups' discussions and new collaborations are increased so as the job opportunities for the young researchers. The Padova Muscle Days have offered in the previous thirty-five years these opportunities, which have matured into innovative and multidisciplinary results to the point that it came naturally to underline it with a neologism now included in the title of the 2025 event: “Mobility Medicine”, a discipline not yet officially recognised, that makes explicit the call for rejoining knowledges dispersed in sub-specialisations. The included program of the Padua Days on Muscle and Mobility Medicine 2025 (2025Pdm3) will be hosted at the Hotel Petrarca in Euganean Thermae (Padua, Italy) from 25 to 29 March 2025. It further testifies by listing unique Sessions that it is possible to organize valid countermeasures to the inevitable tendencies towards hyperspecialization that the explosive increase in scientific progress brings with it. Furthermore, the European Journal of Translational Myology and Mobility Medicine (Ejtm3) will accept typescripts on results presented at the 2025Pdm3, together with the Special Section: New Trends in Musculoskeletal Imaging of the MDPI (Basel) Journal Diagnostics, because diagnosis is essential to prevent, manage and follow-up not only neuro-metabolic-muscular disorders, but the unavoidable physiologicical decay of performances in early and late aging. Hoping many others share our dreams, we look forward to meeting you at 2025Pdm3 conference.

Background and Clinical Significance: Sarcopenia, characterized by muscle loss and fat infiltration, poses a significant health burden for aging populations. Quantitative Color 2D and 3D radiodensitometry provides a powerful tool to monitor muscle quality and quantity through CT imaging. This study assessed the impact of a ten-year-long home-bed gym exercise intervention on muscle quality in an elderly subject using CT-derived radiodensitometric analysis. The study involved two comparative analyses: Study A, which compared knee-to-ankle CT scans of the subject between 2013 and 2023; and Study B, which compared the subject’s 2023 thigh CT scan with a cohort of 2500 elderly Icelandic individuals from the AGES-Reykjavik study. Case Presentation: A 70-year-old male began a home-based Full-Body In-Bed Gym exercise program in 2013. Quantitative muscle volume and radiodensity measurements were performed using CT at baseline and after ten years. Results: Study A shows significant improvements in muscle volume observed in the knee-to-ankle region, while a slower decline in radiodensity was noted, indicating substantial preservation of muscle quality despite the expected decay of ten-year aging. For instance, muscle volume increased by 15% in the left Soleus muscle and by 6% in the right Soleus muscle, while the average radiodensity decreased by 12–17 HU. The subject’s thigh muscle quality at 80-years-old is above the AGES-Reykjavik’s cohort average, with reduced fat infiltration. Conclusions: Long-term home Full-Body In-Bed Gym, a low-impact exercise, can mitigate aging sarcopenia, as evidenced by improved tissue radiodensity and muscle mass substantial preservation. This suggests potential applications in personalized healthcare strategies to enhance muscle preservation among aging populations.

Articular cartilage morphology and composition are essential factors in joint biomechanics, and their alteration is a crucial aspect of osteoarthritis (OA), a prevalent disease that causes pain and functional loss. This research focuses on developing patient-specific synthetic cartilage using innovative Digital Anatomy polymers. The objectives include investigating the morphology, characterizing the mechanical properties, and replicating the architecture of natural cartilage. This approach offers potential alternatives to traditional manufacturing methods and reduces the need for expensive in vivo experiments. Finite Element Analysis (FEA) validates a novel patient-specific measurement setup. It provides insights into the role of morphology in the distribution of stress and strain within cartilage. CAD design is also utilized to create standardized fiber-reinforced samples that mimic the layered micro-architecture of natural cartilage, allowing for the study of their contribution to the overall mechanical properties. The results demonstrate that 3D-printed polymers can effectively replicate the elastic properties of cartilage. The proposed patient-specific simulator produces reliable results, which have been validated through FEM analysis. While the recreated microstructure closely resembles biological cartilage samples, the elastic properties are slightly underestimated. In conclusion, designing an in silico knee joint is a feasible approach that offers numerous advantages for further development. The Young’s modulus values of our synthetic cartilage modules range from 2.43 MPa to 7.24 MPa, within the range reported in the literature. Moreover, Young´s modulus at the micro level shows the differences between surface 1.74 MPa and internal substrate 1.83 MPa depending on the fiber orientation. Finally, our model proves to be mechanically and morphologically accurate at both the macro and micro levels.

Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features’ trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.