State of art of mobility medicine: some more abstracts and evidence that the success of Pdm3 is based on extra-session relationships.

Scientific conferences increasingly suffer from the need for short presentations in which speakers like to dwell on the details of their work. A mitigating factor is to encourage discussion and planning of collaborations by organizing small meetings in a hotel large enough to host all attendees. This extends discussions' opportunities during morning breakfasts, lunches, dinners and long evenings together. Even if the vast majority of participants will not stay for the entire duration of the Conference, the possibilities for specialists to interact with specialists who are even very distant in terms of knowledge increase enormously. In any case, the results in terms of new job opportunities for young participants outweigh the costs for the organizers. Thirty years of Padova Muscle Days offer many examples, but the authors of this report on the state of the art of Mobility Medicine testify that this also happened in the 2024 Five Days of Muscle and Mobility Medicine (2024Pdm3) hosted at the Hotel Petrarca, Thermae of Euganea Hills and Padua, Italy which is in fact a valid countermeasure to the inevitable tendencies towards hyperspecialization that the explosive increase in scientific progress brings with it.

The role of bone mineral density and cartilage volume to predict knee cartilage degeneration.

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.