Objective: Management of osteoarticular infections (OAI) represents a major public health challenge. To deal with this, the French Ministry of Health created the (CRIOAC) in 2008. CRIOAC functions as a national network of reference centers for OAI, with the aim of coordinating, providing expertise, offering training, and conducting research at the regional level.
View Article and Find Full Text PDFBackground: Brain metastasis invasion pattern (BMIP) is an emerging biomarker associated with recurrence-free and overall survival in patients, and differential response to therapy in preclinical models. Currently, BMIP can only be determined from the histopathological examination of surgical specimens, precluding its use as a biomarker prior to therapy initiation. The aim of this study was to investigate the potential of machine learning (ML) approaches to develop a noninvasive magnetic resonance imaging (MRI)-based biomarker for BMIP determination.
View Article and Find Full Text PDFIntroduction: Management of bone and joint infections (BJI) requires prolonged and high-dose antibiotic therapy to achieve target concentrations in bone tissue. However, these therapies often lead to adverse effects in patients who are frequently fragile, with multiple comorbidities and associated medications. The decision to treat these complex cases is made during a multidisciplinary team meeting at the reference centre for complex osteoarticular infections (CRIOAC).
View Article and Find Full Text PDFThe longevity of polymer-based sealant and jointing products, including elastomers, significantly depends on the level of exposure to sunlight and joint movement. These factors are particularly crucial in the application of polymers in construction due to their susceptibility to degradation under environmental conditions. For instance, diurnal cycles of contraction and dilation, arising from daily temperature fluctuations, impose significant stress on sealants and joints, impacting their durability over time.
View Article and Find Full Text PDFAs cities face a changing climate, buildings will be subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under climate change is essential for maintaining sustainable and resilient communities. To better prepare building simulation climate data with urban effects, a computationally efficient approach is used to generate "urbanized" data, where the city's unique signature is obtained through the dynamic Weather Research and Forecasting model for the Ottawa, Canada region.
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