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Personalized 3D-printed kidney models could serve as valuable educational tools for patients undergoing robot-assisted partial nephrectomy (RAPN). These models facilitate patients' understanding of their pathology, surgical procedure, and anatomy. However, the costs associated with 'personalized' printing remain a barrier to their use. This study aims to thoroughly investigate the benefits of using a personalized 3D-printed kidney model as opposed to a generic 3D-printed kidney model as an educational tool for patients and as a communication tool for healthcare professionals. In this prospective single center study, 60 patients undergoing RAPN will be randomized to receive information based on their personalized 3D-printed tumoral kidney model or a generic 3D-printed tumoral kidney model. These models will accompany patients throughout their care pathway, from pre-operative consultations to the post-operative visit. The impact of these models on the management approaches of various healthcare professionals will also be examined. The data will be collected and analyzed using a mixed method, combining interviews (with patients and caregivers), observations during the presentation of the models and questionnaires (understanding of their pathology and the surgical procedure, Health-literacy, satisfaction). Three dimensional kidney models have the potential to play a central role in the preoperative information process and serve as an effective educational tool during the patient's social interactions with relatives and healthcare professionals. This study will evaluate the potential advantages of personalized 3D models of tumoral kidneys compared to their generic counterparts. The PERSONALIZE study was registered on ClinicalTrials.gov (NCT06379698) on the 5th of September 2023.

The announcement of a diagnosis can be a source of anxiety for patients. Managing this anxiety is a major challenge, in terms of quality of life but also for the use of anxiolytic and analgesic therapies. The use of 3D modeling technology in partial nephrectomy surgery has proved its worth as a surgical aid but it could also help patients to manage their own care, by reducing their anxiety and increasing their understanding of the disease and its treatment. We aim to test this hypothesis with a prospective multicenter trial. R3DP-A (Rein 3D - Anxiety) is an unblinded, multicenter, randomized, prospective, superiority-controlled trial. Participants are patients with kidney tumors treated by robot-assisted partial laparoscopic nephrectomy. The 234 patients (78x3 groups) from 6 French centers will undergo a pre-operative consultation dedicated to a personalized explanation of the surgical management and its risks. They will be randomized into three (1:1:1) groups corresponding to three types of support for consultation: use of a virtual 3D model of the kidney and its tumor; a printed 3D model; or the standard information sheet from the French Association of Urology (control group). Several self-questionnaires will be sent by the UroConnect® application and completed at different times during the study. The primary endpoint will be pre-operative anxiety (STAI-state questionnaire completed the day before surgery D-1). Secondary endpoints will be changes in anxiety levels between the pre-operative and post-operative consultations (between inclusion and D15 post-op), changes in health literacy and quality of life (HLSEU-Q16 and EQ-5D-5L questionnaires at inclusion and D15), feelings of understanding of the disease and its treatment at pre-operative period (Wake questionnaire at D-1), and consultation times. We aim to highlight a benefit of using a personalized 3D model on the anxiety level of patients undergoing partial nephrectomy surgery, as well as on their level of understanding of their pathology and its surgical treatment. The use of these models could be incorporated into current practice to improve patient experience throughout care.

This work explores water hydrolysis using magnesium as a decentralized dihydrogen source for off-grid households. A dedicated reactor design enabled on-demand dihydrogen generation, coupled with a Proton Exchange Membrane Fuel Cell (PEMFC) for electricity and heat production. Different energy management strategies were compared, highlighting the limitations of single-purpose approaches and the benefits of converting surplus electricity to heat. The integration of photovoltaic generation further reduced magnesium demand by 30%, thus reducing storage requirements to close to 1565 kg of magnesium powder per year, i.e., a volume of 0.9 m3 to cover the heat and electricity needs of a four-person household. Results demonstrate that combining water hydrolysis with magnesium and renewables provides a feasible and sustainable solution for autonomous energy supply in isolated sites.

This paper focuses on the development of a fully programmable morphological coprocessor for embedded devices. It is a well-known fact that the majority of morphological processing operations are composed of a (potentially large) number of sequential elementary operators. At the same time, the industrial context induces a high demand on robustness and decision liability that makes the application even more demanding. Recent stationary platforms (PC, GPU, clusters) no more represent a computational bottleneck in real-time vision or image processing applications. However, in embedded solutions such applications still hit computational limits. The morphological co-processing unit (MCPU) replies to this demand. It assembles the previously published efficient dilation/erosion units with geodesic units and ALUs to support a larger collection of morphological operations, from a simple dilation to serial filters involving a geodesic reconstruction step. The coprocessor has been integrated into an FPGA platform running a server that is able to respond to client’s requests over the ethernet. The experimental performance of the MCPU measured on a wide set of operations brings as results in orders of magnitude better than another embedded platform, built around an ARM A9 quad-core processor.