Repeated pancreatic resection for pancreatic metastases from renal cell Carcinoma: A Spanish multicenter study (PANMEKID)

Background and objectives: Recurrent isolated pancreatic metastasis from Renal Cell Carcinoma (RCC) after pancreatic resection is rare. The purpose of our study is to describe a series of cases of relapse of pancreatic metastasis from renal cancer in the pancreatic remnant and its surgical treatment with a repeated pancreatic resection, and to analyse the results of both overall and disease -free survival. Methods: Multicenter retrospective study of patients undergoing pancreatic resection for RCC pancreatic metastases, from January 2010 to May 2020. Patients were grouped into two groups depending on whether they received a single pancreatic resection (SPS) or iterative pancreatic resection. Data on short and long-term outcome after pancreatic resection were collected. Results: The study included 131 pancreatic resections performed in 116 patients. Thus, iterative pancreatic surgery (IPS) was performed in 15 patients. The mean length of time between the first pancreatic surgery and the second was 48.9 months (95 % CI: 22.2-56.9). There were no differences in the rate of postoperative complications. The DFS rates at 1, 3 and 5 years were 86 %, 78 % and 78 % vs 75 %, 50 % and 37 % in the IPS and SPS group respectively (p = 0.179). OS rates at 1, 3, 5 and 7 years were 100 %, 100 %, 100 % and 75 % in the IPS group vs 95 %, 85 %, 80 % and 68 % in the SPS group (p = 0.895). Conclusion: Repeated pancreatic resection in case of relapse of pancreatic metastasis of RCC in the pancreatic remnant is justified, since it achieves OS results similar to those obtained after the first resection

Pancreatic metastases from renal cell carcinoma. Postoperative outcome after surgical treatment in a Spanish multicenter study (PANMEKID)

Background: Renal Cell Carcinoma (RCC) occasionally spreads to the pancreas. The purpose of our study is to evaluate the short and long-term results of a multicenter series in order to determine the effect of surgical treatment on the prognosis of these patients. Methods: Multicenter retrospective study of patients undergoing surgery for RCC pancreatic metastases, from January 2010 to May 2020. Variables related to the primary tumor, demographics, clinical characteristics of metastasis, location in the pancreas, type of pancreatic resection performed and data on short and long-term evolution after pancreatic resection were collected. Results: The study included 116 patients. The mean time between nephrectomy and pancreatic metastases' resection was 87.35 months (ICR: 1.51-332.55). Distal pancreatectomy was the most performed technique employed (50 %). Postoperative morbidity was observed in 60.9 % of cases (Clavien-Dindo greater than IIIa in 14 %). The median follow-up time was 43 months (13-78). Overall survival (OS) rates at 1, 3, and 5 years were 96 %, 88 %, and 83 %, respectively. The disease-free survival (DFS) rate at 1, 3, and 5 years was 73 %, 49 %, and 35 %, respectively. Significant prognostic factors of relapse were a disease free interval of less than 10 years (2.05 [1.13-3.72], p 0.02) and a history of previous extrapancreatic metastasis (2.44 [1.22-4.86], p 0.01). Conclusions: Pancreatic resection if metastatic RCC is found in the pancreas is warranted to achieve higher overall survival and disease-free survival, even if extrapancreatic metastases were previously removed. The existence of intrapancreatic multifocal compromise does not always warrant the performance of a total pancreatectomy in order to improve survival. (C) 2021 The Authors. Published by Elsevier Ltd

Educación y futuro : revista de investigación aplicada y experiencias educativas

Resumen basado en el de la publicaciónTítulo, resumen y palabras clave también en inglésSe propone el paradigma del liderazgo creativo como forma de responder con perspectivas innovadoras a complejos retos de los líderes de las organizaciones educativas del siglo XXI. El liderazgo creativo supone la construcción de conocimiento generado desde dentro de la organización con y desde los profesionales que desarrollan su labor en ella, con la figura del líder como gestor no sólo de recursos, sino también de las personas. Hasta el inicio del nuevo milenio se pedía al líder el desarrollo de las habilidades racionales centradas en el análisis y la toma de decisiones, pero debido a la aparición de nuevas técnicas de gestión, ante los retos que se tienen que afrontar en educación, estas competencias deben completarse con otras de índole más creativa que lleven a diseñar productos y servicios innovadores que impliquen crecimiento, adaptación, exploración, e innovación; y esto sólo es posible si el director educativo, en el ejercicio del liderazgo, se apoya en el talento de su gente.ES

Análisis incluye 138 carteles de licitación de 26 instituciones: Compra de madera aserrada en las instituciones inscritas en el SICOP 2012–2018

Compra de madera aserrada en las instituciones inscritas en el SICOP 2012–201

Machine learning control and modeling -How taming turbulence can be made easy, efficient, fast and fun!

International audienceClosed-loop turbulence control has current and future engineering applications of truly epic proportions, including cars, trains, airplanes, jet noise, air conditioning, medical applications, wind turbines, combustors, and energy systems, i.e., well-known topics in the GDR 2502. A key feature, opportunity and technical challenge is the inherent nonlinearity of the actuation response [1]. For instance, excitation at a given frequency will affect also other frequencies. This frequency cross-talk is not accessible in any linear control framework. Recently, Artificial Intelligence (AI) / Machine Learning (ML) has opened game-changing new avenues [2]: the automated model-free discovery and exploitation of unknown nonlinear actuation mechanisms directly in the plant and the automated reduced-order modeling from these data. In this talk, we review recent successes on these avenues for broadband frequency turbulence with distributed actuators. Methodological advances include (1) a ML response model predicting performance increases by actuation [3]. (2) the cluster-based network model for automated robust identification of coherent-structure dynamics [4], (3) the explorative gradient method for actuation optimization with the convergence rate of a gradient method and an exploration of global minima [6], and, last but not least, (4) a novel fast-learning gradient-enriched machine learning control which optimizes MIMO feedback laws [5]. Thus, we achieve: (1) 31% drag reduction of a turbulent boundary layer with spanwise traveling surface waves [3, 4], (2) 17% drag reduction of a slanted Ahmed body with 5 groups of orientable actuation jets [6], (3) a significant increase of mixing of a turbulent jet with a novel distributed unsteady actuation [7] and (4) an understanding of the coherent structure dynamics. Nan Deng and Guy Cornejo Maceda will elaborate recent advances for the fluidic pinball during in this meeting

Machine learning control and modeling -How taming turbulence can be made easy, efficient, fast and fun!

International audienceClosed-loop turbulence control has current and future engineering applications of truly epic proportions, including cars, trains, airplanes, jet noise, air conditioning, medical applications, wind turbines, combustors, and energy systems, i.e., well-known topics in the GDR 2502. A key feature, opportunity and technical challenge is the inherent nonlinearity of the actuation response [1]. For instance, excitation at a given frequency will affect also other frequencies. This frequency cross-talk is not accessible in any linear control framework. Recently, Artificial Intelligence (AI) / Machine Learning (ML) has opened game-changing new avenues [2]: the automated model-free discovery and exploitation of unknown nonlinear actuation mechanisms directly in the plant and the automated reduced-order modeling from these data. In this talk, we review recent successes on these avenues for broadband frequency turbulence with distributed actuators. Methodological advances include (1) a ML response model predicting performance increases by actuation [3]. (2) the cluster-based network model for automated robust identification of coherent-structure dynamics [4], (3) the explorative gradient method for actuation optimization with the convergence rate of a gradient method and an exploration of global minima [6], and, last but not least, (4) a novel fast-learning gradient-enriched machine learning control which optimizes MIMO feedback laws [5]. Thus, we achieve: (1) 31% drag reduction of a turbulent boundary layer with spanwise traveling surface waves [3, 4], (2) 17% drag reduction of a slanted Ahmed body with 5 groups of orientable actuation jets [6], (3) a significant increase of mixing of a turbulent jet with a novel distributed unsteady actuation [7] and (4) an understanding of the coherent structure dynamics. Nan Deng and Guy Cornejo Maceda will elaborate recent advances for the fluidic pinball during in this meeting