Výsledky vyhledávání - "Marco Pascale"

413.11: The Improvement Study: Call for a New International Prospective Non-competitive, Observational Study to Validate And Optimize Kinetic Models to Predict Liver Allograft Failure At 3 and 12 Months After Liver Transplantation

Publikováno v: Transplantation. 106:S416-S417

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d5d71c1fc084def0b474e7755a395698
https://doi.org/10.1097/01.tp.0000887692.86698.cd

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The risk of hepatic artery thrombosis in liver transplantation is correlated with donor characteristics

Autor: Marco Pascale, Giovanni Moschetta, Francesco Frongillo, Giuseppe Bianco, Francesco Giovinazzo, Erida Nure, Salvatore Agnes

Publikováno v: International Journal of Surgery. 100:106469

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b2f2a1987fed827632d2fd6eae4c2db1
https://doi.org/10.1016/j.ijsu.2022.106469

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Cold ischemia time can be predictive of thrombocytopenia after liver transplantation

Autor: Marco Pascale, Giovanni Moschetta, Francesco Frongillo, Giuseppe Bianco, Salvatore Agnes

Publikováno v: International Journal of Surgery. 100:106470

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::99c1a281a6ccb421c2a82f8efba60231
https://doi.org/10.1016/j.ijsu.2022.106470

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Detection of the early graft injury after liver transplantation through tacrolimus blood-bile ratio

Autor: Marco Pascale, Giuseppe Bianco, Lorenzo Ferri, Francesco Giovinazzo, Antonio Liguori, Giuseppe Marrone, Luca Miele, Antonio Grieco, Salvatore Agnes

Publikováno v: International Journal of Surgery. 100:106468

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9833839ab43a5bff4a31fca2d207f4c4
https://doi.org/10.1016/j.ijsu.2022.106468

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Investigating the Behaviour of Radial Basis Function Networks in Regression and Classification of Geospatial Data

Autor: Andrea Guidali, Marco Pascale, Elisabetta Binaghi, Mauro Guglielmin

Publikováno v: Intelligent Data Engineering and Automated Learning – IDEAL 2010 ISBN: 9783642153808
IDEAL

This work investigates learning and generalisation capabilities of Radial Basis Function Networks used to solve function regression and classification tasks in the environmental context. In particular RBFN is applied to solve the problem of snow cove

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2324bc04c130a8b0c835130e3b93146f
https://doi.org/10.1007/978-3-642-15381-5_14

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