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Risk factors and prediction of acute kidney injury after liver transplantation:Logistic regression and artificial neural network approaches

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World Journal of Hepatology 2022年第3期14卷 570-582页

作者： luis Cesar Bredt luis alberto batista peres Michel Risso Leandro Cavalcanti de Albuquerque Leite Barros Department of Surgical Oncology and Hepatobilary Surgery UnioesteCascavel 85819-110ParanáBrazil Department of Nephrology UnioesteCascavel 85819-110ParanáBrazil Department of Internal Medicine Assis Gurgacz UniversityCascavel 85000ParanáBrazil Department of Hepatobiliary Surgery UnioesteCascavel 85819-110ParanáBrazil

BACKGROUND Acute kidney injury(AKI)has serious consequences on the prognosis of patients undergoing liver ***,artificial neural network(ANN)was reported to have better predictive ability than the classical logistic regression(LR)for this postoperative *** To identify the risk factors of AKI after deceased-donor liver transplantation(DDLT)and compare the prediction performance of ANN with that of LR for this *** Adult patients with no evidence of end-stage kidney dysfunction(KD)who underwent the first DDLT according to model for end-stage liver disease(MELD)score allocation system was *** was defined according to the International Club of Ascites criteria,and potential predictors of postoperative AKI were identified by *** prediction performance of both ANN and LR was *** The incidence of AKI was 60.6%(n=88/145)and the following predictors were identified by LR:MELD score>25(odds ratio[OR]=1.999),preoperative kidney dysfunction(OR=1.279),extended criteria donors(OR=1.191),intraoperative arterial hypotension(OR=1.935),intraoperative massive blood transfusion(MBT)(OR=1.830),and postoperative serum lactate(SL)(OR=2.001).The area under the receiver-operating characteristic curve was best for ANN(0.81,95%confidence interval[CI]:0.75-0.83)than for LR(0.71,95%CI:0.67-0.76).The root-mean-square error and mean absolute error in the ANN model were 0.47 and 0.38,*** The severity of liver disease,pre-existing kidney dysfunction,marginal grafts,hemodynamic instability,MBT,and SL are predictors of postoperative AKI,and ANN has better prediction performance than LR in this scenario.

关键词： Logistic regression Liver transplantation Acute kidney injury Machine learning Artificial neural network

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Risk factors for acute kidney injury after partial hepatectomy

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World Journal of Hepatology 2017年第18期9卷 815-822页

作者： luis Cesar Bredt luis alberto batista peres Department of Surgical Oncology and Hepa- tobiliary Surgery University Hospital of Western Paraná State University of Western Paraná Department of Nephrology University Hospital of Western Paraná State University of Western Paraná

AIM To identify risk factors for the occurrence of acute kidney injury(AKI) in the postoperative period of partial hepatectomies. METHODS Retrospective analysis of 446 consecutive resections in 405 patients, analyzing clinical characteristics, preoperative laboratory data, intraoperative data, and postoperative laboratory data and clinical evolution. Adopting the International Club of Ascites criteria for the definition of AKI, potential predictors of AKI by logistic regression were identified. RESULTS Of the total 446 partial liver resections, postoperative AKI occurred in 80 cases(17.9%). Identified predictors of AKI were: Non-dialytic chronic kidney injury(CKI), biliary obstruction, the Model for End-Stage Liver Disease(MELD) score, the extent of hepatic resection, the occurrence of intraoperative hemodynamic instability, post-hepatectomy haemorrhage, and postoperative sepsis. CONCLUSION The MELD score, the presence of non-dialytic CKIand biliary obstruction in the preoperative period, and perioperative hemodynamics instability, bleeding, and sepsis are risk factors for the occurrence of AKI in patients that underwent partial hepatectomy.

关键词：肾损害 Hepatectomy 手术后肝切除术

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Acute renal injury after partial hepatectomy

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World Journal of Hepatology 2016年第21期8卷 891-901页

作者： luis alberto batista peres luis Cesar Bredt Raphael Flavio Fachini Cipriani Department of Nephrology University Hospital of Western ParanáState University of Western Paraná Department of Surgical Oncology and Hepatobiliary Surgery University Hospital of Western ParanáState University of Western Paraná Department of General Surgery University Hospital of Western ParanáState University of Western Paraná

Currently, partial hepatectomy is the treatment of choice for a wide variety of liver and biliary conditions. Among the possible complications of partial hepatectomy, acute kidney injury(AKI) should be considered as an important cause of increased morbidity and postoperative mortality. Difficulties in the data analysis related to postoperative AKI after liver resections are mainly due to the multiplicity of factors to be considered in the surgical patients, moreover, there is no consensus of the exact definition of AKI after liver resection in the literature, which hampers comparison and analysis of the scarce data published on the subject. Despite this multiplicity of risk factors for postoperative AKI after partial hepatectomy, there are main factors that clearly contribute to its occurrence. First factor relates to large blood losses with renal hypoperfusion during the operation, second factor relates to the occurrence of post-hepatectomy liver failure with consequent distributive circulatory changes and hepatorenal syndrome. Eventually, patients can have more than one factor contributing to post-operative AKI, and frequently these combinations of acute insults can be aggravated by sepsis or exposure to nephrotoxic drugs.

关键词： Hepatectomy Liver resection Acute renal injury Hepatorenal syndrome Kidney

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Artificial neural network for prediction of acute kidney injury after liver transplantation for cirrhosis and hepatocellular carcinoma

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Artificial Intelligence in Cancer 2021年第5期2卷 51-59页

作者： luis Cesar Bredt luis alberto batista peres Department of Surgical Oncology and General Surgery University Hospital of Western ParanáState University of Western ParanáCascavel 85819-110ParanáBrazil Department of Nephrology University Hospital of Western ParanáState University of Western ParanáCascavel 85819-110ParanáBrazil

Acute kidney injury(AKI)has serious consequences on the prognosis of patients undergoing liver transplantation(LT)for liver cancer and *** neural network(ANN)has recently been proposed as a useful tool in many fields in the setting of solid organ transplantation and surgical oncology,where patient prognosis depends on a multidimensional and nonlinear relationship between variables pertaining to the surgical procedure,the donor(graft characteristics),and the recipient *** the specific case of LT,ANN models have been developed mainly to predict survival in patients with cirrhosis,to assess the best donor-to-recipient match during allocation processes,and to foresee postoperative complications and *** is a specific opinion review on the role of ANN in the prediction of AKI after LT for liver cancer and cirrhosis,highlighting potential strengths of the method to forecast this serious postoperative complication.

关键词： Liver transplantation Acute kidney injury Artificial neural network Prediction Hepatocellular carcinoma Postoperative

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A multi-integrated approach on toxicity effects of engineered TiO2 nanoparticles

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Frontiers of Environmental Science & Engineering 2015年第5期9卷 793-803页

作者： Ana PICADO Susana M. PAIXAO Liliana MOITA luis SILVA Mario S. DINIZ Joana LOURENCO Isabel peres luisa CASTRO Jose Brito CORREIA Joana PEREIRA Isabel FERREIRA Antonio Pedro Alves MATOS Pedro BARQUINHA Elsa MENDONCA LNEG-National Laboratory of Energy and Geology I.P. Lisbon 1649-038 Portugal REQUIMTE Chemistry Department Fine Chemistry and Biotechnology Center Faculty of Sciences and Technology New University of Lisbon Caparica 2829-516 Portugal IMAR-Ocean Institute Department of Sciences and Environmental Engineering Faculty of Sciences and Technology New University of Lisbon Caparica 2829-516 Portugal CENIMAT/I3N and Department of Materials Science Faculty of Sciences and Technology New University of Lisbon Caparica 2829-516 Portugal Pathological Anatomy Curry Cabml Hospital Lisbon 1069-166 Portugal CESAM Faculty of Sciences Lisbon University Lisbon 1749-016 Portugal

The new properties of engineered nanoparti- cles drive the need for new knowledge on the safety, fate, behavior and biologic effects of these particles on organisms and ecosystems. Titanium dioxide nanoparticles have been used extensively for a wide range of applica- tions, e.g, self-cleaning surface coatings, solar cells, water treatment agents, topical sunscreens. Within this scenario increased environmental exposure can be expected but data on the ecotoxicological evaluation ofnanoparticles are still scarce. The main purpose of this work was the evaluation of effects of TiO2 nanoparticles in several organisms, covering different trophic levels, using a battery of aquatic assays. Using fish as a vertebrate model organism tissue histological and ultrastructural observations and the stress enzyme activity were also studied. TiO2 nanoparticles （Aeroxide~ P25）, two phase composition ofanatase （65%） and rutile （35%） with an average particle size value of 27.64-11 nm were used. Results on the ECs0 for the tested aquatic organisms showed toxicity for the bacteria, the algae and the crustacean, being the algae the most sensitive tested organism. The aquatic plant Lemna minor showed no effect on growth. The fish Carassius auratus showed no effect on a 21 day survival test, though at a biochemical level the cytosolic Glutathione-S-Transferase total activity, in intestines, showed a general significant decrease （p〈0.05） after 14 days of exposure for all tested concentrations. The presence of TiO2 nanoparticles aggregates were observed in the intestine lumen but their internalization by intestine cells could not be confirmed.

关键词： ecotoxicity enzymatic analysis histology,transmission electron microscopy (TEM) TiO2-nanoparti-cles

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