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Vigia E, Ramalhete L, Ribeiro R, Barros I, Chumbinho B, Filipe E, Pena A, Bicho L, Nobre A, Carrelha S, Sobral M, Lamelas J, Coelho JS, Ferreira A, Marques HP. Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at Risk. J Pers Med 2023; 13:1071. [PMID: 37511684 PMCID: PMC10381793 DOI: 10.3390/jpm13071071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/21/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
INTRODUCTION Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection. METHODS We collected 74 features from 79 patients who underwent simultaneous pancreas-kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation. RESULTS Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model. CONCLUSION Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.
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Affiliation(s)
- Emanuel Vigia
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
- Nova Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
| | - Luís Ramalhete
- Nova Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
- Blood and Transplantation Center of Lisbon, Instituto Português do Sangue e da Transplantação, Alameda das Linhas de Torres, n 117, 1769-001 Lisbon, Portugal
- iNOVA4Health, Advancing Precision Medicine, RG11, Reno-Vascular Diseases Group, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
| | - Rita Ribeiro
- Nova Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
| | - Inês Barros
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Beatriz Chumbinho
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Edite Filipe
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Ana Pena
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Luís Bicho
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Ana Nobre
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Sofia Carrelha
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Mafalda Sobral
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Jorge Lamelas
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - João Santos Coelho
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Aníbal Ferreira
- iNOVA4Health, Advancing Precision Medicine, RG11, Reno-Vascular Diseases Group, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
- Nephrology, Hospital Curry Cabral, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Hugo Pinto Marques
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
- Nova Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
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Franco-Acevedo A, Comes J, Mack JJ, Valenzuela NM. New insights into maladaptive vascular responses to donor specific HLA antibodies in organ transplantation. FRONTIERS IN TRANSPLANTATION 2023; 2:1146040. [PMID: 38993843 PMCID: PMC11235244 DOI: 10.3389/frtra.2023.1146040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/03/2023] [Indexed: 07/13/2024]
Abstract
Transplant vasculopathy (TV) causes thickening of donor blood vessels in transplanted organs, and is a significant cause of graft loss and mortality in allograft recipients. It is known that patients with repeated acute rejection and/or donor specific antibodies are predisposed to TV. Nevertheless, the exact molecular mechanisms by which alloimmune injury culminates in this disease have not been fully delineated. As a result of this incomplete knowledge, there is currently a lack of effective therapies for this disease. The immediate intracellular signaling and the acute effects elicited by anti-donor HLA antibodies are well-described and continuing to be revealed in deeper detail. Further, advances in rejection diagnostics, including intragraft gene expression, provide clues to the inflammatory changes within allografts. However, mechanisms linking these events with long-term outcomes, particularly the maladaptive vascular remodeling seen in transplant vasculopathy, are still being delineated. New evidence demonstrates alterations in non-coding RNA profiles and the occurrence of endothelial to mesenchymal transition (EndMT) during acute antibody-mediated graft injury. EndMT is also readily apparent in numerous settings of non-transplant intimal hyperplasia, and lessons can be learned from advances in those fields. This review will provide an update on these recent developments and remaining questions in our understanding of HLA antibody-induced vascular damage, framed within a broader consideration of manifestations and implications across transplanted organ types.
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Affiliation(s)
- Adriana Franco-Acevedo
- Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, United States
| | - Johanna Comes
- Department of Medical Biochemistry, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Julia J Mack
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, CA, United States
| | - Nicole M Valenzuela
- Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, United States
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Diedkova K, Pogrebnjak AD, Kyrylenko S, Smyrnova K, Buranich VV, Horodek P, Zukowski P, Koltunowicz TN, Galaszkiewicz P, Makashina K, Bondariev V, Sahul M, Čaplovičová M, Husak Y, Simka W, Korniienko V, Stolarczyk A, Blacha-Grzechnik A, Balitskyi V, Zahorodna V, Baginskiy I, Riekstina U, Gogotsi O, Gogotsi Y, Pogorielov M. Polycaprolactone-MXene Nanofibrous Scaffolds for Tissue Engineering. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 36892008 DOI: 10.1021/acsami.2c22780] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
New conductive materials for tissue engineering are needed for the development of regenerative strategies for nervous, muscular, and heart tissues. Polycaprolactone (PCL) is used to obtain biocompatible and biodegradable nanofiber scaffolds by electrospinning. MXenes, a large class of biocompatible 2D nanomaterials, can make polymer scaffolds conductive and hydrophilic. However, an understanding of how their physical properties affect potential biomedical applications is still lacking. We immobilized Ti3C2Tx MXene in several layers on the electrospun PCL membranes and used positron annihilation analysis combined with other techniques to elucidate the defect structure and porosity of nanofiber scaffolds. The polymer base was characterized by the presence of nanopores. The MXene surface layers had abundant vacancies at temperatures of 305-355 K, and a voltage resonance at 8 × 104 Hz with the relaxation time of 6.5 × 106 s was found in the 20-355 K temperature interval. The appearance of a long-lived component of the positron lifetime was observed, which was dependent on the annealing temperature. The study of conductivity of the composite scaffolds in a wide temperature range, including its inductive and capacity components, showed the possibility of the use of MXene-coated PCL membranes as conductive biomaterials. The electronic structure of MXene and the defects formed in its layers were correlated with the biological properties of the scaffolds in vitro and in bacterial adhesion tests. Double and triple MXene coatings formed an appropriate environment for cell attachment and proliferation with mild antibacterial effects. A combination of structural, chemical, electrical, and biological properties of the PCL-MXene composite demonstrated its advantage over the existing conductive scaffolds for tissue engineering.
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Affiliation(s)
- Kateryna Diedkova
- Sumy State University, 2 Rymskogo-Korsakova Street, Sumy 40007, Ukraine
- University of Latvia, 3 Jelgavas Street, Riga LV-1004, Latvia
| | - Alexander D Pogrebnjak
- Sumy State University, 2 Rymskogo-Korsakova Street, Sumy 40007, Ukraine
- Department of Motor Vehicles, Lublin University of Technology, Nadbystrzycka 38 A, Lublin 20-618, Poland
- Al-Farabi Kazakh National University, 71 Al-Farabi Avenue, Almaty 050040, Kazakhstan
| | - Sergiy Kyrylenko
- Sumy State University, 2 Rymskogo-Korsakova Street, Sumy 40007, Ukraine
| | - Kateryna Smyrnova
- Sumy State University, 2 Rymskogo-Korsakova Street, Sumy 40007, Ukraine
- Institute of Materials Science, Faculty of Materials Science and Technology, Slovak University of Technology, J. Bottu 25, Trnava 917 24, Slovakia
| | | | - Pawel Horodek
- Henryk Niewodniczanski Institute of Nuclear Physics of the Polish Academy of Sciences, 152 Radzikowskiego Street, Krakow 31-342, Poland
| | - Pawel Zukowski
- Department of Electrical Devices and High Voltage Technology, Lublin University of Technology, 38 D Nadbystrzycka Street, Lublin 20-618, Poland
| | - Tomasz N Koltunowicz
- Department of Electrical Devices and High Voltage Technology, Lublin University of Technology, 38 D Nadbystrzycka Street, Lublin 20-618, Poland
| | - Piotr Galaszkiewicz
- Department of Electrical Devices and High Voltage Technology, Lublin University of Technology, 38 D Nadbystrzycka Street, Lublin 20-618, Poland
| | - Kristina Makashina
- East-Kazakhstan State Technical University, D. Serikbayev Street, 19, Ust-Kamenogorsk 070000, Kazakhstan
| | - Vitaly Bondariev
- Department of Electrical Devices and High Voltage Technology, Lublin University of Technology, 38 D Nadbystrzycka Street, Lublin 20-618, Poland
| | - Martin Sahul
- Institute of Materials Science, Faculty of Materials Science and Technology, Slovak University of Technology, J. Bottu 25, Trnava 917 24, Slovakia
| | - Maria Čaplovičová
- Centre for Nanodiagnostics of Materials, Slovak University of Technology in Bratislava, 5 Vazovova Street, Bratislava 812 43, Slovakia
| | - Yevheniia Husak
- Sumy State University, 2 Rymskogo-Korsakova Street, Sumy 40007, Ukraine
- Faculty of Chemistry, Silesian University of Technology, 9 Strzody Street, Gliwice 44-100, Poland
| | - Wojciech Simka
- Faculty of Chemistry, Silesian University of Technology, 9 Strzody Street, Gliwice 44-100, Poland
| | - Viktoriia Korniienko
- Sumy State University, 2 Rymskogo-Korsakova Street, Sumy 40007, Ukraine
- University of Latvia, 3 Jelgavas Street, Riga LV-1004, Latvia
| | - Agnieszka Stolarczyk
- Faculty of Chemistry, Silesian University of Technology, 9 Strzody Street, Gliwice 44-100, Poland
| | - Agata Blacha-Grzechnik
- Faculty of Chemistry, Silesian University of Technology, 9 Strzody Street, Gliwice 44-100, Poland
| | - Vitalii Balitskyi
- Materials Research Centre, 3 Krzhizhanovskogo Street, Kyiv 03142, Ukraine
| | - Veronika Zahorodna
- Materials Research Centre, 3 Krzhizhanovskogo Street, Kyiv 03142, Ukraine
| | - Ivan Baginskiy
- Materials Research Centre, 3 Krzhizhanovskogo Street, Kyiv 03142, Ukraine
| | - Una Riekstina
- University of Latvia, 3 Jelgavas Street, Riga LV-1004, Latvia
| | - Oleksiy Gogotsi
- Materials Research Centre, 3 Krzhizhanovskogo Street, Kyiv 03142, Ukraine
| | - Yury Gogotsi
- Sumy State University, 2 Rymskogo-Korsakova Street, Sumy 40007, Ukraine
- A. J. Drexel Nanomaterials Institute, and Department of Materials Science and Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Maksym Pogorielov
- Sumy State University, 2 Rymskogo-Korsakova Street, Sumy 40007, Ukraine
- University of Latvia, 3 Jelgavas Street, Riga LV-1004, Latvia
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Di Cocco P, Gaitonde S, Spaggiari M, Fratti A, Alvarez JA, Petrochenkov E, Valdenepas BT, Gupta P, Benedetti E, Tzvetanov I. Desensitizing With Temporary Donor Splenic Transplant: Hope for the Sensitized Patients on Pancreas and Kidney -Pancreas Transplant Waitlist. Transplant Proc 2023; 55:295-302. [PMID: 36801174 DOI: 10.1016/j.transproceed.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/20/2022] [Accepted: 01/24/2023] [Indexed: 02/21/2023]
Abstract
BACKGROUND Sensitized patients on a waitlist with donor specific antibodies (DSA) or positive flow cytometry cross match (FXM) to deceased donor organ have few pretransplant desensitization options due to increasing graft cold ischemia time. Herein, sensitized simultaneous kidney/pancreas recipients received temporary splenic transplant from the same donor under the hypothesis that spleen would function as a DSA graveyard and provide a safe immunologic window for transplant. METHODS We analyzed presplenic and postsplenic transplant FXM and DSA results of 8 sensitized patients who underwent simultaneous kidney and pancreas transplantation with temporary deceased donor spleen between November 2020 and January 2022. RESULTS Pre-splenic transplant, 4 sensitized patients were both T-cell and B-cell FXM positive; one was only B-cell FXM positive and 3 were DSA positive/FXM negative. Post-splenic transplant, all were FXM negative. Pre-splenic transplant class I and class II DSA were detected in 3 patients, only class I DSA in 4 patients, and only class II DSA in 1 patient. Postsplenic transplant, class I DSA was eliminated in all patients. Class II DSA persisted in 3 patients; all showed a marked decrease in DSA mean fluorescence index. Class II DSA was eliminated in one patient. CONCLUSION Donor spleen functions as a DSA graveyard and provides an immunologically safe window for kidney-pancreas transplantation.
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Affiliation(s)
| | - Sujata Gaitonde
- Department of Pathology, University of Illinois at Chicago, Illinois.
| | - Mario Spaggiari
- Department of Surgery, University of Illinois at Chicago, Illinois
| | - Alberto Fratti
- Department of Surgery, University of Illinois at Chicago, Illinois
| | | | | | - Bentio T Valdenepas
- Department of Pharmacy Practice, University of Illinois at Chicago, Illinois
| | - Priyanka Gupta
- Department of Pathology, University of Illinois at Chicago, Illinois
| | - Enrico Benedetti
- Department of Surgery, University of Illinois at Chicago, Illinois
| | - Ivo Tzvetanov
- Department of Surgery, University of Illinois at Chicago, Illinois
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Balch JA, Delitto D, Tighe PJ, Zarrinpar A, Efron PA, Rashidi P, Upchurch GR, Bihorac A, Loftus TJ. Machine Learning Applications in Solid Organ Transplantation and Related Complications. Front Immunol 2021; 12:739728. [PMID: 34603324 PMCID: PMC8481939 DOI: 10.3389/fimmu.2021.739728] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/25/2021] [Indexed: 11/13/2022] Open
Abstract
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.
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Affiliation(s)
- Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Daniel Delitto
- Department of Surgery, Johns Hopkins University, Baltimore, MD, United States
| | - Patrick J Tighe
- Department of Anesthesiology, University of Florida Health, Gainesville, FL, United States.,Department of Orthopedics, University of Florida Health, Gainesville, FL, United States.,Department of Information Systems/Operations Management, University of Florida Health, Gainesville, FL, United States
| | - Ali Zarrinpar
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Philip A Efron
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.,Department of Computer and Information Science and Engineering University of Florida, Gainesville, FL, United States.,Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.,Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Azra Bihorac
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States.,Department of Medicine, University of Florida Health, Gainesville, FL, United States
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States.,Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
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Anger LB, Doligalski CT. Solid organ transplant pharmacotherapy: Complicated and continually changing. Pharmacotherapy 2021; 41:4-5. [PMID: 33598986 DOI: 10.1002/phar.2492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 11/30/2020] [Accepted: 12/01/2020] [Indexed: 11/09/2022]
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