Nigral and Striatal Grafts to the Lesioned Neostriatum: Models of Graft Function

Post-transplant diabetes mellitus (PTDM) and dyslipidaemia are the most common metabolic complications in kidney transplant recipients (KTR). They are associated with a higher risk of lower graft function and survival, as well as an increased risk of cardiovascular disease (CVD). The aim of this review is to provide current data on the epidemiology, pathophysiology and optimal management of these two principal metabolic complications in KTR. Several risk factors in this metabolic milieu are either already present or emerge after renal transplantation, such as those due to immunosuppressive therapy. However, the exact pathogenic mechanisms have not been fully elucidated. Awareness of these disorders is crucial to estimate CVD risk in KTR and optimize screening and therapeutic strategies. These include lifestyle (preferably according to the Mediterranean pattern) and immunosuppressive regimen modification, as well as the best available anti-diabetic (insulin or oral hypoglycaemic agents) and hypolipidaemic (e.g. statins) regimen according to an individual’s metabolic profile and medical history.

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Association Between Delayed Graft Function (DGF) Biomarkers and Long-term Outcomes After Living Donor Kidney Transplantation

Reviews on Recent Clinical Trials ◽

10.2174/1574887113666180522082930 ◽

2018 ◽

Vol 13 (4) ◽

pp. 312-318 ◽

Cited By ~ 1

Author(s):

Zahra Sahraei ◽

Mona Mehdizadeh ◽

Jamshid Salamzadeh ◽

Mohsen Nafar ◽

Azadeh Eshraghi

Keyword(s):

Kidney Transplantation ◽

Living Donor ◽

Graft Function ◽

Delayed Graft Function ◽

Long Term Outcomes ◽

Donor Kidney ◽

Living Donor Kidney Transplantation ◽

Donor Kidney Transplantation ◽

Living Donor Kidney

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Reduced glycemic variability and flexible graft function after islet transplantation: A case report

Journal of Diabetes Investigation ◽

10.1111/jdi.13292 ◽

2020 ◽

Vol 11 (6) ◽

pp. 1677-1680 ◽

Cited By ~ 1

Author(s):

Toshihiro Nakamura ◽

Junji Fujikura ◽

Takayuki Anazawa ◽

Ryo Ito ◽

Masahito Ogura ◽

...

Keyword(s):

Case Report ◽

Islet Transplantation ◽

Graft Function ◽

Glycemic Variability

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The future is coming: promising perspectives regarding the use of machine learning in renal transplantation

Brazilian Journal of Nephrology ◽

10.1590/2175-8239-jbn-2018-0047 ◽

2019 ◽

Vol 41 (2) ◽

pp. 284-287

Author(s):

Pedro Guilherme Coelho Hannun ◽

Luis Gustavo Modelli de Andrade

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Graft Function ◽

Statistical Technique ◽

Daily Routine ◽

Learning Approaches ◽

Post Transplantation ◽

Chronic Allograft Rejection ◽

Institutional Experience ◽

Learning Principles

Abstract Introduction: The prediction of post transplantation outcomes is clinically important and involves several problems. The current prediction models based on standard statistics are very complex, difficult to validate and do not provide accurate prediction. Machine learning, a statistical technique that allows the computer to make future predictions using previous experiences, is beginning to be used in order to solve these issues. In the field of kidney transplantation, computational forecasting use has been reported in prediction of chronic allograft rejection, delayed graft function, and graft survival. This paper describes machine learning principles and steps to make a prediction and performs a brief analysis of the most recent applications of its application in literature. Discussion: There is compelling evidence that machine learning approaches based on donor and recipient data are better in providing improved prognosis of graft outcomes than traditional analysis. The immediate expectations that emerge from this new prediction modelling technique are that it will generate better clinical decisions based on dynamic and local practice data and optimize organ allocation as well as post transplantation care management. Despite the promising results, there is no substantial number of studies yet to determine feasibility of its application in a clinical setting. Conclusion: The way we deal with storage data in electronic health records will radically change in the coming years and machine learning will be part of clinical daily routine, whether to predict clinical outcomes or suggest diagnosis based on institutional experience.

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