Presse Med
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Lung transplantation has been accepted as a viable treatment for end-stage respiratory failure. While regression models continue to be a standard approach for attempting to predict patients' outcomes after lung transplantation, more sophisticated supervised machine learning (ML) techniques are being developed and show encouraging results. Transplant clinicians could utilize ML as a decision-support tool in a variety of situations (e.g. waiting list mortality, donor selection, immunosuppression, rejection prediction). Although for some topics ML is at an advanced stage of research (i.e. imaging and pathology) there are certain topics in lung transplantation that needs to be aware of the benefits it could provide.
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Type 1 diabetes is a disease resulting from autoimmune destruction of the insulin-producing beta cells in the pancreas. When type 1 diabetes develops into severe secondary complications, in particular end-stage nephropathy, or life-threatening severe hypoglycemia, the best therapeutic approach is pancreas transplantation, or more recently transplantation of the pancreatic islets of Langerhans. Islet transplantation is a cell therapy procedure, that is minimally invasive and has a low morbidity, but does not display the same rate of functional success as the more invasive pancreas transplantation because of suboptimal engraftment and survival. ⋯ A successful bioartificial pancreas would address the issues of engraftment, survival and rejection. Inclusion of unlimited sources of insulin-producing cells, such as xenogeneic porcine islets or stem cell-derived beta cells would further solve the problem of organ shortage. This article reviews the current status of clinical islet transplantation, the strategies aiming at developing a bioartificial pancreas, the clinical trials conducted in the field and the perspectives for further progress.