Advances in personalized medicine and noninvasive diagnostics in solid organ transplantation

Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at Risk

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.

New insights into maladaptive vascular responses to donor specific HLA antibodies in organ transplantation

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.

Polycaprolactone-MXene Nanofibrous Scaffolds for Tissue Engineering

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.

Desensitizing With Temporary Donor Splenic Transplant: Hope for the Sensitized Patients on Pancreas and Kidney -Pancreas Transplant Waitlist

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.

Machine Learning Applications in Solid Organ Transplantation and Related Complications

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.