RESUMO
Background: The complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it. Material and method: Liver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated "in situ" for transplantation, and those discarded after the "in situ" evaluation were considered as no transplantable liver grafts, while those grafts transplanted after "in situ" evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed. Results: A total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85. Conclusion: The tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.
RESUMO
Post-surgical chemotherapy in pancreatic cancer has notorious side effects due to the high dose required. Multiple devices have been designed to tackle this aspect and achieve a delayed drug release. This study aimed to explore the controlled and sustained local delivery of a reduced drug dose from an irinotecan-loaded electrospun nanofiber membrane (named TARTESSUS) that can be placed on the patients' tissue after tumor resection surgery. The drug delivery system formulation was made of polycaprolactone (PCL). The mechanical properties and the release kinetics of the drug were adjusted by the electrospinning parameters and by the polymer ratio between 10 w.t.% and 14 w.t.% of PCL in formic acid:acetic acid:chloroform (47.5:47.5:5). The irinotecan release analysis was performed and three different release periods were obtained, depending on the concentration of the polymer in the dissolution. The TARTESSUS device was tested in 2D and 3D cell cultures and it demonstrated a decrease in cell viability in 2D culture between 72 h and day 7 from the start of treatment. In 3D culture, a decrease in viability was seen between 72 h, day 7 (p < 0.001), day 10 (p < 0.001), 14 (p < 0.001), and day 17 (p = 0.003) as well as a decrease in proliferation between 72 h and day 10 (p = 0.030) and a reduction in spheroid size during days 10 (p = 0.001), 14 (p < 0.001), and 17 (p < 0.001). In conclusion, TARTESSUS showed a successful encapsulation of a chemotherapeutic drug and a sustained and delayed release with an adjustable releasing period to optimize the therapeutic effect in pancreatic cancer treatment.