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The early detection of graft failure in pediatric liver transplantation is crucial for appropriate intervention. Graft failure is associated with numerous perioperative risk factors. This study aimed to develop an individualized predictive model for 90-days graft failure in pediatric liver transplantation using machine learning methods. We conducted a single-center retrospective cohort study. A total of 87 liver transplantation cases performed in patients aged < 12 years at the Severance Hospital between January 2010 and September 2020 were included as data samples. Preoperative conditions of recipients and donors, intraoperative care, postoperative serial laboratory parameters, and events observed within seven days of surgery were collected as features. A least absolute shrinkage and selection operator (LASSO) -based method was used for feature selection to overcome the high dimensionality and collinearity of variables. Among 146 features, four variables were selected as the resultant features, namely, preoperative hepatic encephalopathy, sodium level at the end of surgery, hepatic artery thrombosis, and total bilirubin level on postoperative day 7. These features were selected from different times and represent distinct clinical aspects. The model with logistic regression demonstrated the best prediction performance among various machine learning methods tested (area under the receiver operating characteristic curve (AUROC) = 0.898 and area under the precision–recall curve (AUPR) = 0.882). The risk scoring system developed based on the logistic regression model showed an AUROC of 0.910 and an AUPR of 0.830. Together, the prediction of graft failure in pediatric liver transplantation using the proposed machine learning model exhibited superior discrimination power and, therefore, can provide valuable information to clinicians for their decision making during the postoperative management of the patients. [ABSTRACT FROM AUTHOR]

Natural and synthetic melanins are promising pigments for several uses, such as biosorbents and bioadhesives. Further, their high biocompatibilities and excellent biological activities potentially allow them to be ideal biomaterials for environmental and biotechnological applications. Naturally occurring melanin has been used as a biosorbent for the environmental treatment of organic chemicals and for metal ion removal, and melanins have been chemically engineered to function more efficiently. In addition, melanin has been engineered for use as an adhesive coating material, and its polymeric properties have been enhanced for medical applications. Clearly, given melanin's high biocompatibility and biodegradability, it is a very viable biopolymer for further applications in environmental biotechnology fields. In this review, we highlight the uses of melanin as a biosorbent and bioadhesive in biotechnology fields. [ABSTRACT FROM AUTHOR]

Designing the process layout in a chemical plant is a complex and multidisciplinary task requiring input from experts in fields such as chemical, civil, mechanical, and instrument engineering. Plant layout entails the allocation of a given number of facilities on a given piece of land. Determining the optimal distribution of facilities in a plant requires an optimization calculation, including a variety of distance constraints, one of which is related to process safety. A few approaches have been taken to transform consequence analysis, such as toxic gas dispersion and its mitigation as well as the risks of fire and explosions, into mathematical equations as constraints of an optimization problem. An optimization problem with constraints related to safety is not easy to solve given limitations such as nonlinearity, uncertainty, and ethical difficulties in converting human life to costs for calculation purposes. However, safety concerns have increased to the point that developing this type of approach is necessary. The objective of this study was to review the current methodologies for plant layout optimization and to resolve facility siting issues. Process safety concepts are considered with a view to identifying gaps and issues with current methods in order to develop better methodologies for designing safer layouts. [ABSTRACT FROM AUTHOR]