Does hyperphenylalaninemia induce brain glucose hypometabolism? Cerebral spinal fluid findings in treated adult phenylketonuric patients.

Despite numerous studies in human patients and animal models for phenylketonuria (PKU; OMIM#261600), the pathophysiology of PKU and the underlying causes of brain dysfunction and cognitive problems in PKU patients are not well understood. In this study, lumbar cerebral spinal fluid (CSF) was obtained immediately after blood sampling from early-treated adult PKU patients who had fasted overnight. Metabolite and amino acid concentrations in the CSF of PKU patients were compared with those of non-PKU controls. The CSF concentrations and CSF/plasma ratios for glucose and lactate were found to be below normal, similar to what has been reported for glucose transporter1 (GLUT1) deficiency patients who exhibit many of the same clinical symptoms as untreated PKU patients. CSF glucose and lactate levels were negatively correlated with CSF phenylalanine (Phe), while CSF glutamine and glutamate levels were positively correlated with CSF Phe levels. Plasma glucose levels were negatively correlated with plasma Phe concentrations in PKU subjects, which partly explains the reduced CSF glucose concentrations. Although brain glucose concentrations are unlikely to be low enough to impair brain glucose utilization, it is possible that the metabolism of Phe in the brain to produce phenyllactate, which can be transported across the blood-brain barrier to the blood, may consume glucose and/or lactate to generate the carbon backbone for glutamate. This glutamate is then converted to glutamine and carries the Phe-derived ammonia from the brain to the blood. While this mechanism remains to be tested, it may explain the correlations of CSF glutamine, glucose, and lactate concentrations with CSF Phe.

Sustainable manufacturing through application of reconfigurable and intelligent systems in production processes: a system perspective.

Sustainable production aims at creating products from processes that minimize environmental impact, energy consumption and natural resources. Customers and markets are ever more leaning towards digital, custom, and flexible solutions with lower environmental impact. Hence, Industry 4.0 (I4.0) solutions are increasingly including social and environmental sustainability aspects. We focus on the realization of an infrastructure integrating industrially relevant application modules by combining system reconfigurability and artificial intelligence, towards sustainable production. To meet the final goal of sustainable production, we address four challenges considering flexibility and sustainability in production in a holistic way: (1) developing infrastructural and methodological tools to support companies to explore the potential of I4.0 towards sustainable production; (2) managing the configurability and customization possibilities of products; (3) effectively handling the flexibility provided by a production system with rapid reconfiguration capabilities; (4) integrating hardware and software flexibility by using reconfigurable robotics and machine learning methods. By developing and connecting different application modules, we obtain a physical demonstrator which represents on the one hand an exemplary scenario of reconfigurable and flexible production system; on the other, it enables new research activities and insights with a see, touch & feel approach for industrial and research realities.