Ketogenic diet as a glycine lowering therapy in nonketotic hyperglycinemia and impact on brain glycine levels.

BACKGROUND: Nonketotic hyperglycinemia (NKH) is a severe neurometabolic disorder characterized by increased glycine levels. Current glycine reduction therapy uses high doses of sodium benzoate. The ketogenic diet (KD) may represent an alternative method of glycine reduction. AIM: We aimed to assess clinical and biochemical effects of two glycine reduction strategies: high dose benzoate versus KD with low dose benzoate. METHODS: Six infants with NKH were first treated with high dose benzoate therapy to achieve target plasma glycine levels, and then switched to KD with low dose benzoate. They were evaluated as clinically indicated by physical examination, electroencephalogram, plasma and cerebral spinal fluid amino acid levels. Brain glycine levels were monitored by magnetic resonance spectroscopy (MRS). RESULTS: Average plasma glycine levels were significantly lower with KD compared to benzoate monotherapy by on average 28%. Two infants underwent comparative assessments of brain glycine levels via serial MRS. A 30% reduction of brain glycine levels was observed in the basal ganglia and a 50% reduction in the white matter, which remained elevated above normal, and was equivalent between the KD and high dose benzoate therapies. CSF analysis obtained while participants remained on the KD showed a decrease in glycine, serine and threonine levels, reflecting their gluconeogenetic usage. Clinically, half the patients had seizure reduction on KD, otherwise the clinical impact was variable. CONCLUSION: KD is an effective glycine reduction method in NKH, and may provide a more consistent reduction in plasma glycine levels than high-dose benzoate therapy. Both high-dose benzoate therapy and KD equally reduced but did not normalize brain glycine levels even in the setting of low-normal plasma glycine.

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user.