Novel mutations in the human sucrase-isomaltase gene (SI) that cause congenital carbohydrate malabsorption

Disaccharide intolerance I or congenital sucrase-isomaltase deficiency (CSID) is a disorder leading to maldigestion of disaccharides, which is autosomal recessively inherited. Here we analyzed the sucrase-isomaltase (SI) gene from 11 patients of Hungarian origin with congenital sucrase-isomaltase deficiency. Variants in the SI gene had previously been described in CSID patients, which cause amino acid exchanges that affect the transport, the processing, or the function of the SI protein. None of our patients had known mutations for CSID. Our analyses revealed 43 SI variants in total, 15 within exons and one at a splice site. Eight of the exonic mutations lead to amino acid exchanges, causing hypomorph or null alleles. One new variation affects a splice site, which is also predicted to result in a null allele. All potential pathological alterations were present on one allele only. In six out of the 11 patients the phenotype of CSID could be explained by compound heterozygosity.

Capillary zone electrophoresis determination of carbohydrate-deficient transferrin using the new CEofix reagents under high-resolution conditions

Capillary zone electrophoresis (CZE) with a dynamic double coating based on the new CEofix reagents is shown to provide high-resolution separations of serum transferrin (Tf) isoforms, a prerequisite for the monitoring of unusual and complex Tf patterns, including those seen with genetic variants and disorders of glycosylation. A 50 microm I.D. fused-silica capillary of 60 cm total length, an applied voltage of 20 kV and a capillary temperature of 30 degrees C results in 15 min CZE runs of high assay precision and thus provides a robust approach for the determination of carbohydrate-deficient transferrin (CDT, sum of asialo-Tf and disialo-Tf in relation to total Tf) in human serum. Except for selected samples of patients with severe liver diseases and sera with high levels of paraproteins, interference-free Tf patterns are detected. Compared with the use of the previous CEofix reagents for CDT under the same instrumental conditions, the resolution between disialo-Tf and trisialo-Tf is significantly higher (1.7 versus 1.4). The CDT levels of reference and patient sera are comparable, suggesting that the new assay can be applied for screening and confirmation analyses. The high-resolution CZE assay represents an attractive alternative to HPLC and can be regarded as a candidate of a reference method for CDT.

Biomass and Stored Carbohydrate Compensation after Above-Ground Biomass Removal in a Perennial Herb: Does Environmental Productivity Play a Role?

Many plant species are able to tolerate severe disturbance leading to removal of a substantial portion of the body by resprouting from intact or fragmented organs. Resprouting enables plants to compensate for biomass loss and complete their life cycles. The degree of disturbance tolerance, and hence the ecological advantage of damage tolerance (in contrast to alternative strategies), has been reported to be affected by environmental productivity. In our study, we examined the influence of soil nutrients (as an indicator of environmental productivity) on biomass and stored carbohydrate compensation after removal of aboveground parts in the perennial resprouter Plantago lanceolata. Specifically, we tested and compared the effects of nutrient availability on biomass and carbon storage in damaged and undamaged individuals. Damaged plants of P. lanceolata compensated neither in terms of biomass nor overall carbon storage. However, whereas in the nutrient-poor environment, root total non-structural carbohydrate concentrations (TNC) were similar for damaged and undamaged plants, in the nutrient-rich environment, damaged plants had remarkably higher TNC than undamaged plants. Based on TNC allocation patterns, we conclude that tolerance to disturbance is promoted in more productive environments, where higher photosynthetic efficiency allows for successful replenishment of carbohydrates. Although plants under nutrient-rich conditions did not compensate in terms of biomass or seed production, they entered winter with higher content of carbohydrates, which might result in better performance in the next growing season. This otherwise overlooked compensation mechanism might be responsible for inconsistent results reported from other studies.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.