Purpose: To determine the accuracy of magnetic resonance imaging (MRI) volume scans: 1) to measure known meal volumes in vitro, and 2) to compare volume changes In response to a meal measured with... Show morePurpose: To determine the accuracy of magnetic resonance imaging (MRI) volume scans: 1) to measure known meal volumes in vitro, and 2) to compare volume changes In response to a meal measured with the barostat with those measured with MRI in vivo. Materials and Methods: Polyethylene bags were filled with known volumes and MRI volume scans were performed to determine the accuracy of the volume measurements. Barostat measurements and MRI volume scans were performed simultaneously in 14 healthy subjects before and up to 90 minutes after ingestion of a liquid meal. Results: In vitro MRI-determined volumes showed an excellent linear relationship (r = 0.995, P < 0.001) with actual meal volumes. Although fasting gastric volume, postprandial gastric volume, and relaxation volume measured with MRI were significantly larger compared to volumes measured with the barostat, volumes determined with both techniques showed excellent correlation. Conclusion: Volumes in the range of postprandial meal volumes are accurately measured with MRI. MRI is a non-invasive technique to measure stomach volumes and volume changes in response to a meal. Volume changes in response to a meal measured with MRI correlate perfectly with those measured with the barostat device. Show less
Background: Multiplex Ligation-Dependent Probe Amplification (MLPA) is an application that can be used for the detection of multiple chromosomal aberrations in a single experiment. In one reaction,... Show moreBackground: Multiplex Ligation-Dependent Probe Amplification (MLPA) is an application that can be used for the detection of multiple chromosomal aberrations in a single experiment. In one reaction, up to 50 different genomic sequences can be analysed. For a reliable work-flow, tools are needed for administrative support, data management, normalisation, visualisation, reporting and interpretation. Results: Here, we developed a data management system, MLPAInter for MLPA interpretation, that is windows executable and has a stand-alone database for monitoring and interpreting the MLPA data stream that is generated from the experimental setup to analysis, quality control and visualisation. A statistical approach is applied for the normalisation and analysis of large series of MLPA traces, making use of multiple control samples and internal controls. Conclusions: MLPAinter visualises MLPA data in plots with information about sample replicates, normalisation settings, and sample characteristics. This integrated approach helps in the automated handling of large series of MLPA data and guarantees a quick and streamlined dataflow from the beginning of an experiment to an authorised report. Show less
