Gastric emptying and myoelectrical activity testing in children with esophageal atresia: A pilot study.

Objectives: Abnormalities of gastric function in children with esophageal atresia (EA) could potentially contribute to gastrointestinal symptoms and reduced quality of life (QOL). Therefore, we aimed to determine the feasibility and clinical usefulness of gastric function testing in children with EA. Methods: The validated PedsQL Gastrointestinal Symptoms Questionnaire (PedsQL-GI) was completed to assess gastrointestinal symptoms and symptom-related QOL. Gastric emptying and gastric myoelectrical activity were studied using 13C-gastric emptying octanoic acid breath test (13C-GEBT) and surface electrogastrography (EGG). Correlations between 13C-GEBT and EGG parameters and PedsQL-GI scores were investigated. Results: Fifteen patients (four males) were included (median age: 6 [3.0-8.5] years). Mean PedsQL-GI scores as reported by the children were comparable to the healthy population. However, parents reported a diminished QOL. Gastric function tests (gastric emptying and/or surface EGG) showed abnormalities in 12 patients (80%). Patients with abnormal slow waves showed abnormal gastric emptying coefficient more often. There was no significant association between 13C-GEBT nor EGG results and PedsQL-GI scores. Conclusions: 13C-GEBT and EGG can be used to evaluate gastric function in patients with EA. Abnormal gastric function tests were present in 80% of our cohort. However, abnormal gastric function did not significantly correlate with reported gastrointestinal symptom-related QOL.

Spotting psychopaths using technology.

For the past three and a half decades, the Psychopathy Checklist-Revised (PCL-R) and the self-report Psychopathic Personality Inventory-Revised (PPI-R) have been the standard measures for the diagnosis of psychopathy. Technological approaches can enhance these diagnostic methodologies. The purpose of this paper is to present a state-of-the-art review of various technological approaches for spotting psychopathy, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), functional MRI (fMRI), transcranial magnetic stimulation (TMS), and other measures. Results of EEG event-related potential (ERP) experiments support the theory that impaired amygdala function may be responsible for abnormal fear processing in psychopathy, which can ultimately manifest as psychopathic traits, as outlined by the PCL-R or PPI-R. Imaging studies, in general, point to reduced fear processing capabilities in psychopathic individuals. While the human element, introduced through researcher/participant interactions, can be argued as unequivocally necessary for diagnosis, these purely objective technological approaches have proven to be useful in conjunction with the subjective interviewing and questionnaire methods for differentiating psychopaths from non-psychopaths. Furthermore, these technologies are more robust than behavioral measures, which have been shown to fail.

EEG/MEG- and imaging-based diagnosis of Alzheimer's disease.

In recent years, researchers have embarked on a search of computer-aided methods for diagnosis of the Alzheimer's disease (AD) to help clinicians make the diagnosis earlier and more accurately such that treatment of the disease can begin sooner when there is a higher chance of success in slowing down the progression of this disease. This article presents a review of journal articles on brain signal- and image-based diagnosis of AD published in the past few years. The areas of signal processing, electroencephalogram and magnetoencephalogram are considered. In the area of image analysis, the following modalities are reviewed: magnetic resonance imaging (MRI), functional MRI, diffusion tensor MRI, and structural MRI. Computer-aided early diagnosis of the AD would be a major breakthrough with a very significant worldwide impact because medications would be able to slow down the progression of the disease. This review shows that this is a very active area in the frontier of brain research, with many multidisciplinary researchers exploring a variety of approaches using various types of brain signals and imaging technologies. The brain signal-based approaches will be able to point toward early onset diagnosis of the AD, but as the disease progresses, a multimodal approach can increase the accuracy of the diagnosis.