Diaphragmatic hernia in an 8-year-old with Ehlers-Danlos syndrome

Ehlers-Danlos syndrome (EDS) includes a group of connective-tissue disorders characterized by abnormal collagen metabolism. The diverse spectrum of this disease and its complications present a distinct challenge to the surgeon. Patients with this hereditary disorder may have hyperelastic or fragile skin, poor wound healing, hypermobile joints, clotting abnormalities, spontaneous pneumothoraces, recurrent hernias, bowel perforation, and vascular complications. An 8-year-old female with EDS type VII and a newly diagnosed diaphragmatic hernia is presented. Surgical considerations of this syndrome are reviewed.     

An event-related potentials study of identity positive and negative priming.

Although extensive research on negative priming has been conducted using behavioural measures, little is known about its brain correlates. Moreover, results from this scarce number of studies are rather contradictory. The aim of this study is to further explore the temporal course of negative and positive priming in the brain by means of an identity task in which participants had to recognize letters. Results showed that positive priming modulated both reaction times and brain waves in the 600-800 ms time-interval. These effects might be related to memory processes. However, whereas negative priming was reflected in slowed reaction times, no brain wave modulations were found. This pattern of results remarks the influence of task parameters when addressing the study of negative priming.     

Pattern recognition analysis of 1H NMR spectra from perchloric acid extracts of human brain tumor biopsies

Pattern recognition techniques (factor analysis and neural networks) were used to investigate and classify human brain tumors based on the ¹H NMR spectra of chemically extracted biopsies (n = 118). After removing information from lactate (because of variable ischemia times), unsupervised learning suggested that the spectra separated naturally into two groups: meningiomas and other tumors. Principal component analysis reduced the dimensionality of the data. A back-propagation neural network using the first 30 principal components gave 85% correct classification of meningiomas and nonmeningiomas. Simplification by vector rotation gave vectors that could be assigned to various metabolites, making it possible to use or to reject their information for neural network classification. Using scores calculated from the four rotated vectors due to creatine and glutamine gave the best classification into meningiomas and nonmeningiomas (89% correct). Classification of gliomas (n = 47) gave 62% correct within one grade. Only inositol showed a significant correlation with glioma grade.