Mayo Foundation for Medical Education and Research

This document relates to methods and materials involved in treating a mammal having a gastrointestinal disorder (e.g., an irritable bowel syndrome (IBS) such as post-infection IBS (PI-IBS)). For example, one or more protease inhibitors and/or one or more microorganisms that produce one or more protease inhibitors can be administered to a mammal having, or at risk of developing, a gastrointestinal disorder to treat the mammal.

Endolumenal devices and methods can be used for the treatment of health conditions including obesity and diabetes. In some embodiments, the methods and systems provided herein can cause weight loss or control diabetes by reducing the caloric absorption of an individual. For example, this document provides several devices and methods for treating obesity and diabetes by using electroporation to modulate the duodenal mucosa. In addition, this document provides devices and methods for bypassing portions of the gastrointestinal tract to reduce nutritional uptake.

A prosthesis comprising a central body having a first section and a second section, and a plurality of protruding sections extending laterally from an outer surface of the central body is disclosed. The plurality of protruding sections is spaced apart around the outer surface of the central body. The first section has a first longitudinal axis and the second section has second longitudinal axis angled with respect to the first longitudinal axis.

Described here is a system and method for generating motion corrected Doppler ensemble data from ultrasound data wherein the ultrasound data comprise image obtained at a plurality of different time points. Local motion data from the ultrasound data are generated by tracking displacements in one or more local regions in the ultrasound data and stored. Clutter-filtered Doppler ensemble (CFDE) data are generated with the computer system by inputting the ultrasound data to a clutter filter and motion corrected Doppler ensemble data are generated by inputting the data and the CFDE data to a local non-rigid motion correction algorithm.

A device for treating a fracture between a proximal and a distal section of a bone is disclosed. It comprises a prosthesis configured to be implanted in the proximal section of the bone and an elongated plate dimensioned for placement on the bone across the fracture. The plate has a bone interface surface that faces the bone when placed across the fracture. The surface has a proximal region proximal to a first plane transverse to the plate, a distal region distal to a second plane transverse to the plate, and a midshaft region positioned between the two planes. The bone interface surface's shape transitions from a first curvature that is convex or concave at a proximal portion of the plate to a second curvature at a second portion of the plate longitudinally adjacent to the proximal portion of the plate. The second curvature is convex when the first curvature is concave and vice versa.

Provided herein is technology for esophageal disorder screening and particularly, but not exclusively, to methods, compositions, and related uses for detecting the presence of esophageal disorders (e.g., Barrett's esophagus, Barrett's esophageal dysplasia, etc.). In addition, the technology provides methods, compositions and related uses for distinguishing between Barrett's esophagus and Barrett's esophageal dysplasia, and between Barrett's esophageal low-grade dysplasia, Barrett's esophageal high-grade dysplasia, and esophageal adenocarcinoma within samples obtained through endoscopic brushing or nonendoscopic whole esophageal brushing or swabbing using a tethered device (e.g. such as a capsule sponge, balloon, or other device).

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme ("GBM") are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor ("BAT") regions.

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Zusammenfassung wird geladen …

Zusammenfassung wird geladen …