Systematic review and meta-analyses of trendelenburg and prone position on intraocular pressure in adult patients undergoing surgery

Background. Patients undergoing surgery in the Trendelenburg and prone positions may be at risk for postoperative vision loss associated with increased intraocular pressure. The purpose of this dissertation research is to estimate the magnitude of the increase in intraocular pressure at specific perioperative time points in adult patients undergoing surgery in the Trendelenburg and prone positions. Methods. Comprehensive search strategies were used to identify eligible studies for two meta-analyses and to address the research questions. For each meta-analysis, standardized mean difference effect sizes were calculated for selected perioperative time points. Results. Using a random effects model, the meta-analysis examining the effect of Trendelenburg position, showed that intraocular pressure decreased significantly after induction and before arousal. Intraocular pressure increased significantly after abdominal insufflation and during Trendelenburg position. The meta-analysis examining the effect of prone position, showed that intraocular pressure increased significantly between induction of anesthesia and up to 10 minutes of prone position and continued to increase significantly until the end of the prone position. Conclusions. Intraocular pressure increases of the magnitude found in this research demonstrate the need for implementing interventions to reduce the risk for postoperative vision loss in patients undergoing surgery in the Trendelenburg and prone positions.Includes bibliographical reference

THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images

In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science