In vitro Charakterisierung des Migrationsverhaltens gegenüber SDF-1 und ATP von Zellen des humanen Knochenmarks

Im ischämischen Myokard kommt es durch Überexpression von SDF-1 zur Rekrutierung von Stammzellen aus dem Knochenmark. Auch Nukleotide wie ATP werden in den Extrazellulärraum freigesetzt. Die vorliegende Arbeit zeigt die selektive Migration von hämatopoetischen Stammzellen des Knochenmarks zu SDF-1 und ATP. CD133+ Stammzellen migrierten in vitro sowohl zu SDF-1, zu ATP und einer Kombination beider Faktoren. Viabilität und Koloniebildung von CD133+ Zellen wurden durch ATP nicht beeinträchtigt. BM-TNCs bzw. CD34+ Subpopulationen migrierten signifikant zu einer Kombination aus SDF-1 und ATP

Patient satisfaction with divided anesthesia care.

BACKGROUND Up to now, no prospective cohort study using a validated questionnaire has assessed patients' expectation and perception of divided anesthesia care and its influence on patient satisfaction. OBJECTIVE We assessed patient satisfaction with divided anesthesia care in a district general hospital in Switzerland. We hypothesized that patient expectations, combined with their perceptions of the (un)importance of continuous anesthesia care would influence patient satisfaction. MATERIAL AND METHODS A total of 484 eligible in-patients receiving anesthesia from October 2019 to February 2020 were included and received preoperative information about divided care via a brochure and face-to-face. The primary outcome was the assessment of patient satisfaction with divided anesthesia care using a validated questionnaire. In group 1 continuity of care was considered important but not performed. In group 2 continuity was ensured. In group 3 continuity was regarded as not important and was not performed. In group 4 patients could not remember or did not answer. A psychometrically developed validated questionnaire was sent to patients at home after discharge. RESULTS A total of 484 completed questionnaires (response rate 81%) were analyzed. In group 1 (n = 110) the mean total dissatisfaction score was 25% (95% confidence interval [CI] 21.8-28.1), in group 2 (n = 61) 6.8% (95% CI 4.8-8.7), in group 3 (n = 223) 12.1% (95% CI 10.7-13.4), and in group 4 (n = 90) 15% (95% CI 11-18); ANOVA: p < 0.001, η = 0.43. Of the patients 286 (59%) considered continuity of care by the same anesthetist relatively unimportant (34%) or not important at all (25%). The other 40% considered it important (22%) or very important (18%). CONCLUSION Despite receiving comprehensive preoperative information about divided anesthesia care, 40% of patients still considered continuity of care by the same anesthetist important. We recommend further research evaluating whether and how patient expectations can be modified towards the common practice of divided care and patient satisfaction can be increased

The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo

Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of about 800 ​pts/m2 and the oblique imagery used for 3D mesh texturing realizes a ground sampling distance of about 2–3 ​cm. This enables the identification of fine-grained structures and represents the state of the art in UAV-based mapping. iii) Both data modalities will be published for a total of three epochs allowing applications such as change detection. The dataset depicts the village of Hessigheim (Germany), henceforth referred to as H3D - either represented as 3D point cloud H3D(PC) or 3D mesh H3D(Mesh). It is designed to promote research in the field of 3D data analysis on one hand and to evaluate and rank existing and emerging approaches for semantic segmentation of both data modalities on the other hand. Ultimately, we hope that H3D will become a widely used benchmark dataset in company with the well-established ISPRS Vaihingen 3D Semantic Labeling Challenge benchmark (V3D). The dataset can be downloaded from https://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx

The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo

Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of about 800 ​pts/m2 and the oblique imagery used for 3D mesh texturing realizes a ground sampling distance of about 2–3 ​cm. This enables the identification of fine-grained structures and represents the state of the art in UAV-based mapping. iii) Both data modalities will be published for a total of three epochs allowing applications such as change detection. The dataset depicts the village of Hessigheim (Germany), henceforth referred to as H3D - either represented as 3D point cloud H3D(PC) or 3D mesh H3D(Mesh). It is designed to promote research in the field of 3D data analysis on one hand and to evaluate and rank existing and emerging approaches for semantic segmentation of both data modalities on the other hand. Ultimately, we hope that H3D will become a widely used benchmark dataset in company with the well-established ISPRS Vaihingen 3D Semantic Labeling Challenge benchmark (V3D). The dataset can be downloaded from https://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx.Urban Data Scienc