University of Würzburg

About: University of Würzburg is a education organization based out in Wurzburg, Bayern, Germany. It is known for research contribution in the topics: Population & Gene. The organization has 31437 authors who have published 62203 publications receiving 2337033 citations. The organization is also known as: Julius-Maximilians-Universität Würzburg & Würzburg University.

Vulnerability of the nigrostriatal system as detected by transcranial ultrasound

Abstract: Objective: To assess the incidence of a hyperechogenic substantia nigra (SN) by transcranial sonography (TCS) in healthy people and to evaluate whether an enlarged hyperechogenic SN area is associated with functional impairment of the nigrostriatal system. Background and Methods: Until now, preclinical impairment of the nigrostriatal system could be identified only by functional neuroimaging techniques such as PET in selected groups of patients. TCS is a new, noninvasive ultrasound technique that has demonstrated an increased echogenicity of the SN in patients with PD, whereas in most healthy individuals, the SN is either barely detectable or undetectable by TCS. Results: Of 330 healthy volunteers, 8.6% exhibited an increased echogenicity of the SN. From these, 10 clinically healthy individuals with distinct unilateral or bilateral hyperechogenic signals in the SN region (SN area above 0.25 cm 2 ) underwent comprehensive motor testing, neuropsychological assessment, MRI, and [ 18 F]-dopa PET examination. With regard to motor functions, these individuals did not differ from 10 age- and sex-matched controls with a low echogenic SN and an area of echogenic signals below 0.2 cm 2 . Enlargement of hyperechogenic areas in the 10 healthy individuals was associated with a marked decrease in the accumulation of [ 18 F]-dopa in the caudate nucleus and putamen. Conclusions: Substantia nigra hyperechogenicity appears to indicate a functional impairment of the nigrostriatal system. Transcranial sonography may be a suitable method of identifying persons at risk of nigrostriatal alterations, making possible the introduction of early neuroprotective therapy.

Phase III Study of Weekly High-Dose Infusional Fluorouracil Plus Folinic Acid With or Without Irinotecan in Patients With Metastatic Colorectal Cancer: European Organisation for Research and Treatment of Cancer Gastrointestinal Group Study 40986

Abstract: Purpose To demonstrate that adding irinotecan to a standard weekly schedule of high-dose, infusional fluorouracil (FU) and leucovorin (folinic acid [FA]) can prolong progression-free survival (PFS) Patients and Methods Four hundred thirty patients with measurable or assessable metastatic colorectal cancer were randomly assigned to receive either FA 500 mg/m2 as a 2-hour infusion and FU 26 g/m2 by intravenous 24-hour infusion, both administered weekly for 6 weeks, followed by a 2-week rest (Arbeitsgemeinschaft fur Internistische Onkologie [AIO] arm, n = 216), or a similar schedule but with FU 23 or 20 g/m2 preceded by irinotecan 80 mg/m2 administered over 30 minutes (experimental group, n = 214) Results The median PFS time in the experimental group was 85 months (95% CI, 76 to 99 months) compared with 64 months (95% CI, 53 to 72 months) in the AIO arm (P < 0001) The median overall survival time was increased from 169 to 201 months (P = 2779) The objective response rate was 622% (95% CI, 5

A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios

Abstract: Autonomous vehicle navigation is challenging since various types of road scenarios in real urban environments have to be considered, particularly when only perception sensors are used, without position information. This paper presents a novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data. Our system uses a multisensory scheme to cover the most drivable areas in front of a vehicle. We propose a feature-level fusion method for the LIDAR and vision data and an optimal selection strategy for detecting the best drivable region. Then, a conditional lane detection algorithm is selectively executed depending on the automatic classification of the optimal drivable region. Our system successfully handles both structured and unstructured roads. The results of several experiments are provided to demonstrate the reliability, effectiveness, and robustness of the system.