Background and Objectives: In this study, the spatial courses of the connecting tubule (CNT) of nephron from different depth of cortex and the collecting duct (CD) in mouse kidney was established with the aid of three-dimensional visualization technology. Subjects and Methods: Kidneys from three C57/BL/6J mice were removed after perfusion fixation. The tissue blocks were cut perpendicular to the longitudinal axis of the kidney and embedded in Epon-812. A total of 2000, 2.5-μm-thick consecutive sections were obtained from the renal capsule to papilla. After acquiring the digitalized images and alignment, the CNT from 137 nephrons were traced with the custom-made programs. The spatial arrangement of the CNT was visualized, and the length was measured. Results: Each CD received CNT from 5 to 7 nephrons. The CNT from different level of cortex all drained into the CD at superficial cortex but took different path. The CNT from superficial cortical nephron joined CD directly, while the CNT from middle and juxtamedullary cortical nephron joined each other to form an arcade, and the latter drained into CD at superficial cortex. The length of the arcade ranged 400–800 μm. Conclusions: The CNT joined CD at superficial cortex, which means the fluid along the CD from the cortex would not be added from outside, contributing to stabilization of the hormone regulation in the transportation of solutes and water along the CD from cortex to medulla.

Background and Objectives: Current developments in electroencephalography (EEG) foster medical and nonmedical applications outside the hospitals. For example, continuous monitoring of mental and cognitive states can contribute to avoid critical and potentially dangerous situations in daily life. An important prerequisite for successful EEG at home is a real-time classification of mental states. In this article, we compare different machine learning algorithms for the classification of eye states based on EEG recordings. Materials and Methods: We tested 23 machine learning algorithms from the Waikato Environment for Knowledge Analysis toolkit. Each classifier was analyzed on four different datasets, since two separate approaches – called sample-wise and segment-wise – in combination with raw and filtered data were applied. These datasets were recorded for 27 volunteers. The different approaches are compared in terms of accuracy, complexity, training time, and classification time. Results: Ten out of 23 classifiers fulfilled the determined requirements of high classification accuracy and short time of classification and can be denoted as applicable for real-time EEG eye state classification. Conclusions: We found that it is possible to predict eye states using EEG recordings with an accuracy from about 96% to over 99% in a real-time system. On the other hand, we found no best, universal method of classifying EEG eye states in all volunteers. Therefore, we conclude that the best algorithm should be chosen individually, using the optimal classification accuracy in combination with time of classification as the criterion.

For decision support, a globally connected digital information system is desirable, which uses diagnostic findings and makes language independently statistical (anonymized) information from similar cases of all countries available. It can be realized efficiently in the following way: The definitions of all used diagnostics and measurement procedures are placed online. The defined data are called “Domain Vectors.” Doctors who use the online definitions get measurement results as Domain Vectors in comparable and searchable form. Anonymized selective statistics over patient groups with similar data can help to find the best treatment. Precondition for such distributed and simultaneously connected Domain Vectors are their global online definitions. Every Domain Vector only consists of a link to its definition (e.g., via URL or an abbreviated equivalent) plus numbers. This article explains details and concludes that introduction of the Domain Vectors with their online definitions would be an important step toward internationally connected medicine.

Rapid developments in sensing and imaging technologies are transforming our ability to detect, diagnose, and manage disease. Given financial pressures on managed health care, there is increasing emphasis on disease prevention and wellness programs have become a feature of many plans. Recent market developments include the merger of pharmacy and health-care organizations, which promises to open new avenues in health maintenance and diagnosis. Herein, we review recent developments in the field and present a vision for how point-of-care providers can play a pivotal role in prodromal diagnostics and wellness programs. Emphasis is placed on recent advances in digital detection technologies which have the potential to accelerate evolution of such models. An additional potential benefit of large-scale community-based screening centers lies in the identification of patients for recruitment into clinical trials, and mechanisms are proposed.