Motile cilia are cellular organelles that generate directional fluid flow across various epithelial surfaces including the embryonic node and respiratory mucosa. The proper functioning of cilia is necessary for normal embryo development and, for the respiratory system, the clearance of mucus and potentially harmful particulate matter. Here we show that optical coherence tomography (OCT) is well-suited for quantitatively characterizing the microfluidic-scale flow generated by motile cilia. Our imaging focuses on the ciliated epithelium of Xenopus tropicalis embryos, a genetically manipulable and experimentally tractable animal model of human disease. We show qualitative flow profile characterization using OCT-based particle pathline imaging. We show quantitative, two-dimensional, two-component flow velocity field characterization using OCT-based particle tracking velocimetry. Quantitative imaging and phenotyping of cilia-driven fluid flow using OCT will enable more detailed research in ciliary biology and in respiratory medicine.

Preparing medical students for the takeover or the start-up of a medical practice is an important challenge in Germany today. Therefore, this paper presents a computer-aided serious game (eMedOffice) developed and currently in use at the RWTH Aachen University Medical School. The game is part of the attempt to teach medical students the organizational and conceptual basics of the medical practice of a general practitioner in a problem-based learning environment. This paper introduces methods and concepts used to develop the serious game and describes the results of an evaluation of the game's application in curricular courses at the Medical School.

Objective Morbidity and mortality due to preeclampsia in settings with limited resources often results from delayed diagnosis. The Congo Red Dot (CRD) test, a simple modality to assess the presence of misfolded proteins in urine, shows promise as a diagnostic and prognostic tool for preeclampsia. We propose an innovative mobile health (mHealth) solution that enables the quantification of the CRD test as a batch laboratory test, with minimal cost and equipment. Methods A smartphone application that guides the user through seven easy steps, and that can be used successfully by non-specialized personnel, was developed. After image acquisition, a robust analysis runs on a smartphone, quantifying the CRD test response without the need for an internet connection or additional hardware. In the first stage, the basic image processing algorithms and supporting test standardizations were developed using urine samples from 218 patients. In the second stage, the standardized procedure was evaluated on 328 urine specimens from 273 women. In the third stage, the application was tested for robustness using four different operators and 94 altered samples. Results In the first stage, the image processing chain was set up with high correlation to manual analysis (z-test P < 0.001). In the second stage, a high agreement between manual and automated processing was calculated (Lin’s concordance coefficient ρ_c = 0.968). In the last stage, sources of error were identified and remedies were developed accordingly. Altered samples resulted in an acceptable concordance with the manual gold-standard (Lin’s ρ_c = 0.914). Conclusion Combining smartphone-based image analysis with molecular-specific disease features represents a cost-effective application of mHealth that has the potential to fill gaps in access to health care solutions that are critical to reducing adverse events in resource-poor settings.

In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user’s hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of $98.30\%$ ( $\pm 1.26\%$ ) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills.

Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel’s holiday planning. We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May–September). An average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets. The proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.