Craniocervical manual lymphatic drainage and its impact on intracranial pressure - a pilot study.

BACKGROUND AND PURPOSE: Theoretical considerations and the results of animal studies indicate that manual lymphatic drainage (MLD) might have an impact on intracranial pressure (ICP). There is a lack of clinically qualitative investigations on patients with severe cerebral diseases. METHODS: Between April 2013 and January 2015 a prospective observational study was performed on patients who were undergoing intracranial pressure measurement and treatment with MLD. ICP, cerebral perfusion pressure, mean arterial pressure (MAP), heart rate and oxygen saturation were recorded continuously 15 min before the procedure, during MLD (22 min) and for 15 min after the procedure. For analysis the data treatment units were divided into two groups: patients with a mean baseline ICP <15 mmHg (group 1) and patients with a mean ICP ≥15 mmHg before MLD (group 2). RESULTS: A total of 133 treatment units (61 patients) were analysed (group 1 n = 99; group 2 n = 34). The mean baseline ICP was 10.4 mmHg overall, and 8.3 mmHg and 18.6 mmHg respectively in group 1 and group 2; ICP significantly decreased during therapy with MLD and this persisted during the follow-up period in group 2. MAP did not show any significant differences between the different periods. CONCLUSIONS: Our data showed a significant reduction of ICP during therapy with craniocervical MLD in patients with severe cerebral diseases.

AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research.

A major challenge in data-driven biomedical research lies in the collection and representation of data provenance information to ensure that findings are reproducibile. In order to communicate and reproduce multi-step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node-link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach, we reduce the complexity of the graph using hierarchical and motif-based aggregation. Based on user action and graph attributes, a modular degree-of-interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest-driven adaptive approach to provenance visualization allows users to review and communicate complex multi-step analyses, which can be based on hundreds of files that are processed by numerous workflows. We have integrated our approach into an analysis platform that captures extensive data provenance information, and demonstrate its effectiveness by means of a biomedical usage scenario.