Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) was classified by the International Agency for Research on Cancer as a carcinogen in humans. It acts through an aryl hydrocarbon receptor-mediated mechanism, inducing the transcription of numerous genes, including various cytochrome P450s (CYPs - CYP1A1, 1A2, 1B1). Induction of CYPs may lead to genotoxicity by generating reactive oxygen species (ROS) which can damage DNA directly and/or via the generation of reactive metabolites. We determined ROS formation with the 2',7'-dihydrodichlorofluorescein diacetate fluorescence assay after incubation of HepG2 hepatoma cells or primary rat hepatocytes with TCDD. The amount of 8-oxo-2'-deoxyguanosine (8-oxo-dG) in DNA was measured using HPLC-MS/MS, the amount of CYP1A1 protein by Western blotting. The catalytic activity of CYP1A enzymes was determined as 7-ethoxyresorufin-O-deethylase (EROD) activity. Incubation of cells with TCDD for 48 h caused increased levels of ROS in primary rat hepatocytes as well as increased levels of 8-oxo-dG in DNA compared to untreated cells. In the HepG2 cell line no significant effects were observed for both ROS formation and 8-oxo-dG levels. Both effects were in good agreement with the extent of induction of CYP1A1 protein and EROD activity, suggesting that CYP1 induction is a major source of ROS formation in TCDD-treated hepatocytes.

Background Patients with rare diseases (RDs) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnosis in RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium, which is one of four funded consortia in the German Medical Informatics Initiative, will develop a CDSS for RDs based on distributed clinical data from ten university hospitals. This qualitative study aims to investigate (1) the relevant organizational conditions for the operation of a CDSS for RDs when diagnose patients (e.g. the diagnosis workflow), (2) which data is necessary for decision support, and (3) the appropriate user group for such a CDSS. Methods Interviews were carried out with RDs experts. Participants were recruited from staff physicians at the Rare Disease Centers (RDCs) at the MIRACUM locations, which offer diagnosis and treatment of RDs. An interview guide was developed with a category-guided deductive approach. The interviews were recorded on an audio device and then transcribed into written form. We continued data collection until all interviews were completed. Afterwards, data analysis was performed using Mayring’s qualitative content analysis approach. Results A total of seven experts were included in the study. The results show that medical center guides and physicians from RDC B-centers (with a focus on different RDs) are involved in the diagnostic process. Furthermore, interdisciplinary case discussions between physicians are conducted. The experts explained that RDs exist which cannot be fully differentiated, but rather described only by their overall symptoms or findings: diagnosis is dependent on the disease or disease group. At the end of the diagnostic process, most centers prepare a summary of the patient case. Furthermore, the experts considered both physicians and experts from the B-centers to be potential users of a CDSS. The experts also have different experiences with CDSS for RDs. Conclusions This qualitative study is a first step towards establishing the requirements for the development of a CDSS for RDs. Further research is necessary to create solutions by also including the experts on RDs.

Background Patients with a rare disease (RD) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnostic process in rare diseases (RDs). The MIRACUM (Medical Informatics in Research and Medicine) consortium, which is one of four funded consortia in the German Medical Informatics Initiative, will develop a CDSS for RDs based on distributed clinical data from ten university hospitals. This qualitative study aims to investigate (1) the relevant organizational conditions for the operation of a CDSS for RDs, (2) which data is necessary for decision support, and (3) the appropriate user group for such a CDSS. Methods Interviews were carried out with RDs experts. Participants were recruited from staff physicians at the Rare Disease Centers (RDCs) at the MIRACUM locations, which offer diagnosis and treatment of RDs. An interview guide was developed with a category-guided deductive approach. The interviews were recorded on an audio device and then transcribed into written form. We continued data collection until all interviews were completed. Afterwards, data analysis was performed using Mayring’s qualitative content analysis approach. Results A total of seven experts were included in the study, from seven of the eight MIRACUM locations which have established an RDC. The results show that administrative staff and physicians from RDC B-centers, representing different medical specialties, are involved in the diagnostic process. The experts cited various software programs used for diagnostic support and considered both physicians and experts from the B-centers to be potential users of a CDSS. Furthermore, the experts explained that RDs exist which cannot be fully differentiated, but rather described only by their overall symptoms or findings: diagnosis is dependent on the disease or disease group. At the end of the diagnostic process, most centers prepare a summary of the patient case. Conclusions This qualitative study is a first step towards establishing the requirements for the development of a CDSS for RDs. However, further research is necessary to create solutions by also including the experts on RDs.

Receptor-mediated endocytosis is an essential process in signaling pathways for an activation of intracellular signaling cascades. One example is the Wnt signaling pathway, which seems to depend on endocytosis of the ligand-receptor complex for initiation of Wnt signal transduction. So far, the role of different endocytic pathways in Wnt signaling, the molecular players and the kinetics of this process are unclear.Here, we monitor endocytosis in Wnt3a and Wnt5a mediated signaling by membrane capacitance recordings of HEK293 cells. Our measurements revealed a fast and substantial increase in the number of endocytic vesicles. This endocytotic activity is specifically elicited by extracellular Wnt ligands; it starts immediately upon ligand binding and ceases over a period of ten minutes. By using specific inhibitors, we can dissect Wnt induced endocytosis into two independent pathways, where canonical Wnt3a is taken up mainly by clathrin-independent endocytosis and Wnt5a exclusively by clathrin-mediated endocytosis.