Exploring Dimensionality Reduction Effects in Mixed Reality for Analyzing Tinnitus Patient Data

In the context of big data analytics, gaining insights into high-dimensional data sets can be properly achieved, inter alia, by the use of visual analytics. Current developments in the field of immersive analytics, mainly driven by the improvements of smart glasses and virtual reality headsets, are one enabler to enhance user-friendly and interactive ways for data analytics. Along this trend, more and more fields in the medical domain crave for this type of technology to analyze medical data in a new way. In this work, a mixed-reality prototype is presented that shall help tinnitus researchers and clinicians to analyze patient data more efficiently. In particular, the prototype simplifies the analysis on a high-dimensional real-world tinnitus patient data set by the use of dimensionality reduction effects. The latter is represented by resulting clusters, which are identified through the density of particles, while information loss is denoted as the remaining covered variance. Technically, the graphical interface of the prototype includes a correlation coefficient graph, a plot for the information loss, and a 3D particle system. Furthermore, the prototype provides a voice recognition feature to select or deselect relevant data variables by its users. Moreover, based on a machine learning library, the prototype aims at reducing the computational resources on the used smart glasses. Finally, in practical sessions, we demonstrated the prototype to clinicians and they reported that such a tool may be very helpful to analyze patient data on one hand. On the other, such system is welcome to educate unexperienced clinicians in a better way. Altogether, the presented tool may constitute a promising direction for the medical as well as other domains

HOLOVIEW: Exploring Patient Data in Mixed Reality

Over the last years, the TrackYourTinnitus project collected data from worldwide tinnitus patients using smart mobile devices. The gathered data set, in turn, is high-dimensional and it is therefore challenging to visualize it for analyzing purposes. To remedy this drawback, a 3D approach that applies the Microsoft HoloLens is proposed. More specifically, we visualize tinnitus records as a hologram, which is augmented by the real world. The developed prototype particularly tackles three challenges in the context of analyzing the TrackYourTinnitus data set visually. First, the detection of correlations between dimensions is simplified by highlighting the relations between the diagram axes and visually displaying the correlation coefficient. Second, an outlier detection method reveals striking data points and, third, a clustering approach allows for the recognition of related data points. Finally, the performance of the prototype can be controlled by subsampling the data set in order to receive different types of resolutions. Therefore, the prototype is able to handle large data sets

Einstellung zu Gastarbeitern: Ergebnisse einer Forschungsübung an der Universität zu Köln ; Sekundäranalyse von Umfragedaten des Zentralarchivs

Sekundäranalyse von Daten, die 1980 mit dem ALLBUS des Zentralarchivs (ZA) bei einer repräsentativen Stichprobe von ca. 3.000 Personen erhoben wurden. Die Einstellung gegenüber Gastarbeitern wurde anhand der Bewertung von Statements erhoben und wird nach Alter, Schulbildung, politischer Einstellung und Kontaktverhalten (speziell gegenüber Gastarbeitern) der Befragten differenziert. (AR

Balanced hydroxyethylstarch (HES 130/0.4) impairs kidney function in-vivo without inflammation

Volume therapy is a standard procedure in daily perioperative care, and there is an ongoing discussion about the benefits of colloid resuscitation with hydroxyethylstarch (HES). In sepsis HES should be avoided due to a higher risk for acute kidney injury (AKI). Results of the usage of HES in patients without sepsis are controversial. Therefore we conducted an animal study to evaluate the impact of 6% HES 130/0.4 on kidney integrity with sepsis or under healthy conditions Sepsis was induced by standardized Colon Ascendens Stent Peritonitis (sCASP). sCASP-group as well as control group (C) remained untreated for 24 h. After 18 h sCASP+HES group (sCASP+VOL) and control+HES (C+VOL) received 50 ml/KG balanced 6% HES (VOL) 130/0.4 over 6h. After 24h kidney function was measured via Inulin- and PAH-Clearance in re-anesthetized rats, and serum urea, creatinine (crea), cystatin C and Neutrophil gelatinase-associated lipocalin (NGAL) as well as histopathology were analysed. In vitro human proximal tubule cells (PTC) were cultured +/- lipopolysaccharid (LPS) and with 0.1–4.0% VOL. Cell viability was measured with XTT-, cell toxicity with LDH-test. sCASP induced severe septic AKI demonstrated divergent results regarding renal function by clearance or creatinine measure focusing on VOL. Soleley HES (C+VOL) deteriorated renal function without sCASP. Histopathology revealed significantly derangements in all HES groups compared to control. In vitro LPS did not worsen the HES induced reduction of cell viability in PTC cells. For the first time, we demonstrated, that application of 50 ml/KG 6% HES 130/0.4 over 6 hours induced AKI without inflammation in vivo. Severity of sCASP induced septic AKI might be no longer susceptible to the way of volume expansio

Cardiovascular risk prediction - a systems medicine approach

Background Guidelines for the prevention of cardiovascular disease (CVD) have recommended the assessment of the total CVD risk by risk scores. Current risk algorithms are low in sensitivity and specificity and they have not incorporated emerging risk markers for CVD. We suggest that CVD risk assessment can be still improved. We have developed a long-term risk prediction model of cardiovascular mortality in patients with stable coronary artery disease (CAD) based on newly available machine learning and on an extended dataset of new biomarkers.Methods 2953 participants of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study were included. 184 laboratory and 21 demographic markers were ranked according to their contribution to risk of cardiovascular (CV) mortality using different data mining approaches. A self-learning bioinformatics workflow, including seven different machine learning algorithms, was developed for CV risk prediction. The study population was stratified into patients with and without significant CAD. Thereby, significant CAD was defined as a lumen narrowing of 50 or more in at least one of the coronary segments or a history of definite myocardial infarction. The machine learning models in both subpopulations were compared with established CV risk assessment tools.Results After a follow-up of 10 years, 603 (20.4%) patients died of cardiovascular causes. 95% patients without CAD deceased within ten years and 247 (13.2 %) patients with CAD within 5 years. Overall and in patients without CAD, NT-proBNP (N-terminal pro B-type natriuretic peptide), TnT (Troponin T), estimated cystatin c based GFR (glomerular filtration rate) and age were the highest ranked predictors, while in patients with CAD, NT-proBNP, GFR, CT-proAVP (C-terminal pro arginine vasopressin) and TNT were highest predictive. In the comparison with the FRS, PROCAM and ESC risk scores, the machine learning workflow produced more accurate and robust CV mortality prediction in patients without CAD. Equivalent CV risk prediction was obtained in the CAD subpopulation in comparison with the Marschner risk score. Overall, the existing algorithms in general tend to assign more patients into the medium risk groups, while the machine learning algorithms tend to have a clearer risk/no risk assignment. The framework is available upon request.Conclusion We have developed a fully automated and self-validating computational framework of machine learning techniques using an extensive database of clinical, routinely and non-routinely measured laboratory data. Our framework predicts long-term CV mortality at least as accurate as existing CVD risk scores. A combination of four highly ranked biomarkers and the random forest approach showed the best predictive results. Moreover, a dynamic computational model has several advantages over static CVD risk prediction tools: it is freeware, transparent, variable, transferable and expandable to any population, types of events and time frames

Low testosterone levels predict all-cause mortality and cardiovascular events in women: a prospective cohort study in German primary care patients

Objective: Although associations between testosterone and cardiovascular (CV) morbidity in women have been proposed, no large prospective study has evaluated potential associations between testosterone and mortality in women. The objective was to determine whether baseline testosterone levels in women are associated with future overall or CV morbidity and mortality. Design: Prospective cohort study with a 4.5-year follow-up period. Methods: From a representative sample of German primary care practices, 2914 female patients between 18 and 75 years were analyzed for the main outcome measures: CV risk factors, CV diseases, and all-cause mortality. Results: At baseline, the study population was aged 57.96±14.37 years with a mean body mass index of 26.71±5.17 kg/m2. No predictive value of total testosterone for incident CV risk factors or CV diseases was observed in logistic regressions. Patients with total testosterone levels in the lowest quintile Q1, however, had a higher risk to die of any cause or to develop a CV event within the follow-up period compared to patients in the collapsed quintiles Q2–Q5 in crude and adjusted Cox regression models (all-cause mortality: Q2–Q5 versus Q1: crude hazard ratios (HR) 0.49, 95% confidence interval (CI) 0.33–0.74; adjusted HR 0.62, 95% CI 0.42–0.939; CV events: Q2–Q5 versus Q1: crude HR 0.54, 95% CI 0.38–0.77; adjusted HR 0.68, 95% CI 0.48–0.97). Kaplan–Meier curves revealed similar data. Conclusions: Low baseline testosterone in women is associated with increased all-cause mortality and incident CV events independent of traditional risk factors

Unmet needs in the diagnosis and treatment of dyslipidemia in the primary care setting in Germany

Objectives and methods: DETECT is a cross-sectional study of 55,518 unselected consecutive patients in 3188 representative primary care offices in Germany. In a random subset of 7519 patients, an extensive standardized laboratory program was undertaken. The study investigated the prevalence of cardiovascular disease, known risk factors (such as diabetes, hypertension and dyslipidemia and their co-morbid manifestation), as well as treatment patterns. The present analysis of the DETECT laboratory dataset focused on the prevalence and treatment of dyslipidemia in primary medical care in Germany. Coronary artery disease (CAD), risk categories and LDL-C target achievement rates were determined in the subset of 6815 patients according to the National Cholesterol Education Program (NCEP) ATP III Guidelines. Results: Of all patients, 54.3% had dyslipidemia. Only 54.4% of the NCEP-classified dyslipidemic patients were diagnosed as ‘dyslipidemic’ by their physicians. Only 27% of all dyslipidemic patients (and 40.7% of the recognized dyslipidemic patients) were treated with lipid-lowering medications, and 11.1% of all dyslipidemic patients (41.4% of the patients treated with lipid-lowering drugs) achieved their LDL-C treatment goals. In conclusion, 80.3% of patients in the sample with dyslipidemia went undiagnosed, un-treated or under-treated

Specific binding of a hexanucleotide to HIV-1 reverse transcriptase: a novel class of bioactive molecules

Short oligonucleotides below 8–10 nt in length adopt relatively simple structures. Accordingly, they represent interesting and so far unexplored lead compounds as molecular tools and, potentially, for drug development as a rational improvement of efficacy seem to be less complex than for other classes of longer oligomeric nucleic acid. As a ‘proof of concept’, we describe the highly specific binding of the hexanucleotide UCGUGU (Hex-S3) to human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) as a model target. Ultraviolet (UV) cross-linking studies and competition experiments with primer/template substrates and a RT-directed aptamer suggest site-specific binding of Hex-S3 to the large subunit (p66) of the viral enzyme. The affinity of 5.3 μM is related to hexanucleotide-specific suppression of HIV-1 replication in human cells by up to three orders of magnitude indicating that Hex-S3 exerts specific and biologically relevant activity. Experimental evidence described here further suggests a systematic hexamer array-based search for new tools for molecular biology and novel lead compounds in nucleic acid-based drug development