Institute for Medical Informatics

Sankt Radegund bei Graz, Austria

Institute for Medical Informatics

Sankt Radegund bei Graz, Austria
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Leushacke M.,Singapore Institute of Medical Biology | Ng A.,Singapore Institute of Medical Biology | Galle J.,The Interdisciplinary Center | Loeffler M.,Institute for Medical Informatics | And 3 more authors.
Cell Reports | Year: 2013

The pyloric epithelium continuously self-renews throughout life, driven by limited reservoirs of resident Lgr5+ adult stem cells. Here, we characterize the population dynamics of these stem cells during epithelial homeostasis. Using a clonal fate-mapping strategy, we demonstrate that multiple Lgr5+ cells routinely contribute to epithelial renewal in the pyloric gland and, similar to what was previously observed in the intestine, a balanced homeostasis of the glandular epithelium and stem cell pools is predominantly achieved via neutral competition between symmetrically dividing Lgr5+ stem cells. Additionally, we document a lateral expansion of stem cell clones via gland fission under nondamage conditions. These findings represent a major advance in our basic understanding of tissue homeostasis in the stomach and form the foundation for identifying altered stem cell behavior during gastric disease

SHAPE, the Society for Heart Attack Prevention and Eradication (, a nonprofit grassroots organization dedicated to the mission of eradicating heart attacks, today announced the agenda of its first focus group meeting on prediction of near-future heart attacks using artificial intelligence. The meeting is led by Dr. Morteza Naghavi the founder and executive director of SHAPE and features leading cardiovascular researchers from around the world.. This will be the 20th scientific meeting held by SHAPE since 2001. Detailed agenda of the meeting is shown below. The First Machine Learning Vulnerable Patient Symposium A Focus Group Meeting on Developing an Artificial Intelligence-based Forecast System A Satellite Event in Conjunction with 2016 Annual Scientific Sessions of American Heart Association This event is open to public. Participation via GoToMeeting can be requested. Dinner will be served 7:30 PM. This is the 20th Vulnerable Plaque & Vulnerable Patient Symposium held by SHAPE since 2001. Welcome: Morteza Naghavi, M.D. Founder of SHAPE and Executive Chairman of the SHAPE Task Force Opening Remarks: Valentin Fuster, M.D., Ph.D. Professor of Medicine and Physician-in-Chief, Mount Sinai Hospital and Icahn School of Medicine Jagat Narula M.D., Ph.D. Chief of Cardiology, Mount Sinai West & St. Luke’s Hospitals Associate, Dean, Arnhold Institute for Global Health at Mount Sinai Icahn School of Medicine Ioannis Kakadiaris, Ph.D. Professor of Computer Science and Biomedical Engineering, Director of Machine Learning Laboratory University of Houston Topic: What is Machine Learning and How Can It Shape the Future of Healthcare? Invited Online Presentations: Two Examples of Machine Learning Studies in CVD Risk Assessment (10 minutes each) CVD prediction using support vector machine in a large Australian cohort. Dinesh Kumar, Ph.D. and Sridhar Arjunan, Ph.D. Biosignals Lab, School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia (2) Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large clinical population Piotr Slomka, Ph.D. Chief Scientist, Artificial Intelligence in Medicine Program, Department of Imaging Cedars-Sinai Medical Center, Professor, UCLA School of Medicine, Los Angeles, CA Moderated Discussions on the Vulnerable Patient Project Machine Learning for Prediction of Near-Term CHD Events All investigators will be asked to give a very brief introduction of their study and how it can fit in Background: Imagine instead of the existing daily weather forecasts and hurricane alerts we were told the probability of a storm within the next 10 years! This is how heart attacks are predicted today. We teach our physicians to calculate the 10-year probability of a heart attack and sudden cardiac death based on their patients’ risk factors. Long term predictions do not trigger immediate preventive actions. Although some people develop warning symptoms, half of men and two-thirds of women who die suddenly of coronary heart disease (CHD) have no previous symptoms. Imagine if we could alert people months, weeks, or even days before a heart attack and trigger immediate preventive actions. The Idea: Use machine learning to create new algorithms to detect who will experience a CHD event within a year (The Vulnerable Patient). Algorithms will be based on banked biospecimen and information collected days up to 12 months prior to the event. We will utilize existing cohorts such as MESA, Heinz Nixdorf Recall Study, Framingham Heart Study, BioImage Study and the Dallas Heart Study. External validation to test for discrimination and calibration will be conducted using other longitudinal observational studies that provide adjudicated cardiovascular event information such as the MiHeart, JHS, DANRISK and ROBINSCA. Additionally, we will use machine learning to characterize individuals who, despite high conventional risk, have lived over 80 years with no CHD events (The Invulnerable ). We expect to discover new targets for drug and possibly vaccine development. We will make the algorithms available as an open source tool to collect additional data over time and increase its predictive value. Organizers: SHAPE as the originating and organizing center for the entire project, recruiting new studies and biobanks, conducting workshops with researchers from each study, fundraising, creating an open source platform community for future enhancement and collaborations. Stanford as the coordinating center for collecting data and samples, and basic science labs. Mount Sinai as the data review and publication center. Machine Learning Lab to be decided, either Google, Apple, IBM, Facebook, Amazon or wherever we find a strong industry partner or sponsor. Director, Cardiac Computed Tomography, Associate Professor of Medicine, Johns Hopkins University Division of Cardiology, The Johns Hopkins Hospital Imagine the new machine learning Vulnerable Patient detection algorithm (heart attack forecaster) is created and validated. If studies confirm the algorithm is able to detect the Vulnerable Patient with 50% or more certainty. In other words, 1 out of 2 patients classified as Vulnerable Patient goes to have an ASCVD event in the following 12 months. Now the questions are: A)    What preventive actions would you take if your asymptomatic patient tested positive as a Vulnerable Patient? B)    What preventive actions would you take if the patient was you?! (This question is meant to circumvent regulatory and financial limitations that may apply to your patients but may not hold you back). Moderators will invite comments from all participants in the meeting. Invited Key Opinion Leaders (Alphabetic Order) Arthur Agatston, M.D. Founder of South Beach Diet, Director of Wellness at Baptist Hospital and Professor of Medicine at University of Miami, FL Daniel Berman, M.D. Professor of Medicine at UCLA, Director of Cardiac Imaging and Nuclear Cardiology at Cedars-Sinai, Los Angeles, CA Michael Blaha, M.D., M.P.H., Director of Clinical Research, Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University, Baltimore, MD Mathew Budoff, M.D. Professor of Medicine and Director of Preventive Cardiology, UCLA Harbor, Los Angeles, CA Adolfo Correa, M.D., Ph.D. Chief Science Officer, Jackson Heart Study, Professor of Medicine and Pediatrics, University of Mississippi, Jackson, MS Rahul Deo, M.D., Ph.D. Assistant Professor of Medicine, Division of Cardiology, University of California, San Francisco, CA Raimund Erbel, M.D. Professor of Medicine, Chief of Cardiology and Director of West German Heart Centre, University Essen, Germany Sergio Fazio, M.D., Ph.D. Chair of Preventive Cardiology and Professor of Medicine, Oregon Health and Science University, Portland, OR Zahi Fayad, M.D. Professor of Radiology and Medicine (Cardiology), Director of the Translational and Molecular Imaging Institute, Mount Sinai Hospital, New York, NY Philip Greenland, M.D., Professor of Cardiology, Director, Institute for Public Health and Medicine, Center for Population Health Sciences, Chicago, IL Robert Harrington, M.D. Chair of the Department of Medicine, Professor of Medicine, Stanford University School of Medicine, Stanford, CA Harvey Hecht, M.D., Director of Cardiac CT Imaging Laboratory, Mount Sinai School of Medicine, New York, NY Karl-Heinz Jöckel, Ph.D. Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Germany Ioannis Kakadiaris, Ph.D. Professor of Computer Science and Biomedical Engineering, University of Houston, Houston, TX Stanley Kleis, Ph.D. Professor of Mechanical Engineering and Biomedical Engineering, University of Houston, Houston, TX Tatiana Kuznetsova, M.D. Professor and Director, Hypertension and Cardiovascular Epidemiology, University of Leuven, Leuven, Belgium Daniel Levy, M.D. Director of Framingham Heart Study, and Intramural Investigator, National Institute of Health, Bethesda, MD Roxana Mehran, M.D. Professor of Medicine and Director of Interventional Clinical Trials, Mount Sinai School of Medicine, New York, NY Ralph Metcalfe, Ph.D. Professor of Mechanical and Biomedical Engineering, University of Houston, Houston, TX Susanne Moebus, Ph.D., M.P.H. Biologist & Epidemiologist, Head of the Centre for Urban Epidemiology, University Essen, Germany Morteza Naghavi, M.D. Founder and Executive Chairman of the SHAPE Task Force, President of MEDITEX, Houston, TX Tasneem Z. Naqvi, M.D. Professor of Medicine and Director of Echocardiography, College of Medicine, May Clinic, Scottsdale, AZ Jagat Narula, M.D., Ph.D. Associate Dean for Global Affairs, Professor of Medicine (Cardiology), Mount Sinai Hospital and School of Medicine, New York, NY Ulla Roggenbuck, Ph.D. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany Henrik Sillesen, M.D. Professor and Head of Dept. of Vascular Surgery, Rigs Hospitalet, University of Copenhagen, Copenhagen, Denmark Robert Superko, M.D. Professor of Medicine and President at Cholesterol, Genetics, and Heart Disease Institute, Carmel, CA Pierre-Jean Touboul, M.D. Professor of Neurology, Department of Neurology and Stroke Center, AP-HP Bichat University Hospital, Neurology and Stroke Center, Paris, France Nathan Wong, M.P.H., Ph.D. Professor of Epidemiology and Director, Heart Disease Prevention Program, University of California, Irvine, CA Symposium Registration About SHAPE The Society for Heart Attack Prevention and Eradication (SHAPE) is a non-profit organization that promotes education and research related to prevention, detection, and treatment of heart attacks. SHAPE is committed to raising public awareness about revolutionary discoveries that are opening exciting avenues that can lead to the eradication of heart attacks. SHAPE's mission is to eradicate heart attacks in the 21st century. SHAPE has recently embarked on “Machine Learning Heart Attack Forecast System (Vulnerable Patient Project)” Project which is a collaborative effort between world’s leading cardiovascular researchers to develop a new Heart Attack Forecast System empowered by artificial intelligence. Additional information on this innovative project will be announced soon. To learn more about SHAPE visit Contact information: 1-877-SHAPE11 and info(at)shapesociety(dot)org. Learn more about the Vulnerable Patient About SHAPE Task Force The SHAPE Task Force, an international group of leading cardiovascular physicians and researchers, has created the SHAPE Guidelines, which educates physicians on how to identify asymptomatic atherosclerosis (hidden plaques) and implement proper therapies to prevent a future heart attack. According to the SHAPE Guidelines, men 45-75 and women 55-75 need to be tested for hidden plaques in coronary or carotid arteries. Individuals with high risk atherosclerosis (high plaque score) should be treated even if their cholesterol level is within statistical “normal range.” If they have plaques, the so-called normal is not normal for them. The higher the amount of plaque burden in the arteries the higher the risk and the more vulnerable to heart attack. SHAPE Guideline aims to identify the asymptomatic “Vulnerable Patient” and offer them intensive preventive therapy to prevent a future heart attack. Knowing one's plaque score can be a matter of life and death. The SHAPE Task Force includes the following: Click below to learn about SHAPE Centers of Excellence Drs Naghavi, PK Shah, Daniel Berman, and Mathew Budoff members of the SHAPE Task Force explain how hospitals and community clinics can become a SHAPE Center of Excellence and establish themselves a leader in preventive health.

Koch R.,University of Gottingen | Demant M.,University of Gottingen | Aung T.,University of Gottingen | Diering N.,University of Gottingen | And 14 more authors.
Blood | Year: 2014

Tumors are composed of phenotypically heterogeneous cell populations. The nongenomic mechanisms underlying transitions and interactions between cell populations are largely unknown. Here, we show that diffuse large B-cell lymphomas possess a self-organized infrastructure comprising side population (SP) and non-SP cells, where transitions between clonogenic states are modulated by exosomemediated Wnt signaling. DNAmethylation modulated SP-non-SP transitions and was correlated with the reciprocal expressions of Wnt signaling pathway agonist Wnt3a in SP cells and the antagonist secreted frizzled-related protein 4 in non-SP cells. Lymphoma SP cells exhibited autonomous clonogenicity and exported Wnt3a via exosomes to neighboring cells, thusmodulating population equilibriumin the tumor. © 2014 by The American Society of Hematology.

Braun M.,Albert Ludwigs University of Freiburg | Brandt A.U.,Charité - Medical University of Berlin | Schulz S.,Albert Ludwigs University of Freiburg | Schulz S.,Institute for Medical Informatics | Boeker M.,Albert Ludwigs University of Freiburg
BMC Medical Informatics and Decision Making | Year: 2014

Background: Numerous information models for electronic health records, such as openEHR archetypes are available. The quality of such clinical models is important to guarantee standardised semantics and to facilitate their interoperability. However, validation aspects are not regarded sufficiently yet. The objective of this report is to investigate the feasibility of archetype development and its community-based validation process, presuming that this review process is a practical way to ensure high-quality information models amending the formal reference model definitions. Methods. A standard archetype development approach was applied on a case set of three clinical tests for multiple sclerosis assessment: After an analysis of the tests, the obtained data elements were organised and structured. The appropriate archetype class was selected and the data elements were implemented in an iterative refinement process. Clinical and information modelling experts validated the models in a structured review process. Results: Four new archetypes were developed and publicly deployed in the openEHR Clinical Knowledge Manager, an online platform provided by the openEHR Foundation. Afterwards, these four archetypes were validated by domain experts in a team review. The review was a formalised process, organised in the Clinical Knowledge Manager. Both, development and review process turned out to be time-consuming tasks, mostly due to difficult selection processes between alternative modelling approaches. The archetype review was a straightforward team process with the goal to validate archetypes pragmatically. Conclusions: The quality of medical information models is crucial to guarantee standardised semantic representation in order to improve interoperability. The validation process is a practical way to better harmonise models that diverge due to necessary flexibility left open by the underlying formal reference model definitions.This case study provides evidence that both community- and tool-enabled review processes, structured in the Clinical Knowledge Manager, ensure archetype quality. It offers a pragmatic but feasible way to reduce variation in the representation of clinical information models towards a more unified and interoperable model. © 2014Braun et al.; licensee BioMed Central Ltd.

Badura A.,Institute of Hygiene | Feierl G.,Institute of Hygiene | Pregartner G.,Institute for Medical Informatics | Krause R.,Medical University of Graz | Grisold A.J.,Institute of Hygiene
Clinical Microbiology and Infection | Year: 2015

Antibiotic resistance patterns of more than 120 000 clinical Escherichia coli isolates were retrospectively analysed. Isolates originated from both hospitalized patients and outpatients from the region of southeast Austria from 1998 to 2013. Except for amoxicillin/clavulanic acid, nitrofurantoin and piperacillin/tazobactam, all of the antibiotics analysed showed increasing proportions of resistant isolates over time, which were most prominent for ampicillin (from 25.4% in 1998 to 40% in 2013), cefotaxime (0.1% to 6.7%), ceftazidime (0.3% to 14.2%), ciprofloxacin (4.3% to 16.7%) and trimethoprim/sulfamethoxazole (14.6% to 24.8%). There was a marked increase in extended-spectrum β-lactamase-positive isolates (0.1% to 6.3%) starting in 2005, with male patients and hospital-related patients showing a higher increase than female patients and outpatients. Proportions of resistant isolates for most antibiotics were generally higher for male patients and hospital-related patients. Amikacin, nitrofurantoin and trimethoprim/sulfamethoxazole showed a marked increase in resistance proportions among male subjects aged 10 to 19 years which were absent for female subjects, indicating a strong modulation potential of host characteristics. © 2015 European Society of Clinical Microbiology and Infectious Diseases.

Beitzke M.,Institute for Medical Informatics | Gattringer T.,Institute for Medical Informatics | Enzinger C.,Institute for Medical Informatics | Wagner G.,Medical University of Graz | And 2 more authors.
Stroke | Year: 2011

BACKGROUND AND PURPOSE-: Nontraumatic subarachnoid hemorrhage at the convexity of the brain (cSAH) is an incompletely characterized subtype of nonaneurysmal subarachnoid bleeding. This study sought to systematically describe the clinical presentation, etiology, and long-term outcome in patients with cSAH. METHODS-: For a 6-year period, we searched our radiological database for patients with nontraumatic nonaneurysmal subarachnoid hemorrhages (n=131) seen on CT or MRI. By subsequent image review, we identified 24 patients with cSAH defined by intrasulcal bleeding restricted to the hemispheric convexities. We reviewed their medical records, analyzed the neuroimaging studies, and followed up patients by telephone or a clinical visit. RESULTS-: The 24 patients with cSAH had a mean age of 70 years (range, 37-88 years), 20 (83%) were >60 years, and 13 (54%) were women. Patients often presented with transient sensory and/or motor symptoms (n=10 [42%]) and seizures (n=5 [21%]), whereas headaches typical of subarachnoid hemorrhage were rare (n=4 [17%]). MRI provided evidence for prior bleedings in 11 patients (microbleeds in 10 and parenchymal bleeds in 5) with a bleeding pattern suggestive of cerebral amyloid angiopathy in 5 subjects. At follow-up (after a mean of 33 months), 14 patients (64%) had an unfavorable outcome (modified Rankin scale score 3-6), including 5 deaths. We did not observe recurrent cSAH. CONCLUSIONS-: Our data suggest that cSAH often presents with features not typical for subarachnoid bleeding. In the elderly, cSAH is frequently associated with bleeding-prone conditions such as cerebral amyloid angiopathy. Recurrence of cSAH is rare but the condition itself is a marker of poor prognosis. © 2011 American Heart Association, Inc.

Herbst A.,Ludwig Maximilians University of Munich | Jurinovic V.,Institute for Medical Informatics | Krebs S.,Ludwig Maximilians University of Munich | Thieme S.E.,Ludwig Maximilians University of Munich | And 3 more authors.
BMC Genomics | Year: 2014

Background: Deregulation of Wnt/β-catenin signaling is a hallmark of the majority of sporadic forms of colorectal cancer and results in increased stability of the protein β-catenin. β-catenin is then shuttled into the nucleus where it activates the transcription of its target genes, including the proto-oncogenes MYC and CCND1 as well as the genes encoding the basic helix-loop-helix (bHLH) proteins ASCL2 and ITF-2B. To identify genes commonly regulated by β-catenin in colorectal cancer cell lines, we analyzed β-catenin target gene expression in two non-isogenic cell lines, DLD1 and SW480, using DNA microarrays and compared these genes to β-catenin target genes published in the PubMed database and DNA microarray data presented in the Gene Expression Omnibus (GEO) database.Results: Treatment of DLD1 and SW480 cells with β-catenin siRNA resulted in differential expression of 1501 and 2389 genes, respectively. 335 of these genes were regulated in the same direction in both cell lines. Comparison of these data with published β-catenin target genes for the colon carcinoma cell line LS174T revealed 193 genes that are regulated similarly in all three cell lines. The overlapping gene set includes confirmed β-catenin target genes like AXIN2, MYC, and ASCL2. We also identified 11 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that are regulated similarly in DLD1 and SW480 cells and one pathway - the steroid biosynthesis pathway - was regulated in all three cell lines.Conclusions: Based on the large number of potential β-catenin target genes found to be similarly regulated in DLD1, SW480 and LS174T cells as well as the large overlap with confirmed β-catenin target genes, we conclude that DLD1 and SW480 colon carcinoma cell lines are suitable model systems to study Wnt/β-catenin signaling and associated colorectal carcinogenesis. Furthermore, the confirmed and the newly identified potential β-catenin target genes are useful starting points for further studies. © 2014 Herbst et al.; licensee BioMed Central Ltd.

Kuper C.,Institute for Medical Informatics | Fraek M.-L.,Institute for Medical Informatics | Muller H.-H.,Institute for Medical Informatics | Beck F.-X.,Institute for Medical Informatics | And 2 more authors.
Critical Care Medicine | Year: 2012

Objective: Acute kidney injury associated with reduced urinary concentration is a frequent and severe complication during sepsis. The present study addressed the effect of endotoxemia on the functional and molecular mechanisms that determine urinary concentrating ability. Efficient urinary concentration depends on, amongst other factors, the expression of the Cl channel kidney-specific chloride channel 1 and its subunit Barttin, the urea transporter-A1, and the water channel aquaporin 2, all of which are regulated by the transcription factor TonEBP/NFAT5. Design: Experimental animal and cell culture model. Setting: University laboratory. Subjects: Wistar rats and Madin-Darby canine kidney cells. Interventions: Rats were injected with lipopolysaccharide (5 mg/kg bodyweight intraperitoneal) or vehicle (phosphate-buffered saline) as control. After 24 hrs, urine, blood, and tissue samples from various kidney zones were analyzed for parameters that determine urinary concentration ability. Madin-Darby canine kidney cells were treated under isotonic or hypertonic conditions with the nitric oxide donor S-nitroso-N-acetylpenicillamine.Measurements and Main Results: In rats injected with lipopolysaccharide, urine osmolality was reduced by ~40%, along with medullary induction of inducible nitric oxide synthase and a dramatic increase in urinary nitric oxide degradation products nitrite/nitrate. Concomitantly, expressions of ClC-K1, Barttin, urea transporter-A1, and aquaporin 2 were significantly lower. This was associated with the appearance of S-nitrosylated TonEBP/NFAT5, as monitored by the biotin-switch assay and immunoprecipitation, and reduced TonEBP/NFAT5 DNA binding activity in the renal inner medulla. These results were confirmed in Madin-Darby canine kidney cells transfected with a reporter construct driven by the urea transporter-A promoter, in which the nitric oxide donor S-nitroso-N-acetylpenicillamine reduces urea transporter-A reporter activity under isotonic and hypertonic conditions. Conclusions: The present data demonstrate that lipopolysaccharide increases medullary nitric oxide production by iNOS induction, resulting in impairment of the transcriptional activity of TonEBP/NFAT5 by S-nitrosylation. The consequence thereof is reduced expression of TonEBP/NFAT5 target genes ClC-K1, Barttin, urea transporter-A1, and aquaporin 2 that are required for urinary concentration. Our findings may provide further insight into the molecular mechanisms underlying the urinary concentration defect in sepsis. © 2012 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins.

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