Incidence, risk factors, and outcome of aspiration pneumonitis in ICU overdose patients

Objective: To assess the incidence and outcome of clinically significant aspiration pneumonitis in intensive care unit (ICU) overdose patients and to identify its predisposing factors. Design: Retrospective cohort study. Setting: Medical ICU of an academic tertiary care hospital. Patients: Atotal of 273 consecutive overdose admissions. Measurements and results: Clinically significant aspiration pneumonitis was defined as the occurrence of respiratory dysfunction in apatient with alocalised infiltrate on chest X-ray within 72 h of admission. In our cohort we identified 47 patients (17%) with aspiration pneumonitis. Importantly, aspiration pneumonitis was associated with ahigher incidence of cardiac arrest (6.4 vs 0.9%; p = 0.037) and an increased duration of both ICU stay and overall hospital stay [respectively: median 1 (interquartile range 1-3) vs 1 (1-2), p = 0.025; and median 2 (1-7) vs 1 (1-3), p < 0.001]. In multivariate logistic regression analysis, Glasgow Coma Scale (GCS) score [odds ratio (OR) for each point of GCS 0.8; 95% confidence interval (CI) 0.7-0.9; p = 0.001], ingestion of opiates (OR 4.5; 95% CI 1.7-11.6; p = 0.002), and white blood cell count (WBC) (OR for each increase in WBC of 109/l 1.05; 95% CI 1.0-1.19; p = 0.049) were identified as independent risk factors. Conclusions: Clinically relevant aspiration pneumonitis is afrequent complication in overdose patients admitted to the ICU. Moreover, aspiration pneumonitis is associated with ahigher incidence of cardiac arrest and increased ICU and total in-hospital sta

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.Bjoern Menze is supported through the DFG funding (SFB 824, subproject B12) and a Helmut-Horten-Professorship for Biomedical Informatics by the Helmut-Horten-Foundation. Florian Kofler is Supported by Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE), GSC 81. An Tang was supported by the Fonds de recherche du Québec en Santé and Fondation de l’association des radiologistes du Québec (FRQS- ARQ 34939 Clinical Research Scholarship – Junior 2 Salary Award). Hongwei Bran Li is supported by Forschungskredit (Grant NO. FK-21- 125) from University of Zurich.Peer ReviewedArticle signat per 109 autors/es: Patrick Bilic 1,a,b, Patrick Christ 1,a,b, Hongwei Bran Li 1,2,∗,b, Eugene Vorontsov 3,a,b, Avi Ben-Cohen 5,a, Georgios Kaissis 10,12,15,a, Adi Szeskin 18,a, Colin Jacobs 4,a, Gabriel Efrain Humpire Mamani 4,a, Gabriel Chartrand 26,a, Fabian Lohöfer 12,a, Julian Walter Holch 29,30,69,a, Wieland Sommer 32,a, Felix Hofmann 31,32,a, Alexandre Hostettler 36,a, Naama Lev-Cohain 38,a, Michal Drozdzal 34,a, Michal Marianne Amitai 35,a, Refael Vivanti 37,a, Jacob Sosna 38,a, Ivan Ezhov 1, Anjany Sekuboyina 1,2, Fernando Navarro 1,76,78, Florian Kofler 1,13,57,78, Johannes C. Paetzold 15,16, Suprosanna Shit 1, Xiaobin Hu 1, Jana Lipková 17, Markus Rempfler 1, Marie Piraud 57,1, Jan Kirschke 13, Benedikt Wiestler 13, Zhiheng Zhang 14, Christian Hülsemeyer 1, Marcel Beetz 1, Florian Ettlinger 1, Michela Antonelli 9, Woong Bae 73, Míriam Bellver 43, Lei Bi 61, Hao Chen 39, Grzegorz Chlebus 62,64, Erik B. Dam 72, Qi Dou 41, Chi-Wing Fu 41, Bogdan Georgescu 60, Xavier Giró-i-Nieto 45, Felix Gruen 28, Xu Han 77, Pheng-Ann Heng 41, Jürgen Hesser 48,49,50, Jan Hendrik Moltz 62, Christian Igel 72, Fabian Isensee 69,70, Paul Jäger 69,70, Fucang Jia 75, Krishna Chaitanya Kaluva 21, Mahendra Khened 21, Ildoo Kim 73, Jae-Hun Kim 53, Sungwoong Kim 73, Simon Kohl 69, Tomasz Konopczynski 49, Avinash Kori 21, Ganapathy Krishnamurthi 21, Fan Li 22, Hongchao Li 11, Junbo Li 8, Xiaomeng Li 40, John Lowengrub 66,67,68, Jun Ma 54, Klaus Maier-Hein 69,70,7, Kevis-Kokitsi Maninis 44, Hans Meine 62,65, Dorit Merhof 74, Akshay Pai 72, Mathias Perslev 72, Jens Petersen 69, Jordi Pont-Tuset 44, Jin Qi 56, Xiaojuan Qi 40, Oliver Rippel 74, Karsten Roth 47, Ignacio Sarasua 51,12, Andrea Schenk 62,63, Zengming Shen 59,60, Jordi Torres 46,43, Christian Wachinger 51,12,1, Chunliang Wang 42, Leon Weninger 74, Jianrong Wu 25, Daguang Xu 71, Xiaoping Yang 55, Simon Chun-Ho Yu 58, Yading Yuan 52, Miao Yue 20, Liping Zhang 58, Jorge Cardoso 9, Spyridon Bakas 19,23,24, Rickmer Braren 6,12,30,a, Volker Heinemann 33,a, Christopher Pal 3,a, An Tang 27,a, Samuel Kadoury 3,a, Luc Soler 36,a, Bram van Ginneken 4,a, Hayit Greenspan 5,a, Leo Joskowicz 18,a, Bjoern Menze 1,2,a // 1 Department of Informatics, Technical University of Munich, Germany; 2 Department of Quantitative Biomedicine, University of Zurich, Switzerland; 3 Ecole Polytechnique de Montréal, Canada; 4 Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; 5 Department of Biomedical Engineering, Tel-Aviv University, Israel; 6 German Cancer Consortium (DKTK), Germany; 7 Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; 8 Philips Research China, Philips China Innovation Campus, Shanghai, China; 9 School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK; 10 Institute for AI in Medicine, Technical University of Munich, Germany; 11 Department of Computer Science, Guangdong University of Foreign Studies, China; 12 Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany; 13 Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany; 14 Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School, China; 15 Department of Computing, Imperial College London, London, United Kingdom; 16 Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany; 17 Brigham and Women’s Hospital, Harvard Medical School, USA; 18 School of Computer Science and Engineering, the Hebrew University of Jerusalem, Israel; 19 Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, PA, USA; 20 CGG Services (Singapore) Pte. Ltd., Singapore; 21 Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, India; 22 Sensetime, Shanghai, China; 23 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA; 24 Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA, USA; 25 Tencent Healthcare (Shenzhen) Co., Ltd, China; 26 The University of Montréal Hospital Research Centre (CRCHUM) Montréal, Québec, Canada; 27 Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montréal, Canada; 28 Institute of Control Engineering, Technische Universität Braunschweig, Germany; 29 Department of Medicine III, University Hospital, LMU Munich, Munich, Germany; 30 Comprehensive Cancer Center Munich, Munich, Germany; 31 Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Germany; 32 Department of Radiology, University Hospital, LMU Munich, Germany; 33 Department of Hematology/Oncology & Comprehensive Cancer Center Munich, LMU Klinikum Munich, Germany; 34 Polytechnique Montréal, Mila, QC, Canada; 35 Department of Diagnostic Radiology, Sheba Medical Center, Tel Aviv university, Israel; 36 Department of Surgical Data Science, Institut de Recherche contre les Cancers de l’Appareil Digestif (IRCAD), France; 37 Rafael Advanced Defense System, Israel; 38 Department of Radiology, Hadassah University Medical Center, Jerusalem, Israel; 39 Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China; 40 Department of Electrical and Electronic Engineering, The University of Hong Kong, China; 41 Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; 42 Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Sweden; 43 Barcelona Supercomputing Center, Barcelona, Spain; 44 Eidgenössische Technische Hochschule Zurich (ETHZ), Zurich, Switzerland; 45 Signal Theory and Communications Department, Universitat Politecnica de Catalunya, Catalonia, Spain; 46 Universitat Politecnica de Catalunya, Catalonia, Spain; 47 University of Tuebingen, Germany; 48 Mannheim Institute for Intelligent Systems in Medicine, department of Medicine Mannheim, Heidelberg University, Germany; 49 Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany; 50 Central Institute for Computer Engineering (ZITI), Heidelberg University, Germany; 51 Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany; 52 Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, NY, USA; 53 Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, South Korea; 54 Department of Mathematics, Nanjing University of Science and Technology, China; 55 Department of Mathematics, Nanjing University, China; 56 School of Information and Communication Engineering, University of Electronic Science and Technology of China, China; 57 Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany; 58 Department of Imaging and Interventional Radiology, Chinese University of Hong Kong, Hong Kong, China; 59 Beckman Institute, University of Illinois at Urbana-Champaign, USA; 60 Siemens Healthineers, USA; 61 School of Computer Science, the University of Sydney, Australia; 62 Fraunhofer MEVIS, Bremen, Germany; 63 Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany; 64 Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands; 65 Medical Image Computing Group, FB3, University of Bremen, Germany; 66 Departments of Mathematics, Biomedical Engineering, University of California, Irvine, USA; 67 Center for Complex Biological Systems, University of California, Irvine, USA; 68 Chao Family Comprehensive Cancer Center, University of California, Irvine, USA; 69 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; 70 Helmholtz Imaging, Germany; 71 NVIDIA, Santa Clara, CA, USA; 72 Department of Computer Science, University of Copenhagen, Denmark; 73 Kakao Brain, Republic of Korea; 74 Institute of Imaging & Computer Vision, RWTH Aachen University, Germany; 75 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China; 76 Department of Radiation Oncology and Radiotherapy, Klinikum rechts der Isar, Technical University of Munich, Germany; 77 Department of computer science, UNC Chapel Hill, USA; 78 TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, GermanyPostprint (published version

Nachhaltigkeit im industriellen Umfeld

Im Rahmen der Lehrveranstaltung "Nachhaltigkeit im industriellen Umfeld" im Masterstudiengang Umwelt- und Verfahrenstechnik der Hochschulen Konstanz und Ravensburg-Weingarten fand im Dezember 2016 eine studentische Fachkonferenz statt. Die Studierenden entwickelten in Einzelarbeit oder als Zweierteam Konferenzbeiträge zu folgenden Themen: - Spannendes aus dem Bereich der Energieerzeugung und der Grauen Energie - Aspekte der Kreislaufwirtschaft - Ökosysteme - ihre Belastung und Erhalt - Spezifische Wirtschaftszweige und Nachhaltigkeit Die Ergebnisse der studentischen Fachkonferenz zur „Nachhaltigkeit im industriellen Umfeld“ werden in der vorliegenden Publikation präsentiert

Controlled Formation of Porous 2D Lattices from C 3 ‐symmetric Ph 6 −Me‐Tribenzotriquinacene‐OAc 3

The on-surface self-assembly of molecules to form holey nanographenes is a promising approach to control the properties of the resulting 2D lattice. Usually, planar molecules are utilized to prepare flat, structurally confined molecular layers, with only a few recent examples of warped precursors. However, control of the superstructures is limited thus far. Herein, we report the temperature-controlled self-assembly of a bowl-shaped, acetylated C3 -symmetric hexaphenyltribenzotriquinacene derivative on Cu(111). Combining scanning tunneling microscopy (STM) and density functional theory (DFT) confirms the formation of highly differing arrangements starting with π-stacked bowl-to-bowl dimers at low coverage at room temperature via chiral honeycomb structures, an intermediate trigonal superstructure, followed by a fully carbon-based, flattened hexagonal superstructure formed by on-surface deacetylation, which is proposed as a precursor for holey graphene networks with unique defect structures

Investigating surface correction relations for RGB stars

State-of-the-art stellar structure and evolution codes fail to adequately describe turbulent convection. For stars with convective envelopes, such as red giants, this leads to an incomplete depiction of the surface layers. As a result, the predicted stellar oscillation frequencies are haunted by systematic errors, the so-called surface effect. Different empirically and theoretically motivated correction relations have been proposed to deal with this issue. In this paper, we compare the performance of these surface correction relations for red giant branch stars. For this purpose, we apply the different surface correction relations in asteroseismic analyses of eclipsing binaries and open clusters. In accordance with previous studies of main-sequence stars, we find that the use of different surface correction relations biases the derived global stellar properties, including stellar age, mass, and distance estimates. We furthermore demonstrate that the different relations lead to the same systematic errors for two different open clusters. Our results overall discourage from the use of surface correction relations that rely on reference stars to calibrate free parameters. Due to the demonstrated systematic biasing of the results, the use of appropriate surface correction relations is imperative to any asteroseismic analysis of red giants. Accurate mass, age, and distance estimates for red giants are fundamental when addressing questions that deal with the chemo-dynamical evolution of the Milky Way galaxy. In this way, our results also have implications for fields, such as galactic archaeology, that draw on findings from stellar physics

Prohormones in the early diagnosis of cardiac syncope

Background--The early detection of cardiac syncope is challenging. We aimed to evaluate the diagnostic value of 4 novel prohormones, quantifying different neurohumoral pathways, possibly involved in the pathophysiological features of cardiac syncope: midregional-pro-A-type natriuretic peptide (MRproANP), C-terminal proendothelin 1, copeptin, and midregionalproadrenomedullin. Methods and Results--We prospectively enrolled unselected patients presenting with syncope to the emergency department (ED) in a diagnostic multicenter study. ED probability of cardiac syncope was quantified by the treating ED physician using a visual analogue scale. Prohormones were measured in a blinded manner. Two independent cardiologists adjudicated the final diagnosis on the basis of all clinical information, including 1-year follow-up. Among 689 patients, cardiac syncope was the adjudicated final diagnosis in 125 (18%). Plasma concentrations of MRproANP, C-terminal proendothelin 1, copeptin, and midregional-proadrenomedullin were all significantly higher in patients with cardiac syncope compared with patients with other causes (P < 0.001). The diagnostic accuracies for cardiac syncope, as quantified by the area under the curve, were 0.80 (95% confidence interval [CI], 0.76-0.84), 0.69 (95% CI, 0.64-0.74), 0.58 (95% CI, 0.52-0.63), and 0.68 (95% CI, 0.63-0.73), respectively. In conjunction with the ED probability (0.86; 95% CI, 0.82-0.90), MRproANP, but not the other prohormone, improved the area under the curve to 0.90 (95% CI, 0.87-0.93), which was significantly higher than for the ED probability alone (P=0.003). An algorithm to rule out cardiac syncope combining an MRproANP level of < 77 pmol/L and an ED probability of < 20% had a sensitivity and a negative predictive value of 99%. Conclusions--The use of MRproANP significantly improves the early detection of cardiac syncope among unselected patients presenting to the ED with syncope

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community

Pre-hospital management protocols and perceived difficulty in diagnosing acute heart failure

Aim To illustrate the pre-hospital management arsenals and protocols in different EMS units, and to estimate the perceived difficulty of diagnosing suspected acute heart failure (AHF) compared with other common pre-hospital conditions. Methods and results A multinational survey included 104 emergency medical service (EMS) regions from 18 countries. Diagnostic and therapeutic arsenals related to AHF management were reported for each type of EMS unit. The prevalence and contents of management protocols for common medical conditions treated pre-hospitally was collected. The perceived difficulty of diagnosing AHF and other medical conditions by emergency medical dispatchers and EMS personnel was interrogated. Ultrasound devices and point-of-care testing were available in advanced life support and helicopter EMS units in fewer than 25% of EMS regions. AHF protocols were present in 80.8% of regions. Protocols for ST-elevation myocardial infarction, chest pain, and dyspnoea were present in 95.2, 80.8, and 76.0% of EMS regions, respectively. Protocolized diagnostic actions for AHF management included 12-lead electrocardiogram (92.1% of regions), ultrasound examination (16.0%), and point-of-care testings for troponin and BNP (6.0 and 3.5%). Therapeutic actions included supplementary oxygen (93.2%), non-invasive ventilation (80.7%), intravenous furosemide, opiates, nitroglycerine (69.0, 68.6, and 57.0%), and intubation 71.5%. Diagnosing suspected AHF was considered easy to moderate by EMS personnel and moderate to difficult by emergency medical dispatchers (without significant differences between de novo and decompensated heart failure). In both settings, diagnosis of suspected AHF was considered easier than pulmonary embolism and more difficult than ST-elevation myocardial infarction, asthma, and stroke. Conclusions The prevalence of AHF protocols is rather high but the contents seem to vary. Difficulty of diagnosing suspected AHF seems to be moderate compared with other pre-hospital conditions

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094