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54. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Author

Gau, Rémi, Noble, Stephanie, Heuer, Katja, Bottenhorn, Katherine L, Bilgin, Isil P, Yang, Yu-Fang, Huntenburg, Julia M, Bayer, Johanna MM, Bethlehem, Richard AI, Rhoads, Shawn A, Vogelbacher, Christoph, Borghesani, Valentina, Levitis, Elizabeth, Wang, Hao-Ting, Van Den Bossche, Sofie, Kobeleva, Xenia, Legarreta, Jon Haitz, Guay, Samuel, Atay, Selim Melvin, Varoquaux, Gael P, Huijser, Dorien C, Sandström, Malin S, Herholz, Peer, Nastase, Samuel A, Badhwar, AmanPreet, Dumas, Guillaume, Schwab, Simon, Moia, Stefano, Dayan, Michael, Bassil, Yasmine, Brooks, Paula P, Mancini, Matteo, Shine, James M, O’Connor, David, Xie, Xihe, Poggiali, Davide, Friedrich, Patrick, Heinsfeld, Anibal S, Riedl, Lydia, Toro, Roberto, Caballero-Gaudes, César, Eklund, Anders, Garner, Kelly G, Nolan, Christopher R, Demeter, Damion V, Barrios, Fernando A, Merchant, Junaid S, McDevitt, Elizabeth A, Oostenveld, Robert, Craddock, R Cameron, Rokem, Ariel, Doyle, Andrew, Ghosh, Satrajit S, Nikolaidis, Aki, Stanley, Olivia W, Uruñuela, Eneko, Community, The Brainhack, Anousheh, Nasim, Arnatkeviciute, Aurina, Auzias, Guillaume, Bachar, Dipankar, Bannier, Elise, Basanisi, Ruggero, Basavaraj, Arshitha, Bedini, Marco, Bellec, Pierre, Benn, R Austin, Berluti, Kathryn, Bollmann, Steffen, Bollmann, Saskia, Bradley, Claire, Brown, Jesse, Buchweitz, Augusto, Callahan, Patrick, Chan, Micaela Y, Chandio, Bramsh Q, Cheng, Theresa, Chopra, Sidhant, Chung, Ai Wern, Close, Thomas G, Combrisson, Etienne, Cona, Giorgia, Constable, R Todd, Cury, Claire, Dadi, Kamalaker, Damasceno, Pablo F, Das, Samir, De Vico Fallani, Fabrizio, DeStasio, Krista, Dickie, Erin W, Dorfschmidt, Lena, Duff, Eugene P, DuPre, Elizabeth, Dziura, Sarah, Esper, Nathalia B, Esteban, Oscar, Fadnavis, Shreyas, Flandin, Guillaume, Flannery, Jessica E, and Flournoy, John

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Communication, Congresses as Topic, Internet, Practice Guidelines as Topic, Brainhack Community, Brainhack, best practices, collaboration, community building, hackathon, inclusivity, neuroscience, open science, reproducibility, training, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Published
2021

55. Author Correction: brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Author

Hayashi, Soichi, Caron, Bradley A., Heinsfeld, Anibal Sólon, Vinci-Booher, Sophia, McPherson, Brent, Bullock, Daniel N., Bertò, Giulia, Niso, Guiomar, Hanekamp, Sandra, Levitas, Daniel, Ray, Kimberly, MacKenzie, Anne, Avesani, Paolo, Kitchell, Lindsey, Leong, Josiah K., Nascimento-Silva, Filipi, Koudoro, Serge, Willis, Hanna, Jolly, Jasleen K., Pisner, Derek, Zuidema, Taylor R., Kurzawski, Jan W., Mikellidou, Kyriaki, Bussalb, Aurore, Chaumon, Maximilien, George, Nathalie, Rorden, Christopher, Victory, Conner, Bhatia, Dheeraj, Aydogan, Dogu Baran, Yeh, Fang-Cheng F., Delogu, Franco, Guaje, Javier, Veraart, Jelle, Fischer, Jeremy, Faskowitz, Joshua, Fabrega, Ricardo, Hunt, David, McKee, Shawn, Brown, Shawn T., Heyman, Stephanie, Iacovella, Vittorio, Mejia, Amanda F., Marinazzo, Daniele, Craddock, R. Cameron, Olivetti, Emanuale, Hanson, Jamie L., Garyfallidis, Eleftherios, Stanzione, Dan, Carson, James, Henschel, Robert, Hancock, David Y., Stewart, Craig A., Schnyer, David, Eke, Damian O., Poldrack, Russell A., Bollmann, Steffen, Stewart, Ashley, Bridge, Holly, Sani, Ilaria, Freiwald, Winrich A., Puce, Aina, Port, Nicholas L., and Pestilli, Franco

Published
2024
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57. Retrospective Single-Center Case Study of Clinical Variables and the Degree of Actinic Elastosis Associated with Rare Skin Cancers

Author

Konstantin Drexler, Lara Bollmann, Sigrid Karrer, Mark Berneburg, Sebastian Haferkamp, and Dennis Niebel

Subjects
atypical fibroxanthoma, pleomorphic dermal sarcoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi sarcoma, leiomyosarcoma, Biology (General), QH301-705.5
Abstract

(1) Background: Rare skin cancers include epithelial, neuroendocrine, and hematopoietic neoplasias as well as cutaneous sarcomas. Ultraviolet (UV) radiation and sunburns are important drivers for the incidence of certain cutaneous sarcomas; however, the pathogenetic role of UV light is less clear in rare skin cancers compared to keratinocyte cancer and melanoma. In this study, we compared the degree of actinic elastosis (AE) as a surrogate for lifetime UV exposure among selected rare skin cancers (atypical fibroxanthoma [AFX], pleomorphic dermal sarcoma [PDS], dermatofibrosarcoma protuberans [DFSP], Kaposi sarcoma [KS], Merkel cell carcinoma [MCC], and leiomyosarcoma [LMS]) while taking into account relevant clinical variables (age, sex, and body site). (2) Methods: We newly established a semi-quantitative score for the degree of AE ranging from 0 = none to 3 = total loss of elastic fibers (basophilic degeneration) and multiplied it by the perilesional vertical extent (depth), measured histometrically (tumor-associated elastosis grade (TEG)). We matched the TEG of n = 210 rare skin cancers from 210 patients with their clinical variables. (3) Results: TEG values were correlated with age and whether tumors arose on UV-exposed body sites. TEG values were significantly higher in AFX and PDS cases compared to all other analyzed rare skin cancer types. As expected, TEG values were low in DFSP and KS, while MCC cases exhibited intermediate TEG values. (4) Conclusions: High cumulative UV exposure is more strongly associated with AFX/PDS and MCC than with other rare skin cancers. These important results expand the available data associated with rare skin cancers while also offering insight into the value of differentiating among these tumor types based on their relationship with sun exposure, potentially informing preventative, diagnostic and/or therapeutic approaches.

Published
2024
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64. Case Report: Four cases of cardiac sarcoidosis in patients with inherited cardiomyopathy—a phenotypic overlap, co-existence of two rare cardiomyopathies or a second-hit disease

Author

Hans Ebbinghaus, Laura Ueberham, Daniela Husser-Bollmann, Andreas Bollmann, Ingo Paetsch, Cosima Jahnke, Ulrich Laufs, and Borislav Dinov

Subjects
cardiac sarcoidosis, non-ischemic cardiomyopathy, familial cardiomyopathy, ventricular tachyarrhythmias, conduction disease, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Cardiac sarcoidosis (CS), a rare condition characterized by non-caseating granulomas, can manifest with symptoms such as atrioventricular block and ventricular tachycardia (VT), as well as mimic inherited cardiomyopathies. A 48-year-old male presented with recurrent VT. The initial 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET) scan showed uptake of the mediastinal lymph node. Cardiovascular magnetic resonance (CMR) demonstrated intramyocardial fibrosis. The follow-up 18FDG-PET scan revealed the presence of tracer uptake in the left ventricular (LV) septum, suggesting the likelihood of CS. Genetic testing identified a pathogenic LMNA variant. A 47-year-old female presented with complaints of palpitations and syncope. An Ajmaline provocation test confirmed Brugada syndrome (BrS). CMR revealed signs of cardiac inflammation. An endomyocardial biopsy (EMB) confirmed the diagnosis of cardiac sarcoidosis. Polymorphic VT was induced during an electrophysiological study, and an implantable cardioverter-defibrillator (ICD) was implanted. A 58-year-old woman presented with sustained VT with a prior diagnosis of hypertrophic cardiomyopathy (HCM). A genetic work-up identified the presence of a heterozygous MYBC3 variant of unknown significance (VUS). CMR revealed late gadolinium enhancement (LGE), while the 18FDG-PET scan demonstrated LV tracer uptake. The immunosuppressive therapy was adjusted, and no further VTs were observed. A 28-year-old male athlete with right ventricular dilatation and syncope experienced a cardiac arrest during training. Genetic testing identified a pathogenic mutation in PKP2. The autopsy has confirmed the presence of ACM and a distinctive extracardiac sarcoidosis. Cardiac sarcoidosis and inherited cardiomyopathies may interact in several different ways, altering the clinical presentation. Overlapping pathologies are frequently overlooked. Delayed or incomplete diagnosis risks inadequate treatment. Thus, genetic testing and endomyocardial biopsies should be recommended to obtain a clear diagnosis.

Published
2023
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68. P-wave duration and interatrial block as predictors of new-onset atrial fibrillation: A systematic review and meta-analysis

Author

Konstantinos Zagoridis, Emmanuel Koutalas, Stergios Intzes, Marianthi Symeonidou, Nikoleta Zagoridou, Konstantinos Karagogos, Emmanuel Spanoudakis, Emmanuel Kanoupakis, George Kochiadakis, Borislav Dinov, Nikolaos Dagres, Gerhard Hindricks, Andreas Bollmann, and Sotirios Nedios

Subjects
atrial fibrillation, P-wave, interatrial block, interatrial conduction, devices, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background: Early detection of atrial fibrillation (AF) could improve patient outcomes. P-wave duration (PWD) and interatrial block (IAB) are known predictors of new-onset AF and could improve selection for AF screening. This meta-analysis reviews the published evidence and offers practical implications. Methods: Publication databases were systematically searched, and studies reporting PWD and/or morphology at baseline and new-onset AF during follow-up were included. IAB was defined as partial (pIAB) if PWD≥120 ms or advanced (aIAB) if the P-wave was biphasic in the inferior leads. After quality assessment and data extraction, random-effects analysis calculated odds ratio (OR) and confidence intervals (CI). Subgroup analysis was performed for those with implantable devices (continuous monitoring). Results: Among 16,830 patients (13 studies, mean 66 years old), 2,521 (15%) had new-onset AF over a median of 44 months. New-onset AF was associated with a longer PWD (mean pooled difference: 11.5 ms, 13 studies, p

Published
2023
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70. 6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment

Author

Scholtes, Maike, Westhofen, Lukas, Turner, Lara Ruth, Lotto, Katrin, Schuldes, Michael, Weber, Hendrik, Wagener, Nicolas, Neurohr, Christian, Bollmann, Martin, Körtke, Franziska, Hiller, Johannes, Hoss, Michael, Bock, Julian, and Eckstein, Lutz

Subjects
Computer Science - Other Computer Science, Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract

Verification and validation of automated driving functions impose large challenges. Currently, scenario-based approaches are investigated in research and industry, aiming at a reduction of testing efforts by specifying safety relevant scenarios. To define those scenarios and operate in a complex real-world design domain, a structured description of the environment is needed. Within the PEGASUS research project, the 6-Layer Model (6LM) was introduced for the description of highway scenarios. This paper refines the 6LM and extends it to urban traffic and environment. As defined in PEGASUS, the 6LM provides the possibility to categorize the environment and, therefore, functions as a structured basis for subsequent scenario description. The model enables a structured description and categorization of the general environment, without incorporating any knowledge or anticipating any functions of actors. Beyond that, there is a variety of other applications of the 6LM, which are elaborated in this paper. The 6LM includes a description of the road network and traffic guidance objects, roadside structures, temporary modifications of the former, dynamic objects, environmental conditions and digital information. The work at hand specifies each layer by categorizing its items. Guidelines are formulated and explanatory examples are given to standardize the application of the model for an objective environment description. In contrast to previous publications, the model and its design are described in far more detail. Finally, the holistic description of the 6LM presented includes remarks on possible future work when expanding the concept to machine perception aspects., Comment: 16 pages, 7 figures, submitted to IEEE Access

Published
2020

71. DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning

Author

Ribeiro, Fernanda L., Bollmann, Steffen, and Puckett, Alexander M.

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Whether it be in a man-made machine or a biological system, form and function are often directly related. In the latter, however, this particular relationship is often unclear due to the intricate nature of biology. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy from structural and functional MRI data. Our model was not only able to predict the functional organization of human visual cortex from anatomical properties alone, but it was also able to predict nuanced variations across individuals.

Published
2020
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75. COVID-19 and Severe Acute Respiratory Infections: Monitoring Trends in 421 German Hospitals During the First Four Pandemic Waves

Author

Leiner J, Hohenstein S, Pellissier V, König S, Winklmair C, Nachtigall I, Bollmann A, and Kuhlen R

Subjects
initiative of quality medicine, germany, covid-19, sari, inpatient, hospital network, Infectious and parasitic diseases, RC109-216
Abstract

Johannes Leiner,1,2 Sven Hohenstein,2 Vincent Pellissier,2 Sebastian König,1,2 Claudia Winklmair,3 Irit Nachtigall,4 Andreas Bollmann,1,2,5 Ralf Kuhlen3,5,6 On behalf of the scientific advisory board of the Initiative of Quality Medicine (IQM)1Heart Centre Leipzig at University of Leipzig, Department of Electrophysiology, Leipzig, Germany; 2Real World Evidence and Health Technology Assessment at Helios Health Institute, Berlin, Germany; 3Initiative of Quality Medicine, Berlin, Germany; 4Department of Infectious Diseases and Infection Prevention, HELIOS Hospital Emil-von-Behring, Berlin, Germany and Charité - Universitaetsmedizin Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany; 5Helios Health Institute, Berlin, Germany; 6Helios Health, Berlin, GermanyCorrespondence: Ralf Kuhlen, Initiative Qualitaetsmedizin e.V, Alt-Moabit 104, Berlin, 10559, Germany, Tel +49 30 7262 152 - 0, Email ralf.kuhlen@initiative-qualitaetsmedizin.deIntroduction: Reliable surveillance systems to monitor trends of COVID-19 case numbers and the associated healthcare burden play a central role in efficient pandemic management. In Germany, the federal government agency Robert-Koch-Institute uses an ICD-code-based inpatient surveillance system, ICOSARI, to assess temporal trends of severe acute respiratory infection (SARI) and COVID-19 hospitalization numbers. In a similar approach, we present a large-scale analysis covering four pandemic waves derived from the Initiative of Quality Medicine (IQM), a German-wide network of acute care hospitals.Methods: Routine data from 421 hospitals for the years 2019– 2021 with a “pre-pandemic” period (01– 01-2019 to 03– 03-2020) and a “pandemic” period (04– 03-2020 to 31– 12-2021) was analysed. SARI cases were defined by ICD-codes J09-J22 and COVID-19 by ICD-codes U07.1 and U07.2. The following outcomes were analysed: intensive care treatment, mechanical ventilation, in-hospital mortality.Results: Over 1.1 million cases of SARI and COVID-19 were identified. Patients with COVID-19 and additional codes for SARI were at higher risk for adverse outcomes when compared to non-COVID SARI and COVID-19 without any coding for SARI. During the pandemic period, non-COVID SARI cases were associated with 28%, 23% and 27% higher odds for intensive care treatment, mechanical ventilation and in-hospital mortality, respectively, compared to pre-pandemic SARI.Conclusion: The nationwide IQM network could serve as an excellent data source to enhance COVID-19 and SARI surveillance in view of the ongoing pandemic. Future developments of COVID-19/SARI case numbers and associated outcomes should be closely monitored to identify specific trends, especially considering novel virus variants.Graphical Abstract: Keywords: initiative of quality medicine, Germany, COVID-19, SARI, inpatient, hospital network

Published
2023

76. Monocyte chemoattractant protein 1 as a potential biomarker for immune checkpoint inhibitor‐associated neurotoxicity

Author

Nora Möhn, Susann Mahjoub, Laura Duzzi, Emily Narten, Lea Grote‐Levi, Gudrun Körner, Tabea Seeliger, Gernot Beutel, Benjamin‐Alexander Bollmann, Thomas Wirth, André Huss, Hayrettin Tumani, Imke Grimmelmann, Ralf Gutzmer, Philipp Ivanyi, Thomas Skripuletz, and ICOG‐CCCH (Immune Cooperative Oncology Group; Comprehensive Cancer Center Hannover)

Subjects
biomarker, immune‐related adverse events, immunotherapy, neurotoxicity, serum neurofilament light chains, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Oncological patients can benefit substantially from treatment with immune checkpoint inhibitors (ICI). However, there is a growing awareness of immune‐related adverse events (irAE). Especially ICI‐mediated neurological adverse events (nAE(+)), are tough to diagnose and biomarkers to identify patients at risk are missing. Methods A prospective register with prespecified examinations was established for ICI treated patients in December 2019. At the time of data cut‐off, 110 patients were enrolled and completed the clinical protocol. Herein, cytokines and serum neurofilament light chain (sNFL) from 21 patients were analyzed. Results nAE of any grade were observed in 31% of the patients (n = 34/110). In nAE(+) patients a significant increase in sNFL concentrations over time was observed. Patients with higher‐grade nAE had significantly elevated serum‐concentrations of monocyte chemoattractant protein 1 (MCP‐1) and brain‐derived neurotrophic factor (BDNF) at baseline compared to individuals without any nAE (p

Published
2023
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81. Retrospective, Observational Analysis on the Impact of SARS-CoV-2 Variant Omicron in Hospitalized Immunocompromised Patients in a German Hospital Network—The VISAGE Study

Author

Irit Nachtigall, Stefan Kwast, Sven Hohenstein, Sebastian König, Phi Long Dang, Johannes Leiner, Nicola Giesen, Benjamin Thomas Schleenvoigt, Marzia Bonsignore, Andreas Bollmann, Ralf Kuhlen, and Fungwe Jah

Subjects
Omicron, immunocompromised, SARI, in-hospital mortality, mechanical ventilation, intensive care unit, Medicine
Abstract

Aims: Endemic SARS-CoV-2 infections still burden the healthcare system and represent a considerable threat to vulnerable patient cohorts, in particular immunocompromised (IC) patients. This study aimed to analyze the in-hospital outcome of IC patients with severe SARS-CoV-2 infection in Germany. Methods: This retrospective, observational study, analyzed administrative data from inpatient cases (n = 146,324) in 84 German Helios hospitals between 1 January 2022 and 31 December 2022 with regard to in-hospital outcome and health care burden in IC patients during the first 12 months of Omicron dominance. As the primary objective, in-hospital outcomes of patients with COVID-19-related severe acute respiratory infection (SARI) were analyzed by comparing patients with (n = 2037) and without IC diagnoses (n = 14,772). Secondary analyses were conducted on IC patients with (n = 2037) and without COVID-19-related SARI (n = 129,515). A severe in-hospital outcome as a composite endpoint was defined per the WHO definition if one of the following criteria were met: intensive care unit (ICU) treatment, mechanical ventilation (MV), or in-hospital death. Results: In total, 12% of COVID-related SARI cases were IC patients, accounting for 15% of ICU admissions, 15% of MV use, and 16% of deaths, resulting in a higher prevalence of severe in-hospital courses in IC patients developing COVID-19-related SARI compared to non-IC patients (Odds Ratio, OR = 1.4, p < 0.001), based on higher in-hospital mortality (OR = 1.4, p < 0.001), increased need for ICU treatment (OR = 1.3, p < 0.001) and mechanical ventilation (OR = 1.2, p < 0.001). Among IC patients, COVID-19-related SARI profoundly increased the risk for severe courses (OR = 4.0, p < 0.001). Conclusions: Our findings highlight the vulnerability of IC patients to severe COVID-19. The persistently high prevalence of severe outcomes in these patients in the Omicron era emphasizes the necessity for continuous in-hospital risk assessment and monitoring of IC patients.

Published
2024
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82. Local Immune Activation and Age Impact on Humoral Immunity in Mice, with a Focus on IgG Sialylation

Author

Priti Gupta, Tibor Sághy, Miriam Bollmann, Tao Jin, Claes Ohlsson, Hans Carlsten, Carmen Corciulo, and Cecilia Engdahl

Subjects
humoral immune response, IgG, age, antigen-induced arthritis, IgG-sialylation, Medicine
Abstract

Age alters the host’s susceptibility to immune induction. Humoral immunity with circulating antibodies, particularly immunoglobulin G (IgG), plays an essential role in immune response. IgG glycosylation in the fragment crystallizable (Fc) region, including sialylation, is important in regulating the effector function by interacting with Fc gamma receptors (FcγRs). Glycosylation is fundamentally changed with age and inflammatory responses. We aimed to explore the regulation of humoral immunity by comparing responses to antigen-induced immune challenges in young and adult mice using a local antigen-induced arthritis mouse model. This study examines the differences in immune response between healthy and immune-challenged states across these groups. Our initial assessment of the arthritis model indicated that adult mice presented more severe knee swelling than their younger counterparts. In contrast, we found that neither histological assessment, bone mineral density, nor the number of osteoclasts differs. Our data revealed an age-associated but not immune challenge increase in total IgG; the only subtype affected by immune challenge was IgG1 and partially IgG3. Interestingly, the sialylation of IgG2b and IgG3 is affected by age and immune challenges but not stimulated further by immune challenges in adult mice. This suggests a shift in IgG towards a pro-inflammatory and potentially pathogenic state with age and inflammation.

Published
2024
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83. Super-resolution QSM in little or no additional time for imaging (NATIve) using 2D EPI imaging in 3 orthogonal planes

Author

Beata Bachrata, Steffen Bollmann, Jin Jin, Monique Tourell, Assunta Dal-Bianco, Siegfried Trattnig, Markus Barth, Stefan Ropele, Christian Enzinger, and Simon Daniel Robinson

Subjects
Super-resolution, 2D EPI, QSM, NATIve, Ultra-fast, Motion-robust, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Quantitative Susceptibility Mapping has the potential to provide additional insights into neurological diseases but is typically based on a quite long (5–10 min) 3D gradient-echo scan which is highly sensitive to motion. We propose an ultra-fast acquisition based on three orthogonal (sagittal, coronal and axial) 2D simultaneous multi-slice EPI scans with 1 mm in-plane resolution and 3 mm thick slices. Images in each orientation are corrected for susceptibility-related distortions and co-registered with an iterative non-linear Minimum Deformation Averaging (Volgenmodel) approach to generate a high SNR, super-resolution data set with an isotropic resolution of close to 1 mm. The net acquisition time is 3 times the volume acquisition time of EPI or about 12 s, but the three volumes could also replace “dummy scans” in fMRI, making it feasible to acquire QSM in little or No Additional Time for Imaging (NATIve). NATIve QSM values agreed well with reference 3D GRE QSM in the basal ganglia in healthy subjects. In patients with multiple sclerosis, there was also a good agreement between the susceptibility values within lesions and control ROIs and all lesions which could be seen on 3D GRE QSMs could also be visualized on NATIve QSMs. The approach is faster than conventional 3D GRE by a factor of 25–50 and faster than 3D EPI by a factor of 3–5. As a 2D technique, NATIve QSM was shown to be much more robust to motion than the 3D GRE and 3D EPI, opening up the possibility of studying neurological diseases involving iron accumulation and demyelination in patients who find it difficult to lie still for long enough to acquire QSM data with conventional methods.

Published
2023
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84. C-kitpos cells in the human left atrial appendage

Author

Lea Schwarzkopf, Petra Büttner, Karl Scholtyssek, Thomas Schröter, Ruth Hiller, Gerhard Hindricks, Andreas Bollmann, Ulrich Laufs, and Laura Ueberham

Subjects
c-kitpos cells, Cardiac progenitor cells, Human left atrial appendage, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Background: Subpopulations of myocardial c-kitpos cells have the ability to stimulate regeneration in ischemic heart disease by paracrine effects. The left atrial appendage (LAA), which is easy accessible during cardiac surgery, may represent a perfect source for c-kitpos cell extraction for autologous cell therapies in the living human. So far, frequency and distribution of c-kitpos cells in LAA are unknown. Methods: LAAs of patients who underwent cardiac surgery due to coronary artery disease (coronary artery bypass graft, CABG), valvular heart disease or both and of two body donors were examined. Tissue was fixed in 4 % paraformaldehyde, embedded in paraffin, dissected in consecutive sections and stained for c-kitpos cells. In parallel, grade of fibrosis, amount of fat per section and cells positive for mast cell tryptase were examined. Results: We collected 27 LAAs (37.0 % female, mean left ventricular ejection fraction 50.4 %, 63.0 % persistent atrial fibrillation (AF)). Most of the patients underwent combined CABG and valve surgery (51.9 %). C-kitpos cells were detected in 3 different regions: A) Attached to the epicardial fat layer, B) close to vascular structures and C) between cardiomyocytes. C-kitpos cells ranged from 0.05 c-kitpos cells per mm2 to 67.5 c-kitpos cells per mm2. We found no association between number of c-kitpos cells and type of AF, amount of fibrosis or amount of fat. Up to 72 % of c-kitpos cells also showed a positive staining for mast cell tryptase. Conclusion: C-kitpos cells are frequent in LAAs of cardiovascular patients with a rather homogenous distribution throughout the LAA. The LAA can therefore be considered as a source for extraction of a reasonable quantity of autologous cardiac progenitor cells in the living human patient.

Published
2023
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86. Patterns of oral anticoagulant use and outcomes in Asian patients with atrial fibrillation: a post-hoc analysis from the GLORIA-AF Registry

Author

Abban, Dzifa Wosornu, Abdul, Nasser, Abud, Atilio Marcelo, Adams, Fran, Addala, Srinivas, Adragão, Pedro, Ageno, Walter, Aggarwal, Rajesh, Agosti, Sergio, Agostoni, Piergiuseppe, Aguilar, Francisco, Linares, Julio Aguilar, Aguinaga, Luis, Ahmed, Jameel, Aiello, Allessandro, Ainsworth, Paul, Aiub, Jorge Roberto, Al-Dallow, Raed, Alderson, Lisa, Aldrete Velasco, Jorge Antonio, Alexopoulos, Dimitrios, Manterola, Fernando Alfonso, Aliyar, Pareed, Alonso, David, Alves da Costa, Fernando Augusto, Amado, José, Amara, Walid, Amelot, Mathieu, Amjadi, Nima, Ammirati, Fabrizio, Andrade, Marianna, Andrawis, Nabil, Annoni, Giorgio, Ansalone, Gerardo, Ariani, M.Kevin, Arias, Juan Carlos, Armero, Sébastien, Arora, Chander, Aslam, Muhammad Shakil, Asselman, M., Audouin, Philippe, Augenbraun, Charles, Aydin, S., Ayryanova, Ivaneta, Aziz, Emad, Backes, Luciano Marcelo, Badings, E., Bagni, Ermentina, Baker, Seth H., Bala, Richard, Baldi, Antonio, Bando, Shigenobu, Banerjee, Subhash, Bank, Alan, Esquivias, Gonzalo Barón, Barr, Craig, Bartlett, Maria, Kes, Vanja Basic, Baula, Giovanni, Behrens, Steffen, Bell, Alan, Benedetti, Raffaella, Mazuecos, Juan Benezet, Benhalima, Bouziane, Bergler-Klein, Jutta, Berneau, Jean-Baptiste, Bernstein, Richard A., Berrospi, Percy, Berti, Sergio, Berz, Andrea, Best, Elizabeth, Bettencourt, Paulo, Betzu, Robert, Bhagwat, Ravi, Bhatta, Luna, Biscione, Francesco, Bisignani, Giovanni, Black, Toby, Bloch, Michael J., Bloom, Stephen, Blumberg, Edwin, Bo, Mario, Bøhmer, Ellen, Bollmann, Andreas, Bongiorni, Maria Grazia, Boriani, Giuseppe, Boswijk, D.J., Bott, Jochen, Bottacchi, Edo, Kalan, Marica Bracic, Bradman, Drew, Brautigam, Donald, Breton, Nicolas, Brouwers, P.J.A.M., Browne, Kevin, Cortada, Jordi Bruguera, Bruni, A., Brunschwig, Claude, Buathier, Hervé, Buhl, Aurélie, Bullinga, John, Cabrera, Jose Walter, Caccavo, Alberto, Cai, Shanglang, Caine, Sarah, Calò, Leonardo, Calvi, Valeria, Sánchez, Mauricio Camarillo, Candeias, Rui, Capuano, Vincenzo, Capucci, Alessandro, Caputo, Ronald, Rizo, Tatiana Cárdenas, Cardona, Francisco, Carlos da Costa Darrieux, Francisco, Duarte Vera, Yan Carlos, Carolei, Antonio, Carreño, Susana, Carvalho, Paula, Cary, Susanna, Casu, Gavino, Cavallini, Claudio, Cayla, Guillaume, Celentano, Aldo, Cha, Tae-Joon, Cha, Kwang Soo, Chae, Jei Keon, Chalamidas, Kathrine, Challappa, Krishnan, Chand, Sunil Prakash, Chandrashekar, Harinath, Chartier, Ludovic, Chatterjee, Kausik, Chavez Ayala, Carlos Antero, Cheema, Aamir, Cheema, Amjad, Chen, Lin, Chen, Shih-Ann, Chen, Jyh Hong, Chiang, Fu-Tien, Chiarella, Francesco, Chih-Chan, Lin, Cho, Yong Keun, Choi, Jong-Il, Choi, Dong Ju, Chouinard, Guy, Hoi-Fan Chow, Danny, Chrysos, Dimitrios, Chumakova, Galina, José Roberto, Eduardo Julián, Valenzuela, Chuquiure, Nica, Nicoleta Cindea, Cislowski, David J., Clay, Anthony, Clifford, Piers, Cohen, Andrew, Cohen, Michael, Cohen, Serge, Colivicchi, Furio, Collins, Ronan, Colonna, Paolo, Compton, Steve, Connolly, Derek, Conti, Alberto, Buenostro, Gabriel Contreras, Coodley, Gregg, Cooper, Martin, Coronel, Julian, Corso, Giovanni, Sales, Juan Cosín, Cottin, Yves, Covalesky, John, Cracan, Aurel, Crea, Filippo, Crean, Peter, Crenshaw, James, Cullen, Tina, Darius, Harald, Dary, Patrick, Dascotte, Olivier, Dauber, Ira, Davalos, Vicente, Davies, Ruth, Davis, Gershan, Davy, Jean-Marc, Dayer, Mark, De Biasio, Marzia, De Bonis, Silvana, De Caterina, Raffaele, De Franceschi, Teresiano, de Groot, J.R., De Horta, José, De La Briolle, Axel, Topete, Gilberto de la Pena, Vicenzo de Paola, Angelo Amato, de Souza, Weimar, de Veer, A., De Wolf, Luc, Decoulx, Eric, Deepak, Sasalu, Defaye, Pascal, Del-Carpio Munoz, Freddy, Brkljacic, Diana Delic, Deumite, N. Joseph, Di Legge, Silvia, Diemberger, Igor, Dietz, Denise, Dionísio, Pedro, Dong, Qiang, Rossi dos Santos, Fabio, Dotcheva, Elena, Doukky, Rami, D'Souza, Anthony, Dubrey, Simon, Ducrocq, Xavier, Dupljakov, Dmitry, Duque, Mauricio, Dutta, Dipankar, Duvilla, Nathalie, Duygun, A., Dziewas, Rainer, Eaton, Charles B., Eaves, William, Ebels-Tuinbeek; Clifford Ehrlich, L.A., Eichinger-Hasenauer, Sabine, Eisenberg, Steven J., El Jabali, Adnan, El Shahawy, Mahfouz, Hernandes, Mauro Esteves, Izal, Ana Etxeberria, Evonich, Rudolph, III, Evseeva, Oksana, Ezhov, Andrey, Fahmy, Raed, Fang, Quan, Farsad, Ramin, Fauchier, Laurent, Favale, Stefano, Fayard, Maxime, Fedele, Jose Luis, Fedele, Francesco, Fedorishina, Olga, Fera, Steven R., Gomes Ferreira, Luis Gustavo, Ferreira, Jorge, Ferri, Claudio, Ferrier, Anna, Ferro, Hugo, Finsen, Alexandra, First, Brian, Fischer, Stuart, Fonseca, Catarina, Almeida, Luísa Fonseca, Forman, Steven, Frandsen, Brad, French, William, Friedman, Keith, Friese, Athena, Fruntelata, Ana Gabriela, Fujii, Shigeru, Fumagalli, Stefano, Fundamenski, Marta, Furukawa, Yutaka, Gabelmann, Matthias, Gabra, Nashwa, Gadsbøll, Niels, Galinier, Michel, Gammelgaard, Anders, Ganeshkumar, Priya, Gans, Christopher, Quintana, Antonio Garcia, Gartenlaub, Olivier, Gaspardone, Achille, Genz, Conrad, Georger, Frédéric, Georges, Jean-Louis, Georgeson, Steven, Giedrimas, Evaldas, Gierba, Mariusz, Ortega, Ignacio Gil, Gillespie, Eve, Giniger, Alberto, Giudici, Michael C., Gkotsis, Alexandros, Glotzer, Taya V., Gmehling, Joachim, Gniot, Jacek, Goethals, Peter, Goldbarg, Seth, Goldberg, Ronald, Goldmann, Britta, Golitsyn, Sergey, Gómez, Silvia, Mesa, Juan Gomez, Gonzalez, Vicente Bertomeu, Gonzalez Hermosillo, Jesus Antonio, González López, Víctor Manuel, Gorka, Hervé, Gornick, Charles, Gorog, Diana, Gottipaty, Venkat, Goube, Pascal, Goudevenos, Ioannis, Graham, Brett, Greer, G. Stephen, Gremmler, Uwe, Grena, Paul G., Grond, Martin, Gronda, Edoardo, Grönefeld, Gerian, Gu, Xiang, Torres Torres, Ivett Guadalupe, Guardigli, Gabriele, Guevara, Carolina, Guignier, Alexandre, Gulizia, Michele, Gumbley, Michael, Günther, Albrecht, Ha, Andrew, Hahalis, Georgios, Hakas, Joseph, Hall, Christian, Han, Bing, Han, Seongwook, Hargrove, Joe, Hargroves, David, Harris, Kenneth B., Haruna, Tetsuya, Hayek, Emil, Healey, Jeff, Hearne, Steven, Heffernan, Michael, Heggelund, Geir, Heijmeriks, J.A., Hemels, Maarten, Hendriks, I., Henein, Sam, Her, Sung-Ho, Hermany, Paul, Hernández Del Río, Jorge Eduardo, Higashino, Yorihiko, Hill, Michael, Hisadome, Tetsuo, Hishida, Eiji, Hoffer, Etienne, Hoghton, Matthew, Hong, Kui, Hong, Suk keun, Horbach, Stevie, Horiuchi, Masataka, Hou, Yinglong, Hsing, Jeff, Huang, Chi-Hung, Huckins, David, Hughes, kathy, Huizinga, A., Hulsman, E.L., Hung, Kuo-Chun, Hwang, Gyo-Seung, Ikpoh, Margaret, Imberti, Davide, Ince, Hüseyin, Indolfi, Ciro, Inoue, Shujiro, Irles, Didier, Iseki, Harukazu, Israel, C. Noah, Iteld, Bruce, Iyer, Venkat, Jackson-Voyzey, Ewart, Jaffrani, Naseem, Jäger, Frank, James, Martin, Jang, Sung-Won, Jaramillo, Nicolas, Jarmukli, Nabil, Jeanfreau, Robert J., Jenkins, Ronald D., Sánchez, Carlos Jerjes, Jimenez, Javier, Jobe, Robert, Joen-Jakobsen, Tomas, Jones, Nicholas, Moura Jorge, Jose Carlos, Jouve, Bernard, Jung, Byung Chun, Jung, Kyung Tae, Jung, Werner, Kachkovskiy, Mikhail, Kafkala, Krystallenia, Kalinina, Larisa, Kallmünzer, Bernd, Kamali, Farzan, Kamo, Takehiro, Kampus, Priit, Kashou, Hisham, Kastrup, Andreas, Katsivas, Apostolos, Kaufman, Elizabeth, Kawai, Kazuya, Kawajiri, Kenji, Kazmierski, John F., Keeling, P., Kerr Saraiva, José Francisco, Ketova, Galina, Khaira, Ajit Singh, Khripun, Aleksey, Kim, Doo-Il, Kim, Young Hoon, Kim, Nam Ho, Kim, Dae Kyeong, Kim, Jeong Su, Kim, June Soo, Kim, Ki Seok, Kim, Jin bae, Kinova, Elena, Klein, Alexander, Kmetzo, James J., Kneller, G. Larsen, Knezevic, Aleksandar, Angela Koh, Su Mei, Koide, Shunichi, Kollias, Anastasios, Kooistra, J.A., Koons, Jay, Koschutnik, Martin, Kostis, William J., Kovacic, Dragan, Kowalczyk, Jacek, Koziolova, Natalya, Kraft, Peter, Kragten, Johannes A., Krantz, Mori, Krause, Lars, Krenning, B.J., Krikke, F., Kromhout, Z., Krysiak, Waldemar, Kumar, Priya, Kümler, Thomas, Kuniss, Malte, Kuo, Jen-Yuan, Küppers, Achim, Karla Kurrelmeyer, Kwak, Choong Hwan, Laboulle, Bénédicte, Labovitz, Arthur, Lai, Wen Ter, Lam, Andy, Lam, Yat Yin, Zanetti, Fernando Lanas, Landau, Charles, Landini, Giancarlo, Figueiredo, Estêvão Lanna, Larsen, Torben, Lavandier, Karine, LeBlanc, Jessica, Lee, Moon Hyoung, Lee, Chang-Hoon, Lehman, John, Leitão, Ana, Lellouche, Nicolas, Lelonek, Malgorzata, Lenarczyk, Radoslaw, Lenderink, T., González, Salvador León, Leong-Sit, Peter, Leschke, Matthias, Ley, Nicolas, Li, Zhanquan, Li, Xiaodong, Li, Weihua, Li, Xiaoming, Lichy, Christhoh, Lieber, Ira, Limon Rodriguez, Ramon Horacio, Lin, Hailong, Lip, Gregory Y.H., Liu, Feng, Liu, Hengliang, Esperon, Guillermo Llamas, Navarro, Nassip Llerena, Lo, Eric, Lokshyn, Sergiy, López, Amador, López-Sendón, José Luís, Lorga Filho, Adalberto Menezes, Lorraine, Richard S., Luengas, Carlos Alberto, Luke, Robert, Luo, Ming, Lupovitch, Steven, Lyrer, Philippe, Ma, Changsheng, Ma, Genshan, Madariaga, Irene, Maeno, Koji, Magnin, Dominique, Maid, Gustavo, Mainigi, Sumeet K., Makaritsis, Konstantinos, Malhotra, Rohit, Manning, Rickey, Manolis, Athanasios, Manrique Hurtado, Helard Andres, Mantas, Ioannis, Jattin, Fernando Manzur, Maqueda, Vicky, Marchionni, Niccolo, Ortuno, Francisco Marin, Santana, Antonio Martín, Martinez, Jorge, Maskova, Petra, Hernandez, Norberto Matadamas, Matsuda, Katsuhiro, Maurer, Tillmann, Mauro, Ciro, May, Erik, Mayer, Nolan, McClure, John, McCormack, Terry, McGarity, William, McIntyre, Hugh, McLaurin, Brent, Medina Palomino, Feliz Alvaro, Melandri, Francesco, Meno, Hiroshi, Menzies, Dhananjai, Mercader, Marco, Meyer, Christian, Meyer, Beat J., Miarka, Jacek, Mibach, Frank, Michalski, Dominik, Michel, Patrik, Chreih, Rami Mihail, Mikdadi, Ghiath, Mikus, Milan, Milicic, Davor, Militaru, Constantin, Minaie, Sedi, Minescu, Bogdan, Mintale, Iveta, Mirault, Tristan, Mirro, Michael J., Mistry, Dinesh, Miu, Nicoleta Violeta, Miyamoto, Naomasa, Moccetti, Tiziano, Mohammed, Akber, Nor, Azlisham Mohd, Mollerus, Michael, Molon, Giulio, Mondillo, Sergio, Moniz, Patrícia, Mont, Lluis, Montagud, Vicente, Montaña, Oscar, Monti, Cristina, Moretti, Luciano, Mori, Kiyoo, Moriarty, Andrew, Morka, Jacek, Moschini, Luigi, Moschos, Nikitas, Mügge, Andreas, Mulhearn, Thomas J., Muresan, Carmen, Muriago, Michela, Musial, Wlodzimierz, Musser, Carl W., Musumeci, Francesco, Nageh, Thuraia, Nakagawa, Hidemitsu, Nakamura, Yuichiro, Nakayama, Toru, Nam, Gi-Byoung, Nanna, Michele, Natarajan, Indira, Nayak, Hemal M., Naydenov, Stefan, Nazlić, Jurica, Cristian Nechita, Alexandru, Nechvatal, Libor, Negron, Sandra Adela, Neiman, James, Neuenschwander, Fernando Carvalho, Neves, David, Neykova, Anna, Miguel, Ricardo Nicolás, Nijmeh, George, Nizov, Alexey, Campos, Rodrigo Noronha, Nossan, Janko, Novikova, Tatiana, Nowalany-Kozielska, Ewa, Nsah, Emmanuel, Nunez Fragoso, Juan Carlos, Nurgalieva, Svetlana, Nuyens, Dieter, Nyvad, Ole, Odin de Los Rios Ibarra, Manuel, O'Donnell, Philip, O'Donnell, Martin, Oh, Seil, Oh, Yong Seog, Oh, Dongjin, O'Hara, Gilles, Oikonomou, Kostas, Olivares, Claudia, Oliver, Richard, Ruiz, Rafael Olvera, Olympios, Christoforos, omaszuk-Kazberuk, Anna, Asensi, Joaquín Osca, Jose, eena Padayattil, Padilla Padilla, Francisco Gerardo, Rios, Victoria Padilla, Pajes, Giuseppe, Pandey, Shekhar, Paparella, Gaetano, Paris, F., Park, Hyung Wook, Park, Jong Sung, Parthenakis, Fragkiskos, Passamonti, Enrico, Patel, Rajesh J., Patel, Jaydutt, Patel, Mehool, Patrick, Janice, Jimenez, Ricardo Pavón, Paz, Analía, Pengo, Vittorio, Pentz, William, Pérez, Beatriz, Pérez Ríos, Alma Minerva, Pérez-Cabezas, Alejandro, Perlman, Richard, Persic, Viktor, Perticone, Francesco, Peters, Terri K., Petkar, Sanjiv, Pezo, Luis Felipe, Pflücke, Christian, Pham, David N., Phillips, Roland T., Phlaum, Stephen, Pieters, Denis, Pineau, Julien, Pinter, Arnold, Pinto, Fausto, Pisters, R., Pivac, Nediljko, Pocanic, Darko, Podoleanu, Cristian, Politano, Alessandro, Poljakovic, Zdravka, Pollock, Stewart, Garcéa, Jose Polo, Poppert, Holger, Porcu, Maurizio, Reino, Antonio Pose, Prasad, Neeraj, Précoma, Dalton Bertolim, Prelle, Alessandro, Prodafikas, John, Protasov, Konstantin, Pye, Maurice, Qiu, Zhaohui, Quedillac, Jean-Michel, Raev, Dimitar, Raffo Grado, Carlos Antonio, Rahimi, Sidiqullah, Raisaro, Arturo, Rama, Bhola, Ramos, Ricardo, Ranieri, Maria, Raposo, Nuno, Rashba, Eric, Rauch-Kroehnert, Ursula, Reddy, Ramakota, Renda, Giulia, Reza, Shabbir, Ria, Luigi, Richter, Dimitrios, Rickli, Hans, Rieker, Werner, Vera, Tomas Ripolil, Ritt, Luiz Eduardo, Roberts, Douglas, Briones, Ignacio Rodriguez, Rodriguez Escudero, Aldo Edwin, Pascual, Carlos Rodríguez, Roman, Mark, Romeo, Francesco, Ronner, E., Roux, Jean-Francois, Rozkova, Nadezda, Rubacek, Miroslav, Rubalcava, Frank, Russo, Andrea M., Rutgers, Matthieu Pierre, Rybak, Karin, Said, Samir, Sakamoto, Tamotsu, Salacata, Abraham, Salem, Adrien, Bodes, Rafael Salguero, Saltzman, Marco A., Salvioni, Alessandro, Vallejo, Gregorio Sanchez, Fernández, Marcelo Sanmartín, Saporito, Wladmir Faustino, Sarikonda, Kesari, Sasaoka, Taishi, Sati, Hamdi, Savelieva, Irina, Scala, Pierre-Jean, Schellinger, Peter, Scherr, Carlos, Schmitz, Lisa, Schmitz, Karl-Heinz, Schmitz, Bettina, Schnabel, Teresa, Schnupp, Steffen, Schoeniger, Peter, Schön, Norbert, Schwimmbeck, Peter, Seamark, Clare, Searles, Greg, Seidl, Karl-Heinz, Seidman, Barry, Sek, Jaroslaw, Sekaran, Lakshmanan, Serrati, Carlo, Shah, Neerav, Shah, Vinay, Shah, Anil, Shah, Shujahat, Sharma, Vijay Kumar, Shaw, Louise, Sheikh, Khalid H., Shimizu, Naruhito, Shimomura, Hideki, Shin, Dong-Gu, Shin, Eun-Seok, Shite, Junya, Sibilio, Gerolamo, Silver, Frank, Sime, Iveta, Simmers, Tim A., Singh, Narendra, Siostrzonek, Peter, Smadja, Didier, Smith, David W., Snitman, Marcelo, Filho, Dario Sobral, Soda, Hassan, Sofley, Carl, Sokal, Adam, Oi Yan, Yannie Soo, Sotolongo, Rodolfo, Ferreira de Souza, Olga, Sparby, Jon Arne, Spinar, Jindrich, Sprigings, David, Spyropoulos, Alex C., Stakos, Dimitrios, Steinwender, Clemens, Stergiou, Georgios, Stiell, Ian, Stoddard, Marcus, Stoikov, Anastas, Streb, Witold, Styliadis, Ioannis, Su, Guohai, Su, Xi, Sudnik, Wanda, Sukles, Kai, Sun, Xiaofei, Swart, H., Szavits-Nossan, Janko, Taggeselle, Jens, Takagi, Yuichiro, Singh Takhar, Amrit Pal, Tamm, Angelika, Tanaka, Katsumi, Tanawuttiwat, Tanyanan, Tang, Sherman, Tang, Aylmer, Tarsi, Giovanni, Tassinari, Tiziana, Tayal, Ashis, Tayebjee, Muzahir, Berg, J.M. ten, Tesloianu, Dan, The, Salem H.K., Thomas, Dierk, Timsit, Serge, Tobaru, Tetsuya, Tomasik, Andrzej R., Torosoff, Mikhail, Touze, Emmanuel, Trendafilova, Elina, Tsai, W. Kevin, Tse, Hung Fat, Tsutsui, Hiroshi, Tu, Tian Ming, Tuininga, Ype, Turakhia, Minang, Turk, Samir, Turner, Wayne, Tveit, Arnljot, Tytus, Richard, Valadão, C., van Bergen, P.F.M.M., van de Borne, Philippe, van den Berg, B.J., van der Zwaan, C., Van Eck, M., Vanacker, Peter, Vasilev, Dimo, Vasilikos, Vasileios, Vasilyev, Maxim, Veerareddy, Srikar, Miño; Asok Venkataraman, Mario Vega, Verdecchia, Paolo, Versaci, Francesco, Vester, Ernst Günter, Vial, Hubert, Victory, Jason, Villamil, Alejandro, Vincent, Marc, Vlastaris, Anthony, Dahl, Jürgen vom, Vora, Kishor, Vranian, Robert B., Wakefield, Paul, Wang, Ningfu, Wang, Mingsheng, Wang, Xinhua, Wang, Feng, Wang, Tian, Warner, Alberta L., Watanabe, Kouki, Wei, Jeanne, Weimar, Christian, Weiner, Stanislav, Weinrich, Renate, Wen, Ming-Shien, Wiemer, Marcus, Wiggers, Preben, Wilke, Andreas, Williams, David, Williams, Marcus L., Witzenbichler, Bernhard, Wong, Brian, Lawrence Wong, Ka Sing, Wozakowska-Kaplon, Beata, Wu, Shulin, Wu, Richard C., Wunderlich, Silke, Wyatt, Nell, Wylie, John (Jack), Xu, Yong, Xu, Xiangdong, Yamanoue, Hiroki, Yamashita, Takeshi, Bryan Yan, Ping Yen, Yang, Tianlun, Yao, Jing, Yeh, Kuo-Ho, Yin, Wei Hsian, Yotov, Yoto, Zahn, Ralf, Zarich, Stuart, Zenin, Sergei, Zeuthen, Elisabeth Louise, Zhang, Huanyi, Zhang, Donghui, Zhang, Xingwei, Zhang, Ping, Zhang, Jun, Zhao, Shui Ping, Zhao, Yujie, Zhao, Zhichen, Zheng, Yang, Zhou, Jing, Zimmermann, Sergio, Zini, Andrea, Zizzo, Steven, Zong, Wenxia, Zukerman, L. Steven, Romiti, Giulio Francesco, Corica, Bernadette, Proietti, Marco, Mei, Davide Antonio, Frydenlund, Juliane, Bisson, Arnaud, Olshansky, Brian, Chan, Yi-Hsin, Huisman, Menno V., and Chao, Tze-Fan

Published
2023
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87. Overview of quantitative susceptibility mapping using deep learning -- Current status, challenges and opportunities

Author

Jung, Woojin, Bollmann, Steffen, and Lee, Jongho

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Quantitative susceptibility mapping (QSM) has gained broad interests in the field by extracting biological tissue properties, predominantly myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source-inversion. Current state of the art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring iterative optimization nor the choice of regularization parameters. Here, we review the current status of deep learning based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.

Published
2019

88. Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks

Author

Abbasi-Rad, Shahrokh, O'Brien, Kieran, Kelly, Samuel, Vegh, Viktor, Rodell, Anders, Tesiram, Yasvir, Jin, Jin, Barth, Markus, and Bollmann, Steffen

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics
Abstract

Purpose: The purpose of this study is to demonstrate a method for Specific Absorption Rate (SAR) reduction for T2-FLAIR MRI sequences at 7T by predicting the required adiabatic pulse power and scaling the amplitude in a slice-wise fashion. Methods: We used a TR-FOCI adiabatic pulse for spin inversion in a T2-FLAIR sequence to improve B1+ homogeneity and calculate the pulse power required for adiabaticity slice-by-slice to minimize the SAR. Drawing on the implicit B1+ inhomogeneity present in a standard localizer scan, 3D AutoAlign localizers and SA2RAGE B1+ maps were acquired in eight volunteers. A convolutional neural network (CNN) was then trained to predict the B1+ profile from the localizers and scale factors for the pulse power for each slice were calculated. The ability to predict the B1+ profile as well as how the derived pulse scale factors affected the FLAIR inversion efficiency were assessed in transverse, sagittal, and coronal orientations. Results: The predicted B1+ maps matched the measured B1+ maps with a mean difference of 4.45% across all slices. The acquisition in the transverse orientation was shown to be most effective for this method and delivered a 40% reduction in SAR along with 1min and 30-sec reduction in scan time (28%) without degradation of image quality. Conclusion: We propose a SAR reduction technique based on the prediction of B1+ profiles from standard localizer scans using a CNN and show that scaling the inversion pulse power slice-by-slice for FLAIR sequences at 7T reduces SAR and scan time without compromising image quality.

Published
2019
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92. A Large-Scale Comparison of Historical Text Normalization Systems

Author

Bollmann, Marcel

Subjects
Computer Science - Computation and Language
Abstract

There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder--decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available., Comment: Accepted at NAACL 2019

Published
2019
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93. Few-Shot and Zero-Shot Learning for Historical Text Normalization

Author

Bollmann, Marcel, Korchagina, Natalia, and Søgaard, Anders

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can lead to significant improvements by exploiting synergies with related datasets, but there has been no systematic study of different multi-task learning architectures. This paper evaluates 63~multi-task learning configurations for sequence-to-sequence-based historical text normalization across ten datasets from eight languages, using autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary tasks. We observe consistent, significant improvements across languages when training data for the target task is limited, but minimal or no improvements when training data is abundant. We also show that zero-shot learning outperforms the simple, but relatively strong, identity baseline., Comment: Accepted at DeepLo-2019

Published
2019

94. Alternative strategies of nutrient acquisition and energy conservation map to the biogeography of marine ammonia-oxidizing archaea

Author

Qin, Wei, Zheng, Yue, Zhao, Feng, Wang, Yulin, Urakawa, Hidetoshi, Martens-Habbena, Willm, Liu, Haodong, Huang, Xiaowu, Zhang, Xinxu, Nakagawa, Tatsunori, Mende, Daniel R, Bollmann, Annette, Wang, Baozhan, Zhang, Yao, Amin, Shady A, Nielsen, Jeppe L, Mori, Koji, Takahashi, Reiji, Virginia Armbrust, E, Winkler, Mari-K H, DeLong, Edward F, Li, Meng, Lee, Po-Heng, Zhou, Jizhong, Zhang, Chuanlun, Zhang, Tong, Stahl, David A, and Ingalls, Anitra E

Subjects
Life Below Water, Ammonia, Archaea, Nitrification, Nutrients, Oxidation-Reduction, Phylogeny, Environmental Sciences, Biological Sciences, Technology, Microbiology
Abstract

Ammonia-oxidizing archaea (AOA) are among the most abundant and ubiquitous microorganisms in the ocean, exerting primary control on nitrification and nitrogen oxides emission. Although united by a common physiology of chemoautotrophic growth on ammonia, a corresponding high genomic and habitat variability suggests tremendous adaptive capacity. Here, we compared 44 diverse AOA genomes, 37 from species cultivated from samples collected across diverse geographic locations and seven assembled from metagenomic sequences from the mesopelagic to hadopelagic zones of the deep ocean. Comparative analysis identified seven major marine AOA genotypic groups having gene content correlated with their distinctive biogeographies. Phosphorus and ammonia availabilities as well as hydrostatic pressure were identified as selective forces driving marine AOA genotypic and gene content variability in different oceanic regions. Notably, AOA methylphosphonate biosynthetic genes span diverse oceanic provinces, reinforcing their importance for methane production in the ocean. Together, our combined comparative physiological, genomic, and metagenomic analyses provide a comprehensive view of the biogeography of globally abundant AOA and their adaptive radiation into a vast range of marine and terrestrial habitats.

Published
2020

95. Risk factors for heightened COVID‐19‐Related anxiety among breast cancer patients

Author

Yash B. Shah, Stephanie Kjelstrom, Diana Martinez, Adam Leitenberger, Donna‐Marie Manasseh, Melissa Bollmann‐Jenkins, Ann Partridge, Virginia Kaklamani, Rowen Chlebowski, Sharon Larson, and Marisa Weiss

Subjects
anxiety, breast cancer, COVID‐19, mental health, patient‐reported experiences, screening, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background The COVID‐19 pandemic has disrupted medical care, increased isolation, and exacerbated anxiety in breast cancer patients. Since March 2020, Breastcancer.org experienced a sustained surge in requested pandemic‐related information and support. To characterize the pandemic‐related experiences of breast cancer patients, we surveyed the Breastcancer.org Community early in the COVID‐19 era. Methods Breastcancer.org Community members were invited to complete an online questionnaire regarding their experience during the pandemic. Self‐reported data on demographics, comorbidities, care disruptions, anxiety, coping ability, telemedicine use, and satisfaction with care were collected. Results were analyzed using Stata 16.0 (Stata Corp., Inc). Results Included were 568 current and previous breast cancer patients, primarily with U.S. residence. Overall, 43.8% reported at least one comorbidity associated with severe COVID‐19 illness and 61.9% experienced care delays. Moderate to extreme anxiety about contracting COVID‐19 was reported by 36.5%, increasing with number of comorbidities (33.0% vs. 55.4%, p = 0.021), current breast cancer diagnosis (30.4% vs. 42.5%, p = 0.011), and poorer coping ability (15.5% vs. 53.9%, p

Published
2023
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96. Comparative Genomic Analysis of 31 Phytophthora Genomes Reveals Genome Plasticity and Horizontal Gene Transfer

Author

Brent A. Kronmiller, Nicolas Feau, Danyu Shen, Javier F. Tabima, Shahin S. Ali, Andrew D. Armitage, Felipe Arredondo, Bryan A. Bailey, Stephanie R. Bollmann, Angela Dale, Richard J. Harrison, Kelly Hrywkiw, Takao Kasuga, Rebecca McDougal, Charlotte F. Nellist, Preeti Panda, Sucheta Tripathy, Nari M. Williams, Wenwu Ye, Yuanchao Wang, Richard C. Hamelin, and Niklaus J. Grünwald

Subjects
effectors, horizontal gene transfer, oomycete plant pathogens, Phytophthora, Microbiology, QR1-502, Botany, QK1-989
Abstract

Phytophthora species are oomycete plant pathogens that cause great economic and ecological impacts. The Phytophthora genus includes over 180 known species, infecting a wide range of plant hosts, including crops, trees, and ornamentals. We sequenced the genomes of 31 individual Phytophthora species and 24 individual transcriptomes to study genetic relationships across the genus. De novo genome assemblies revealed variation in genome sizes, numbers of predicted genes, and in repetitive element content across the Phytophthora genus. A genus-wide comparison evaluated orthologous groups of genes. Predicted effector gene counts varied across Phytophthora species by effector family, genome size, and plant host range. Predicted numbers of apoplastic effectors increased as the host range of Phytophthora species increased. Predicted numbers of cytoplasmic effectors also increased with host range but leveled off or decreased in Phytophthora species that have enormous host ranges. With extensive sequencing across the Phytophthora genus, we now have the genomic resources to evaluate horizontal gene transfer events across the oomycetes. Using a machine-learning approach to identify horizontally transferred genes with bacterial or fungal origin, we identified 44 candidates over 36 Phytophthora species genomes. Phylogenetic reconstruction indicates that the transfers of most of these 44 candidates happened in parallel to major advances in the evolution of the oomycetes and Phytophthora spp. We conclude that the 31 genomes presented here are essential for investigating genus-wide genomic associations in genus Phytophthora. [Graphic: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Published
2023
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