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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Correction: Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses

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BMC public health 2022年第1期22卷 1250页

作者： Charisse Madlock-Brown Ken Wilkens Nicole Weiskopf Nina Cesare Sharmodeep Bhattacharyya Naomi O Riches Juan Espinoza David Dorr Kerry Goetz Jimmy Phuong Anupam Sule Hadi Kharrazi Feifan Liu Cindy Lemon William G Adams Health Informatics and Information Management University of Tennessee Health Science Center 66 North Pauline St. rm 221 Memphis TN 38163 USA. cmadlock@uthsc.edu. Health Outcomes and Policy Research Program University of Tennessee Health Science Center Memphis TN USA. cmadlock@uthsc.edu. National Institute of Diabetes and Digestive and Kidney Diseases Bethesda MD USA. Medical Informatics and Clinical Epidemiology Oregon Health & Science University Portland OR USA. Biostatistics and Epidemiology Data Analytics Center Boston University Boston MA USA. Department of Statistics Oregon State University Corvallis OR USA. Obstetrics and Gynecology University of Utah School of Medicine Salt Lake City UT USA. Department of Pediatrics Children's Hospital Los Angeles Los Angeles CA USA. National Eye Institute Bethesda MD USA. University of Washington Research Information Technologies Seattle WA USA. Harborview Injury Prevention Research Center Seattle WA USA. Internal Medicine St Joseph Mercy Oakland Hospital Pontiac MI USA. Johns Hopkins School of Public Health Baltimore MD USA. Chan Medical School University of Massachusetts Worcester MA USA. Health Outcomes and Policy Research Program University of Tennessee Health Science Center Memphis TN USA. Boston Medical Center/Boston University School of Medicine Boston MA USA.

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Personalized medicine and Hispanic health: improving health outcomes and reducing health disparities - a National Heart, Lung, and Blood Institute workshop report

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BMC proceedings 2017年第SUPPL 11期11卷 11页

作者： M Larissa Avilés-Santa John Heintzman Nangel M Lindberg Rafael Guerrero-Preston Kenneth Ramos Ana L Abraído-Lanza Jonca Bull Adolph Falcón Mary Ann McBurnie Ernest Moy George Papanicolaou Ileana L Piña Jennifer Popovic Shakira F Suglia Miguel A Vázquez Division of Cardiovascular Sciences National Heart Lung and Blood Institute 6701 Rockledge Drive Room 10188 Bethesda MD 20892-7936 USA. Department of Family Medicine Oregon Health and Science University 318 SW Sam Jackson Park Rd Portland OR 97239 USA. Kaiser Permanente Northwest Center for Health Research 3800 N. Interstate Ave Portland OR 97227 USA. Johns Hopkins University School of Medicine 1550 Orleans Street CRB2 Room 5M Baltimore MD 21231 USA. University of Arizona Health Sciences 1295 North Martin Avenue PO Box 210202 Tucson AZ 86721 USA. Columbia University Mailman School of Public Health 722 West 168th Street New York NY 10032 USA. Office of Minority Health U.S. Food and Drug Administration 10903 New Hampshire Avenue Silver Spring MD 20993 USA. National Alliance for Hispanic Health 1600 P St NW Washington DC 20009 USA. National Center for Health Statistics 3311 Toledo Road Hyattsville MD 20782 USA. Albert Einstein College of Medicine Montefiore Heart and Vascular Center 111 East 210th Street Bronx NY 10467-2401 USA. Program for Health Data and Standardized Methods Center for Health Data Analytics | eHealth Quality & Analytics Division RTI International | 307 Waverley Oaks Road Suite 101 Waltham MA 02452 USA. Rollins School of Public Health Emory University 1518 Clifton Rd Rm 4005 Atlanta GA 30322 USA. Department of Internal Medicine University of Texas Southwestern Medical Center 5323 Harry Hines Boulevard Dallas TX 75390-8856 USA.

Persons of Hispanic/Latino descent may represent different ancestries, ethnic and cultural groups and countries of birth. In the U.S., the Hispanic/Latino population is projected to constitute 29% of the population by 2060. A personalized approach focusing on individual variability in genetics, environment, lifestyle and socioeconomic determinants of health may advance the understanding of some of the major factors contributing to the health disparities experienced by Hispanics/Latinos and other groups in the U.S., thus leading to new strategies that improve health care outcomes. However, there are major gaps in our current knowledge about how personalized medicine can shape health outcomes among Hispanics/Latinos and address the potential factors that may explain the observed differences within this heterogeneous group, and between this group and other U.S. demographic groups. For that purpose, the National Heart, Lung, and Blood Institute (NHLBI), in collaboration with the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), and the Food and Drug Administration (FDA), held a workshop in which experts discussed (1) potential approaches to study medical treatments and health outcomes among Hispanics/Latinos and garner the necessary evidence to fill gaps of efficacy, effectiveness and safety of therapies for heart, lung, blood and sleep (HLBS) disorders and conditions--and their risk factors; (2) research opportunities related to personalized medicine to improve knowledge and develop effective interventions to reduce health disparities among Hispanics/Latinos in the U.S.; and (3) the incorporation of expanded sociocultural and socioeconomic data collection and genetic/genomic/epigenetic information of Hispanic/Latino patients into their clinical assessments, to account for individual variability in ancestry; physiology or disease risk; culture; environment; lifestyle; and socioeconomic determinants of health. The experts also provided recommendat

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Serum Arachidonic Acid Oxylipins Indicate Small Vessel Injury And the Related Executive Function Decline In Stroke Patients

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Alzheimer's & Dementia 2023年第S15期19卷

作者： Di Yu Nuanyi Liang Lisa Y. Xiong Qing Shen William Z. Lin Miracle Ozzoude Maged Goubran Jennifer S. Rabin Joel Ramirez Sean Symons Seyyed MH Haddad Brian Tan Nuwan D. Nanayakkara Allison Ann Dilliott Malcolm Binns Derek Beaton Stephen R. Arnott Jane M Lawrenece-Dewar Demetrios J. Sahlas Leanne Casaubon Yurika Otoki Krista L. Lanctôt Mario Masellis Richard H. Swartz Ameer Y. Taha Walter Swardfager The ONDRI Investigators Department of Pharmacology and Toxicology University of Toronto Toronto ON Canada Sandra Black Centre for Brain Resilience and Recovery Sunnybrook Research Institute Toronto ON Canada Correspondece University of California Davis Davis CA USA Physical Sciences Platform Sunnybrook Research Institute University of Toronto Toronto ON Canada Department of Medical Biophysics University of Toronto Toronto ON Canada Harquail Centre for Neuromodulation Sunnybrook Research Institute Toronto ON Canada Division of Neurology Department of Medicine University of Toronto Toronto ON Canada Rehabilitation Sciences Institute University of Toronto Toronto ON Canada Physical Sciences Sunnybrook Research Institute Toronto ON Canada Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto ON Canada University of Toronto Toronto ON Canada Robarts Research Institute London ON Canada Rotman Research Institute Toronto ON Canada University of Western Ontario London ON Canada Western University London ON Canada Data Science & Advanced Analytics St. Michael’s Hospital Unity Health Toronto Toronto ON Canada Baycrest Hospital Toronto ON Canada Thunder Bay Regional Health Research Institute Northern Ontario School of Medicine University Thunder Bay ON Canada Department of Medicine McMaster University Hamilton ON Canada Division of Agricultural Chemistry Graduate School of Agricultural Science Tohoku University Sendai Tohoku Japan Department of Psychiatry Temerty Faculty of Medicine University of Toronto Toronto ON Canada Toronto Rehabilitation Institute Toronto ON Canada

Background Cerebral small vessel disease (SVD) frequently co-occurs with other neurodegenerative pathologies and collectively lead to cognitive deficits. Previously, we found that the ratios of soluble epoxide hydrolase (sEH)- derived diols to the parent epoxides (generated by CYP2J/2Cs) linoleic acids were associated with greater white matter hyperintensities (WMH) and poorer executive function in patients with transient ischemic attack. However, the associations with the diol and epoxide species derived from arachidonic acid (AA) have not been explored. Methods The study cohort is comprised of 159 stroke patients (mean age = 69.2, 32.1% female) from the Ontario Neurodegenerative Degenerative Research Initiative. EpETrEs (AA epoxides) and DiHETrEs (AA diols) were quantified by a targeted ultrahigh pressure liquid chromatography mass spectrometry (UPLC-MS/MS). Executive function was assessed using a composite score that included Digit Symbol Substitution Test (DSST), Trial-Making Test Part B, Stroop Color-Word Interference Test, and FAS Verbal Fluency Test. WMHs were quantified from T2-Fluid-Attenuated-Inversion-Recovery MRI through Lesion Explorer (https://***/***).In linear models controlling for age, sex, education or age, sex, glucose, cholesterol, and waist circumferences were used for cognitive and imaging out comes, respectively. Results 11(12)-EpETrE was found to be associated with less WHM (ρ = -0.175, p = 0.040) and better executive function (ρ = 0.166, p = 0.039). In a sub-cohort of patients with small vessel stroke (n = 50), the relationship between WMH and 11(12)-EpETrE became stronger (ρ = -0.390, p = 0.014). 14(15)-EpETrE was also associated with less WMH (ρ = -0.416, p = 0.009). Similar relationships were not seen with the diols or diol to epoxide ratios. Conclusions Oxylipins derived from sEH activity may be potential biomarkers of vascular cognitive impairment and small vessel disease in stroke patients.

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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579P Intratumoral fusobacterium as prognostic factor in early stage colorectal cancer: Results of the FUSOMAP study

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Annals of Oncology 2024年 35卷 S469-S470页

作者： P.G. Nuciforo F. Moratalla Navarro I. Baraibar Argota L. Alonso G. serna J. Vidal Barrull L. Aguilera Castro N. Mulet Margalef A. Fernandez Montes M. Cuatrecasas I. Gardeazabal Gonzalez E. Gonzalez P. García Alfonso S. Rosello Keranen R. Comas N. Bayo Puxan R. FASANI M. Obon M.E. Elez Fernandez V. Moreno Aguado Molecular Oncology Dept. Vall d'Hebron Institute of Oncology - Cellex Center Barcelona Spain Programa d’Analítica de Dades Oncològiques (PADO) ICO - Institut Català d'Oncologia l'Hospitalet (Hospital Duran i Reynals) L'Hospitalet De Llobregat Spain Oncology Department Vall d'Hebron University Hospital Barcelona Spain Medical Oncology Department Hospital del Mar Research Institute Universitat Pompeu Fabra CIBERONC Barcelona Spain Gastroenterology Vall d'Hebron University Hospital Barcelona Spain Medical Oncology ICO - Institut Català d'Oncologia Badalona (Hospital Universitario Germans Trias i Pujol) Badalona Spain Dept. Medical Oncology Complexo Hospitalario Universitario de Ourense Ourense Spain Pathology Hospital Clinic y Provincial de Barcelona Barcelona Spain Medical Oncology Department HUMV - Hospital Universitario Marques de Valdecilla Santander Spain Medical Oncology Hospital Universitario Virgen de las Nieves Granada Spain Medical Oncology Hospital Universitario Gregorio Marañón Universidad Complutense Madrid Spain Medical Oncology Hospital Clinico Universitario de Valencia Valencia Valencia Spain Oncology Data Science Group (ODysSey) VHIO - Vall d'Hebron Institute of Oncology Barcelona Spain Project Management VHIO - Vall d'Hebron Institute of Oncology Barcelona Spain Oncology Data Analytics Program ICO - Institut Català d'Oncologia l'Hospitalet (Hospital Duran i Reynals) Hospitalet De Llobregat Spain Medical Oncology Vall d’Hebron University Hospital and Vall d’Hebron Institute of Oncology UVic-UCC Barcelona Spain

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201P Phase II study of taminadenant (A2AR antagonist) + spartalizumab (anti PD-1 antibody) in patients with triple-negative breast cancer (TNBC)

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Immuno-Oncology and Technology 2022年 16卷 100312-100312页

作者： Jerusalem, G. De Braud, F.G.M. de Jonge, M.J.A. Grell, P. Gonçalves, A. Tan, T.J.Y. Greil, R. Yap, T.A. Lin, C-C. Kornbluth, K.A. Yang, X. Vong, C. Choudhury, S. Mataraza, J. Lee, L.H. Otero, J.A. Garralda, E. Medical Oncology CHU Liege and Liege University Liège Belgium Medical Oncology & Haemathology Department University of Milan and Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Medical Oncology Department Erasmus MC Cancer Institute Rotterdam Netherlands Medical Oncology Masaryk Memorial Cancer Institute Brno Czech Republic Medical Oncology Department Institut Paoli-Calmettes and Aix-Marseille University Marseille France Medical Oncology Department National Cancer Centre Singapore Medical Oncology Paracelsus Medical University Hospital Cancer Research Institute-Center for Clinical Cancer and Immunology Trials Gmbh and Cancer Cluster Salzburg Austria Investigational Cancer Therapeutics Dept. (Phase I Program) The University of Texas M. D. Anderson Cancer Center Houston TX USA Department of Oncology National Taiwan University Hospital Taipei Taiwan Translational Clinical Oncology Novartis Institutes for BioMedical Research Cambridge MA USA Early Development Biostatistics Novartis Institutes for BioMedical Research Cambridge MA USA Early Development Analytics Novartis Institutes for BioMedical Research Basel Switzerland Clinical Pharmacology Novartis Institutes for BioMedical Research Cambridge MA USA Oncology Novartis Institutes for BioMedical Research Cambridge MA USA Oncology Data Science Novartis Institutes for BioMedical Research Cambridge MA USA Early Drug Development Group Hospital Universitari Vall d'Hebron Barcelona Spain

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