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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Correction to: Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures

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Annals of biomedical engineering 2019年第1期47卷 332页

作者： Shalin A Parikh Raymond Gomez Mirunalini Thirugnanasambandam Sathyajeeth S Chauhan Victor De Oliveira Satish C Muluk Mark K Eskandari Ender A Finol UTSA/UTHSA Joint Graduate Program in Biomedical Engineering University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA. Department of Mechanical Engineering University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA. Department of Management Science and Statistics University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA. Department of Thoracic & Cardiovascular Surgery Allegheny Health Network Allegheny General Hospital 320 E. North Ave. Pittsburgh PA 15212 USA. Division of Vascular Surgery and Department of Radiology Northwestern University Feinberg School of Medicine 420 E. Superior St. Chicago IL 60611 USA. UTSA/UTHSA Joint Graduate Program in Biomedical Engineering University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA. ender.finol@utsa.edu. Department of Mechanical Engineering University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA. ender.finol@utsa.edu.

This erratum is to correct the variable name on the left hand side of Eq. (2). The correct variable name is "Diameter" rather than the stated "Area."

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Correction to: Material Mapping of QCT-Derived Scapular Models: A Comparison with Micro-CT Loaded Specimens Using Digital Volume Correlation

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Annals of biomedical engineering 2019年第11期47卷 2342页

作者： Nikolas K Knowles Jonathan Kusins Mohammadreza Faieghi Melissa Ryan Enrico Dall'Ara Louis M Ferreira School of Biomedical Engineering The University of Western Ontario London ON Canada. nknowle@uwo.ca. Roth|McFarlane Hand and Upper Limb Centre St. Josephs Health Care London ON Canada. nknowle@uwo.ca. Collaborative Training Program in MSK Health Research and Bone and Joint Institute The University of Western Ontario London ON Canada. nknowle@uwo.ca. Roth|McFarlane Hand and Upper Limb Centre Surgical Mechatronics Laboratory St. Josephs Health Care 268 Grosvenor St. London ON Canada. nknowle@uwo.ca. Department of Mechanical and Materials Engineering The University of Western Ontario London ON Canada. Roth|McFarlane Hand and Upper Limb Centre St. Josephs Health Care London ON Canada. Collaborative Training Program in MSK Health Research and Bone and Joint Institute The University of Western Ontario London ON Canada. School of Biomedical Engineering The University of Western Ontario London ON Canada. Department of Oncology and Metabolism and INSIGNEO Institute for In Silico Medicine University of Sheffield Sheffield UK.

The article Material Mapping of QCT-Derived Scapular Models: A Comparison with Micro-CT Loaded Specimens Using Digital Volume Correlation, written by Knowles et al, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 11 July 2019 without open access. With the author(s)' decision to opt for Open Choice the copyright of the article changed on [August 30] to © The Author(s) 2019 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://***/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Enhancing cell infiltration of electrospun fibrous scaffolds in tissue regeneration

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Bioactive Materials 2016年第1期1卷 56-64页

作者： Jinglei Wu Yi Hong Department of Bioengineering University of Texas at ArlingtonArlingtonTX 76019USA Joint Graduate Program in Biomedical Engineering University of Texas Southwestern Medical CenterDallasTX 75390USA

Electrospinning is one of the most effective approaches to fabricate tissue-engineered scaffolds composed of nano-to sub-microscale fibers that simulate a native extracellular ***,one major concern about electrospun scaffolds for tissue repair and regeneration is that their small pores defined by densely compacted fibers markedly hinder cell infiltration and tissue *** address this problem,researchers have developed and investigated various methods of manipulating scaffold structures to increase pore size or loosen the *** methods involve the use of physical treatments,such as salt leaching,gas foaming and custom-made collectors,and combined techniques to obtain electrospun scaffolds with loose fibrous structures and large *** article provides a summary of these motivating electrospinning techniques to enhance cell infiltration of electrospun scaffolds,which may inspire new electrospinning techniques and their new biomedical applications.

关键词： Electrospinning Scaffolds Porosity control Cell infiltration Tissue regeneration

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Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes

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Nature 2022年第7909期605卷 E3页

作者： Jill E Moore Michael J Purcaro Henry E Pratt Charles B Epstein Noam Shoresh Jessika Adrian Trupti Kawli Carrie A Davis Alexander Dobin Rajinder Kaul Jessica Halow Eric L Van Nostrand Peter Freese David U Gorkin Yin Shen Yupeng He Mark Mackiewicz Florencia Pauli-Behn Brian A Williams Ali Mortazavi Cheryl A Keller Xiao-Ou Zhang Shaimae I Elhajjajy Jack Huey Diane E Dickel Valentina Snetkova Xintao Wei Xiaofeng Wang Juan Carlos Rivera-Mulia Joel Rozowsky Jing Zhang Surya B Chhetri Jialing Zhang Alec Victorsen Kevin P White Axel Visel Gene W Yeo Christopher B Burge Eric Lécuyer David M Gilbert Job Dekker John Rinn Eric M Mendenhall Joseph R Ecker Manolis Kellis Robert J Klein William S Noble Anshul Kundaje Roderic Guigó Peggy J Farnham J Michael Cherry Richard M Myers Bing Ren Brenton R Graveley Mark B Gerstein Len A Pennacchio Michael P Snyder Bradley E Bernstein Barbara Wold Ross C Hardison Thomas R Gingeras John A Stamatoyannopoulos Zhiping Weng University of Massachusetts Medical School Program in Bioinformatics and Integrative Biology Worcester MA USA. The Broad Institute of Harvard and MIT Cambridge MA USA. Department of Genetics School of Medicine Stanford University Palo Alto CA USA. Cold Spring Harbor Laboratory Functional Genomics Cold Spring Harbor NY USA. Altius Institute for Biomedical Sciences Seattle WA USA. Department of Medicine University of Washington School of Medicine Seattle WA USA. Department of Cellular and Molecular Medicine Institute for Genomic Medicine Stem Cell Program Sanford Consortium for Regenerative Medicine University of California San Diego La Jolla CA USA. Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA. Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA. Ludwig Institute for Cancer Research University of California San Diego La Jolla CA USA. Institute for Human Genetics Department of Neurology University of California San Francisco San Francisco CA USA. Genomics Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA. HudsonAlpha Institute for Biotechnology Huntsville AL USA. Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA. Department of Developmental and Cell Biology University of California Irvine Irvine CA USA. Department of Biochemistry and Molecular Biology The Pennsylvania State University University Park PA USA. Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA. Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA. Département de Biochimie et Médecine Moléculaire Université de Montréal Montréal Quebec Canada. Division of Experimental Medicine McGill University Montreal Quebec Canada. Institut de Recherches Cliniques de Montréal (IR

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 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 Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

引用

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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Quantitative Computed Tomography (QCT) derived Bone Mineral Density (BMD) in finite element studies: a review of the literature

引用

Journal of Experimental Orthopaedics 2016年第1期3卷 1-16页

作者： Knowles, Nikolas K. Reeves, Jacob M. Ferreira, Louis M. Graduate Program in Biomedical Engineering The University of Western Ontario 1151 Richmond St London ON Canada Roth|McFarlane Hand and Upper Limb Centre Surgical Mechatronics Laboratory St. Josephs Health Care 268 Grosvenor St London ON Canada Collaborative Training Program in Musculoskeletal Health Research and Bone and Joint Institute The University of Western Ontario 1151 Richmond St London ON Canada Department of Mechanical and Materials Engineering The University of Western Ontario 1151 Richmond St London ON Canada

Background: Finite element modeling of human bone provides a powerful tool to evaluate a wide variety of outcomes in a highly repeatable and parametric manner. These models are most often derived from computed tomography data, with mechanical properties related to bone mineral density (BMD) from the x-ray energy attenuation provided from this data. To increase accuracy, many researchers report the use of quantitative computed tomography (QCT), in which a calibration phantom is used during image acquisition to improve the estimation of BMD. Since model accuracy is dependent on the methods used in the calculation of BMD and density-mechanical property relationships, it is important to use relationships developed for the same anatomical location and using the same scanner settings, as these may impact model accuracy. The purpose of this literature review is to report the relationships used in the conversion of QCT equivalent density measures to ash, apparent, and/or tissue densities in recent finite element (FE) studies used in common density-modulus relationships. For studies reporting experimental validation, the validation metrics and results are presented. Results: Of the studies reviewed, 29% reported the use of a dipotassium phosphate (K₂HPO₄) phantom, 47% a hydroxyapatite (HA) phantom, 13% did not report phantom type, 7% reported use of both K₂HPO₄ and HA phantoms, and 4% alternate phantom types. Scanner type and/or settings were omitted or partially reported in 31% of studies. The majority of studies used densitometric and/or density-modulus relationships derived from different anatomical locations scanned in different scanners with different scanner settings. The methods used to derive various densitometric relationships are reported and recommendations are provided toward the standardization of reporting metrics. Conclusions: This review assessed the current state of QCT-based FE modeling with use of clinical scanne

关键词： Bone density Finite element analysis Mechanical properties QCT

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Air-Impedance electrospinning technique for cartilage regeneration

Air-Impedance electrospinning technique for cartilage regene...

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Fiber Society's Spring 2015 Conference, in conjunction with the 2015 International Conference on Advanced Fibers and Polymer Materials: Functional Fibers and Textiles

作者： Schreyack, Gretchen E. Fetz, Allison E. Bowlin, Gary L. Department of Biomedical Engineering University of Memphis-UTHSC-Memphis Biomedical Engineering Program Memphis University of Memphis and Joint TN United States

Here we present a system for air-impedance electrospinning to generate templates with optimized pore sizes that allow for directed 3D Articular Cartilage (AC, a.k.a. hyaline cartilage) engineering.

关键词： Pore size

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