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STREAMLINE: AN AUTOMATED MACHINE LEARNING PIPELINE FOR BIOMEDICINE APPLIED TO EXAMINE THE UTILITY OF PHOTOGRAPHY-BASED PHENOTYPES FOR OSA PREDICTION ACROSS INTERNATIONAL SLEEP CENTERS

arXiv

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arXiv 2023年

作者： Urbanowicz, Ryan J. Bandhey, Harsh Keenan, Brendan T. Maislin, Greg Hwang, Sy Mowery, Danielle L. Lynch, Shannon M. Mazzotti, Diego R. Han, Fang Li, Qing Yun Penzel, Thomas Tufik, Sergio Bittencourt, Lia Gislason, Thorarinn de Chazal, Philip Singh, Bhajan McArdle, Nigel Chen, Ning-Hung Pack, Allan Schwab, Richard J. Cistulli, Peter A. Magalang, Ulysses J. Department of Computational Biomedicine Cedars Sinai Medical Center Los AngelesCA United States Division of Sleep Medicine Department of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Cancer Prevention and Control Program Fox Chase Cancer Center PhiladelphiaPA United States Division of Medical Informatics Department of Internal Medicine University of Kansas LawrenceKS United States Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine University of Kansas LawrenceKS United States Division of Sleep Medicine Peking University People’s Hospital Beijing China Department of Respiratory and Critical Care Medicine Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Interdisciplinary Center of Sleep Medicine Charite University Hospital Berlin Germany Department of Psychobiology Federal University of Sao Paulo Sao Paulo Brazil Department of Sleep Medicine Landspitali University Hospital Reykjavik Iceland University of Iceland Faculty of Medicine Reykjavik Iceland Charles Perkins Center Faculty of Engineering University of Sydney Sydney Australia Department of Pulmonary Physiology and Sleep Medicine Sir Charles Gairdner Hospital Nedlands Australia University of Western Australia Crawley Australia Department of Pulmonary and Critical Care Medicine Sleep Center Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Charles Perkins Centre Faculty of Medicine and Health University of Sydney Royal North Shore Hospital Sydney Australia Division of Pulmonary Critical Care and Sleep Medicine Department of Medicine Ohio State University Wexner Medical Center ColumbusOH United States 700 N. San Vicente Blvd. Pacific Design Center Suite G541E Los AngelesCA90069 United States

Objective: While machine learning (ML) includes a valuable array of tools for analyzing biomedical data with multivariate and complex underlying associations, significant time and expertise is required to assemble effective, rigorous, comparable, reproducible, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA);a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. Methods: STREAMLINE is designed to tackle biomedical binary classification tasks while (1) avoiding common mistakes, (2) accommodating complex associations and common data challenges, and (3) allowing scalability, reproducibility, and model interpretation. It automates the majority of established, generalizable, and reliably automatable aspects of an ML analysis pipeline while incorporating cutting edge algorithms and providing opportunities for human-in-the-loop customization. We present a broadly refactored and extended release of STREAMLINE, validating and benchmarking performance across simulated and real-world datasets. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting ‘any OSA’ or ‘moderate/severe OSA’ using 3,111 participants from Sleep Apnea Global interdisciplinary Consortium (SAGIC). Results: Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association including epistatic interactions and genetic heterogeneity. OSA analyses identified a significant increase in ROC-AUC when adding CF t

关键词： Machine learning

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Neuroprosthetics: Durable and Fatigue-Resistant Soft Peripheral Neuroprosthetics for In Vivo Bidirectional Signaling (Adv. Mater. 20/2021)

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Advanced Materials 2021年第20期33卷

作者： Hyunseon Seo Sang Ihn Han Kang-Il Song Duhwan Seong Kyungwoo Lee Sun Hong Kim Taesung Park Ja Hoon Koo Mikyung Shin Hyoung Won Baac Ok Kyu Park Soong Ju Oh Hyung-Seop Han Hojeong Jeon Yu-Chan Kim Dae-Hyeong Kim Taeghwan Hyeon Donghee Son School of Medicine Sungkyunkwan University Suwon 16419 Republic of Korea Center for Biomaterials Biomedical Research Institute Korea Institute of Science and Technology Seoul 02792 Republic of Korea Center for Nanoparticle Research Institute of Basic Science (IBS) Seoul 08826 Republic of Korea School of Chemical and Biological Engineering Seoul National University Seoul 08826 Republic of Korea Medical Device Development Center Daegu-Gyeongbuk Medical Innovation Foundation Daegu 41061 Republic of Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Department of Electrical and Computer Engineering Inter-University Semiconductor Research Center Seoul National University Seoul 08826 Republic of Korea Department of Materials Science and Engineering Korea University Seoul 02841 Republic of Korea Interdisciplinary Program for Bioengineering Seoul National University Seoul 08826 Republic of Korea Department of Biomedical Engineering SKKU Institute for Convergence Sungkyunkwan University Suwon 16419 Republic of Korea Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon 16419 Republic of Korea Department of Superintelligence Engineering Sungkyunkwan University Suwon 16419 Republic of Korea

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Modulation of Foreign Body Reaction against PDMS Implant by Grafting Topographically Different Poly(Acrylic Acid) Micropatterns

SSRN

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SSRN 2019年

作者： Lee, Jae Sang Shin, Byung Ho Yoo, Byoung Yong Nam, Sun-Young Lee, Miji Park, Hansoo Choy, Young Bin Heo, Chan Yeong Koh, Won-Gun Department of Chemical and Biomolecular Engineering Yonsei University Seoul03722 Korea Republic of Department of Biomedical Engineering College of Medicine Seoul National University Seoul03080 Korea Republic of Interdisciplinary Program for Bioengineering College of Engineering Seoul National University Seoul08826 Korea Republic of Institute of Medical & Biological Engineering Medical Research Center Seoul National University Seoul03080 Korea Republic of Department of Plastic and Reconstructive Surgery College of Medicine Seoul National University Seoul03080 Korea Republic of Department of Plastic and Reconstructive Surgery Seoul National University Bundang Hospital Seongnam13620 Korea Republic of School of Integrative Engineering Chung-Ang University Korea Republic of

The surface of poly(dimethylsiloxane) (PDMS) was grafted with poly(acrylic acid) (PAA) layers via surface-initiated photopolymerization to suppress the capsular contracture resulting from a foreign body reaction. Owing to the nature of photo-induced polymerization, various PAA micropatterns could be fabricated using photolithography. Hole and stripe micropatterns approximately 100-μm wide and 3-μm thick were grafted onto the PDMS surface without delamination. The incorporation of PAA micropatterns provided not only chemical cues by hydrophilic PAA microdomains but also topographical cues by hole or stripe micropatterns. In vitro studies revealed that a PAA-grafted PDMS surface had a lower proliferation of both macrophages and fibroblasts regardless of the pattern presence. However, PDMS with PAA micropatterns, especially stripe micropatterns, minimized the aggregation of fibroblasts and their subsequent differentiation into myofibroblasts. An in vivo study also showed that PDMS samples with stripe micropatterns polarized macrophages into anti-inflammatory M2 macrophages and most effectively inhibited capsular contracture, which was demonstrated by investigating the inflammation score, transforming-growth-factor-β expression, number of macrophages, and myofibroblasts as well as the collagen density and capsule thickness. © 2019, The Authors. All rights reserved.

关键词： Macrophages

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Connecting distant entities with induction through conditional random fields for named entity recognition: Precursor-induced CRF

arXiv

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arXiv 2018年

作者： Lee, Wangjin Choi, Jinwook Interdisciplinary Program for Bioengineering Seoul National University Korea Republic of Department of Biomedical Engineering College of Medicine Seoul National University Korea Republic of Institute of Medical and Biological Engineering Medical Research Center Seoul National University Korea Republic of

This paper presents a method of designing specific high-order dependency factor on the linear chain conditional random fields (CRFs) for named entity recognition (NER). Named entities tend to be separated from each other by multiple outside tokens in a text, and thus the first-order CRF as well as the second-order CRF may innately lose transition information between distant named entities. The proposed design uses outside label in NER as a transmission medium of precedent entity information on the CRF. Then, empirical results apparently demonstrate that it is possible to exploit long-distance label dependency in the original first-order linear chain CRF structure upon NER while reducing computational loss rather than in the second-order CRF. Copyright © 2018, The Authors. All rights reserved.

关键词： Random processes

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The application of an extracellular vesicle-based biosensor in early diagnosis and prediction of chemoresponsiveness in ovarian cancer

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Cancer Letters 2024年 581卷 216533-216533页

作者： Tsang, Benjamin K. Asare-Werehene, Meshach Hunter, Rob Gerber, Emma Reunov, Arkadiy Brine, Isaiah Chang, Chia-Yu Chang, Chia-Ching Shieh, Dar-Bin Burger, Dylan Anis, Hanan Departments of Obstetrics & Gynecology and Cellular & Molecular Medicine Centre for Infection Immunity and Inflammation Interdisciplinary School of Health Sciences University of Ottawa Ottawa ON K1N 6N5 Canada Chronic Disease Program Ottawa Hospital Research Institute The Ottawa Hospital Ottawa ON K1Y 4E9 Canada Department of Biology St. Francis Xavier University 2320 Notre Dame Avenue Antigonish NS B2G 2W5 Canada School of Electrical Engineering and Computer Science Ottawa-Carleton Institute for Biomedical Engineering University of Ottawa Ottawa ON K1N 6N5 Canada Department of Biological Science and Department of Electrophysics Technology Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 30068 Taiwan Institute of Basic Medical Science Institute of Oral Medicine and Department of Stomatology Advanced Optoelectronic Technology Center and Center for Micro/Nano Science and Technology National Cheng Kung University Hospital National Cheng Kung University Tainan 704 Taiwan Institute of Physics Academia Sinica Taipei 10529 Taiwan

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Intra-condensate demixing of TDP-43 inside stress granules generates pathological aggregates

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Cell 2025年

作者： Xiao Yan David Kuster Priyesh Mohanty Jik Nijssen Karina Pombo-García Jorge Garcia Morato Azamat Rizuan Titus M Franzmann Aleksandra Sergeeva Anh M Ly Feilin Liu Patricia M Passos Leah George Szu-Huan Wang Jayakrishna Shenoy Helen L Danielson Busra Ozguney Alf Honigmann Yuna M Ayala Nicolas L Fawzi Dennis W Dickson Wilfried Rossoll Jeetain Mittal Simon Alberti Anthony A Hyman Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) Dresden 01307 Saxony Germany. Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 USA. Department of Neuroscience Mayo Clinic Jacksonville FL 32224 USA. Biotechnology Center (BIOTEC) Center for Molecular and Cellular Bioengineering Technische Universität Dresden Dresden 01307 Saxony Germany. Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) Dresden 01307 Saxony Germany Edward Doisy Department of Biochemistry and Molecular Biology Saint Louis University St. Louis MO 63104 USA. Department of Molecular Biology Cell Biology & Biochemistry Brown University Providence RI 02912 USA. Center for Biomedical Engineering Brown University Providence RI 02912 USA. Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 USA Department of Chemistry Texas A&M University College Station TX 77843 USA Interdisciplinary Graduate Program in Genetics and Genomics Texas A&M University College Station TX 77843 USA. Electronic address: jeetain@tamu.edu. Biotechnology Center (BIOTEC) Center for Molecular and Cellular Bioengineering Technische Universität Dresden Dresden 01307 Saxony Germany. Electronic address: simon.alberti@tu-dresden.de. Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) Dresden 01307 Saxony Germany. Electronic address: hyman@mpi-cbg.de.

Cytosolic aggregation of the nuclear protein TAR DNA-binding protein 43 (TDP-43) is associated with many neurodegenerative diseases, but the triggers for TDP-43 aggregation are still debated. Here, we demonstrate that TDP-43 aggregation requires a double event. One is up-concentration in stress granules beyond a threshold, and the other is oxidative stress. These two events collectively induce intra-condensate demixing, giving rise to a dynamic TDP-43-enriched phase within stress granules, which subsequently transition into pathological aggregates. Intra-condensate demixing of TDP-43 is observed in iPS-motor neurons, a disease mouse model, and patient samples. Mechanistically, intra-condensate demixing is triggered by local unfolding of the RRM1 domain for intermolecular disulfide bond formation and by increased hydrophobic patch interactions in the C-terminal domain. By engineering TDP-43 variants resistant to intra-condensate demixing, we successfully eliminate pathological TDP-43 aggregates in cells. We suggest that up-concentration inside condensates followed by intra-condensate demixing could be a general pathway for protein aggregation.

关键词： ALS FTD TDP-43 biomolecular condensate intra-condensate demixing neurodegenerative diseases oxidative stress phase separation protein aggregation stress granules

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Cover Feature: Synthesis and Photophysical Properties of Hexabenzocoronene-Tetrabenzoporphyrin Architectures (Eur. J. Org. Chem. 40/2020)

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European Journal of Organic Chemistry 2020年第40期2020卷

作者： Michael Ruppel Lampros-Pascal Gazetas Dr. Dominik Lungerich Dr. Norbert Jux Department of Chemistry and Pharmacy & Interdisciplinary Center for Molecular Materials (ICMM) Friedrich-Alexander University Erlangen-Nuremberg Nikolaus-Fiebiger-Str. 10 91058 Erlangen Germany Institute for Basic Science (IBS) 50 Yonsei-ro Seodaemun-gu Center for Nanomedicine 03722 Seoul Republic of Korea Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University 03722 Seoul Republic of Korea

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Photoacoustic Imaging: An IR820 Dye–Protein Complex for Second Near-Infrared Window and Photoacoustic Imaging (Advanced Optical Materials 4/2020)

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Advanced Optical Materials 2020年第4期8卷

作者： Bulin Du Chunrong Qu Kun Qian Ying Ren Yesen Li Xiaohong Cui Shuqing He Yifan Wu Timothy Ko Ruiqi Liu Xuena Li Yaming Li Zhen Cheng Department of Nuclear Medicine The First Hospital of China Medical University Shenyang 110001 China Molecular Imaging Program at Stanford (MIPS) Canary Center at Stanford for Cancer Early Detection Department of Radiology and Bio-X Program School of Medicine Stanford University Palo Alto CA 94305-5484 USA Academy for Advanced Interdisciplinary Studies and Department of Biomedical Engineering Southern University of Science and Technology (SUSTech) Shenzhen 518055 China Institute of Molecular Medicine College of Life and Health Sciences Northeastern University Shenyang 110000 China

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Silicone breast implant modification review: Overcoming capsular contracture

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Biomaterials Research 2018年第1期22卷 319-327页

作者： Shin, Byung Ho Kim, Byung Hwi Kim, Sujin Lee, Kangwon Choy, Young Bin Heo, Chan Yeong Seoul National University College of Medicine Department of Biomedical Engineering Seoul 03080 South Korea Seoul National University Department of Transdisciplinary Studies Graduate School of Convergence Science and Technology Seoul 08826 South Korea Seoul National University Interdisciplinary Program for Bioengineering College of Engineering Seoul 08826 South Korea Seoul National University Department of Plastic and Reconstructive Surgery College of Medicine Seoul 03080 South Korea Seoul National University Bundang Hospital Department of Plastic and Reconstructive Surgery Seongnam 13620 South Korea Seoul National University Institute of Medical and Biological Engineering Medical Research Center Seoul 03080 South Korea Advanced Institutes of Convergence Technology Suwon Gyeonggi-do 16229 South Korea

Background: Silicone implants are biomaterials that are frequently used in the medical industry due to their physiological inertness and low toxicity. However, capsular contracture remains a concern in long-term transplantation. To date, several studies have been conducted to overcome this problem. This review summarizes and explores these trends. Main body: First, we examined the overall foreign body response from initial inflammation to fibrosis capsule formation in detail and introduced various studies to overcome capsular contracture. Secondly, we introduced that the main research approaches are to inhibit fibrosis with anti-inflammatory drugs or antibiotics, to control the topography of the surface of silicone implants, and to administer plasma treatment. Each study examined aspects of the various mechanisms by which capsular contracture could occur, and addressed the effects of inhibiting fibrosis. Conclusion: This review introduces various silicone surface modification methods to date and examines their limitations. This review will help identify new directions in inhibiting the fibrosis of silicone implants. © 2018 The Author(s).

关键词： Capsular contracture Fibrosis Foreign body reaction Inflammation Modification Silicone implant

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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