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Publisher Correction: Serotonin signals through a gut-liver axis to regulate hepatic steatosis

Nature communications 2019年第1期10卷 158页

作者： Wonsuk Choi Jun Namkung Inseon Hwang Hyeongseok Kim Ajin Lim Hye Jung Park Hye Won Lee Kwang-Hyub Han Seongyeol Park Ji-Seon Jeong Geul Bang Young Hwan Kim Vijay K Yadav Gerard Karsenty Young Seok Ju Chan Choi Jae Myoung Suh Jun Yong Park Sangkyu Park Hail Kim Graduate School of Medical Science and Engineering KAIST Daejeon 34141 Republic of Korea. Department of Biochemistry Yonsei University Wonju College of Medicine Wonju 26426 Republic of Korea. Biomedical Science and Engineering Interdisciplinary Program KAIST Daejeon 34141 Republic of Korea. Department of Internal Medicine Yonsei University College of Medicine Yonsei Liver Center Severance Hospital Seoul 03722 Republic of Korea. Center for Bioanalysis Division of Metrology for Quality of Life Korea Research Institute of Standards and Science Daejeon 34113 Republic of Korea. Biomedical Omics Group Korea Basic Science Institute Chungbuk 28119 Republic of Korea. Department of Genetics and Development Columbia University Medical Center New York NY 10032 USA. Department of Pathology Chonnam National University Medical School Gwangju 61469 Republic of Korea. Department of Internal Medicine Yonsei University College of Medicine Yonsei Liver Center Severance Hospital Seoul 03722 Republic of Korea. drpjy@yuhs.ac. Graduate School of Medical Science and Engineering KAIST Daejeon 34141 Republic of Korea. 49park@cku.ac.kr. Department of Biochemistry College of Medicine Catholic Kwandong University Gangneung 25601 Republic of Korea. 49park@cku.ac.kr. Graduate School of Medical Science and Engineering KAIST Daejeon 34141 Republic of Korea. hailkim@kaist.edu. Biomedical Science and Engineering Interdisciplinary Program KAIST Daejeon 34141 Republic of Korea. hailkim@kaist.edu. KAIST Institute for the BioCentury KAIST Daejeon 34141 Republic of Korea. hailkim@kaist.edu.

The originally published version of this Article contained an error in Figure 2. In panel g, the image of brown adipose tissue from SCD-fed Tph1 GKO mice (top-right) was inadvertently replaced with the equivalent image of SCD-fed WT mice (top-left) during assembly of the figure. This error has now corrected in both the PDF and HTML versions of the Article.

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Time-Varying ℓ₀ Optimization for Spike Inference from Multi-Trial Calcium Recordings

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Data Science in Science 2024年第1期3卷

作者： Shen, Tong Du, Mingyu Johnston, Kevin Grieco, Steven F. Crary, Rachel Guzowski, John F. Lur, Gyorgy Xu, Xiangmin Ombao, Hernando Guindani, Michele Yu, Zhaoxia Department of Statistics University of California Irvine 92697 CA United States Center for Complex Biological Systems University of California Irvine 92697 CA United States Department of Mathematics and The NSF-Simons Center for Multiscale Cell Fate Research University of California Irvine 92697 CA United States Department of Anatomy and Neurobiology School of Medicine University of California Irvine 92697 CA United States Department of Neurobiology and Behavior University of California Irvine 92697 CA United States The Center for the Neurobiology of Learning and Memory University of California Irvine 92697 CA United States Department of Biomedical Engineering University of California Irvine 92697 CA United States Department of Computer Science University of California Irvine 92697 CA United States The Center for Neural Circuit Mapping University of California Irvine 92697 CA United States Statistics Program KAUST Thuwal Saudi Arabia Department of Biostatistics University of California Los Angeles CA United States

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying ℓ₀ penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate functions across trials due to learning. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： calcium imaging firing rate spike detection Time-varying penalty ℓ0 regularization

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Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: bioinformatics

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BMC Bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

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Heart on A Chip: Label-Free Isolation and Single Cell Biophysical Phenotyping Analysis of Primary Cardiomyocytes Using Inertial Microfluidics (Small 8/2021)

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Small 2021年第8期17卷

作者： Hossein Tavassoli Prunella Rorimpandey Young Chan Kang Michael Carnell Chris Brownlee John E Pimanda Peggy P.Y. Chan Vashe Chandrakanthan Department of Telecommunications Electrical Robotics and Biomedical Engineering Swinburne University of Technology Hawthorn Victoria 3122 Australia Department of Pathology School of Medical Sciences University of New South Wales Sydney NSW 2052 Australia Adult Cancer Program Lowy Cancer Research Centre University of New South Wales Sydney NSW 2052 Australia Biomedical Imaging Facility Mark Wainwright Analytical Centre University of New South Wales Sydney NSW 2052 Australia Flow Cytometry Facility Mark Wainwright Analytical Centre University of New South Wales Sydney NSW 2052 Australia Prince of Wales Clinical School University of New South Wales Sydney NSW 2052 Australia Department of Haematology Prince of Wales Hospital Sydney NSW 2052 Australia

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Optimization of hybrid PVA/hFDM scaffold preparation

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AIP Conference Proceedings 2021年第1期2344卷

作者： Rizqa Inayati Muhammad Suhaeri Nuzli Fahdia Melinda Remelia Radiana Dhewayani Antarianto 1Master Program in Biomedical Science Faculty of Medicine Universitas Indonesia Jl. Salemba Raya No. 6 Central Jakarta DKI Jakarta 10430 Indonesia 2Rumah Sakit Universitas Indonesia Kampus Universitas Indonesia Depok West Java 16424 Indonesia 3Stem cell and Tissue Engineering Research Cluster Indonesian Medical Education and Research Institute (IMERI) Universitas Indonesia Salemba Raya Jakarta West Java 10430 Indonesia 4Doctoral Program in Biomedical Science Faculty of Medicine Universitas Indonesia Jl. Salemba Raya No. 6 Central Jakarta DKI Jakarta 10430 Indonesia 5Department of Basic Biomedicine Faculty of Medicine Universitas Kristen Indonesia Jl Mayjen Sutoyo No 2 DKI Jakarta 13630 Indonesia 6Department of Histology Faculty of Medicine Universitas Indonesia Jl. Salemba Raya No. 6 Central Jakarta DKI Jakarta 10430 Indonesia

Osteogenic differentiation from Mesenchymal Stem Cell (MSC) to osteoblast has a clinical significance which is very important for treating bone injuries, in the form of femoral fractures with the most cases in Indonesia. Various studies have been conducted to find the best scaffold that can improve osteogenic differentiation, one of which is the development of a hybrid scaffold made from natural biomaterials in the form of the extracellular matrix, and from synthetic biomaterials. The discovery of the best scaffold is not only focused on the source of the scaffold but also requires optimization of the method in making the scaffold. Therefore, the aim of this study is to find out the optimum method for making hybrid scaffolds that support osteogenic differentiation from MSC. Materials and methods: human Fibroblast-derived Matrix (hFDM) as a hybrid scaffold material collected from decellularized fibroblasts cultures from post-cleft-surgery reconstruction palatal skin. Fibroblast cell cultures were divided into two groups of cultures, cultures without Platelet Rich Plasma (PRP), and cultures with the addition of PRP. For decellularization, we performed optimization at the preparation stage of the decellularization solution, and the time of culture for decellularization. In the preparation of the decellularization solution, we divided it into two groups, NH4OH as material from the decellularization solution was diluted with PBS before mixing with 0.25% Triton X-100, and NH4OH was diluted directly in 0.25% Triton X-100. In optimizing the culture time for decellularization, we divided it into three groups, decellularization on the day when cell growth reached 100% confluent, decellularization on the 3rd day after 100% confluent (H + 3) cells, and decellularization on the 4th day after 100% confluent (H + 4) cells. Next, the hFDM matrix is collected and added Polyvinyl Alcohol (PVA) solutions to form a hybrid PVA / hFDM scaffold in the form of a hydrogel. Observations on h

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Deletion of pancreas-specific miR-216a reduces beta-cell mass and inhibits pancreatic cancer progression in mice

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Cell Reports Medicine 2021年第11期2卷 100434-100434页

作者： Erener, Suheda Ellis, Cara E. Ramzy, Adam Glavas, Maria M. Pereira, Sandra Wang, Tom Pang, Janice Bruin, Jennifer E. Riedel, Michael J. Baker, Robert K. Webber, Travis D. Lesina, Marina Blüher, Matthias Algül, Hana Kopp, Janel L. Herzig, Stephan Kieffer, Timothy J. Department of Cellular & Physiological Sciences Life Sciences Institute University of British Columbia Vancouver BC Canada Institute for Diabetes and Cancer Helmholtz Center Munich Neuherberg Germany Joint Heidelberg-IDC Translational Diabetes Program Inner Medicine 1 Heidelberg University Hospital Heidelberg Germany Technical University Munich Neuherberg 85764 Germany Deutsches Zentrum für Diabetesforschung Neuherberg 85764 Germany Department of Biology and Institute of Biochemistry Carleton University Ottawa ON Canada Comprehensive Cancer Center Munich Technical University of Munich Munich Germany Helmholtz Institute for Metabolic Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig Leipzig Germany Medical Department III – Endocrinology Nephrology Rheumatology University of Leipzig Medical Center Leipzig Germany Department of Surgery University of British Columbia Vancouver BC Canada School of Biomedical Engineering University of British Columbia Vancouver BC Canada

miRNAs have crucial functions in many biological processes and are candidate biomarkers of disease. Here, we show that miR-216a is a conserved, pancreas-specific miRNA with important roles in pancreatic islet and acinar cells. Deletion of miR-216a in mice leads to a reduction in islet size, β-cell mass, and insulin levels. Single-cell RNA sequencing reveals a subpopulation of β-cells with upregulated acinar cell markers under a high-fat diet. miR-216a is induced by TGF-β signaling, and inhibition of miR-216a increases apoptosis and decreases cell proliferation in pancreatic cells. Deletion of miR-216a in the pancreatic cancer-prone mouse line Kras^G12D;Ptf1a^CreER reduces the propensity of pancreatic cancer precursor lesions. Notably, circulating miR-216a levels are elevated in both mice and humans with pancreatic cancer. Collectively, our study gives insights into how β-cell mass and acinar cell growth are modulated by a pancreas-specific miRNA and also suggests miR-216a as a potential biomarker for diagnosis of pancreatic diseases. © 2021 The Author(s)

关键词： biomarker circulating miRNA diabetes micro-RNA miR-216a pancreatic cancer PDAC β-cell mass

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Deep Nir-Ii Optical Imaging Combined with Minimally Invasive Interventional Photothermal Therapy for Orthotopic Bladder Cancer

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SSRN 2022年

作者： Zhao, Qingliang Hong, Fengqiu Geng, Xiaorui Min, Guangzong Sun, Xiang Zhang, Bei Yao, Youliang Li, Ronghui Yuan, Zhen Wang, Jing Zhao, Hengyu Guo, Peng Wen, Xiaofei Nie, Liming Liu, Gang Chen, Xiaoyuan State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics Center for Molecular Imaging and Translational Medicine Department of Laboratory Medicine School of Public Health Xiamen University Xiamen361102 China Cancer Center Faculty of Health Sciences Centre for Cognitive and Brain Sciences University of Macau Macau Special Administrative Region Taipa999078 China College of Materials Xiamen University 422 Siming Nan Road Xiamen361005 China Shenzhen Research Institute of Xiamen University Shenzhen518063 China Department of medical oncology Xiang’an Hospital of Xiamen University 2000 East Xiang’an Rd. Xiang’an Xiamen361102 China Department of Radiology Xiamen Cardiovascular Hospital of Xiamen University School of Medcine Xiamen University Xiamen361102 China Capital Institute of Pediatrics Beijing100020 China Department of Interventional Radiology The First Affiliated Hospital of Xiamen University Fujian Xiamen361000 China The 4th Hospital of Harbin Medical University Heilongjiang Harbin150001 China Medical Research Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Departments of Diagnostic Radiology Surgery Chemical and Biomolecular Engineering and Biomedical Engineering Yong Loo Lin School of Medicine Faculty of Engineering National University of Singapore Singapore119074 Singapore Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore117599 Singapore Nanomedicine Translational Research Program NUS Center for Nanomedicine Yong Loo Lin School of Medicine National University of Singapore Singapore117597 Singapore

Surgical resection is the major method for treating invasive or recurrent noninvasive bladder cancer. However, SR still causes additional risks of urinary tract infection, bleeding and some complications that may be life-threatening. Therefore, it is of urgent need for new diagnosis and therapy scheme for BC. Herein, we proposed a strategy that combines dual-modality 3D NIR-II PA and OCTA molecular imaging to guide visually interventional PTT for BC. Additionally, we developed an excellent NIR-II hyaluronic acid-IR-1048 with liposome-coated (HAPO-1048) photothermal agent for PTT of CD44 overexpressing orthotopic BC, which showed strong NIR-II optical absorption, preferable tumor targeting, excellent biocompatibility and high PTT efficacy. Combined with the NIR-II PA response of the HAPO-1048, we can achieve high sensitivity and speciﬁcity (about 2 mm diameter) dynamic monitoring and investigate the therapeutic efficacy of PTT at larger penetration depth (4.38 mm under the tissue) in orthotopic BC. Simultaneously, we can map the dynamic changes of 3D PA signals and HAPO-1048 NPs enrichment, which is closely related to the PTT efficacy. Significantly, the whole changes of complex microvascular morphometrics and angiogenesis of BC were well surveilled and assessed in vivo using OCTA. Lastly, minimally invasive interventional PTT strategy was implemented to achieve a controllable and precise therapy for BC based on laparoscopy and US guidance. In conclusion, we present a multimodal NIR-II optical imaging synergy laparoscopy interventional scheme, which should expand the current PTT technology, leading to a significant ongoing in treatment of primary tumors of urinary system. © 2022, The Authors. All rights reserved.

关键词： Infrared devices

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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Microplastics in Large Marine Animals Stranded in the Republic of Korea

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SSRN 2022年

作者： Park, Byeongyong Kim, Seung-Kyu Joo, Soobin Kim, Ji-Su Jo, Kyungsik Song, Nan-Seon Im, Jibin Lee, Hee-Jee Kim, Sang Wha Lee, Sung Bin Kim, Sunmin Lee, Youngran Kim, Byung-Yeob Kim, Tae Won Program in Biomedical Science and Engineering Inha University Incheon22212 Korea Republic of Department of Ocean Sciences Inha University Incheon22212 Korea Republic of Department of Marine Science College of Natural Sciences Incheon National University Academy-ro 119 Yeounsu-gu Incheon22012 Korea Republic of Korean Environmental Technology Consulting Hotline Korea Republic of Department of Microbiology and Immunology Institute of Endemic Disease College of Medicine Seoul National University Seoul Korea Republic of Laboratory of Aquatic Biomedicine College of Veterinary Medicine Research Institute for Veterinary Science Seoul National University Seoul Korea Republic of Department of Parasitology College of Medicine Chungbuk National University Korea Republic of Department of Companion Animals Osan University Korea Republic of Department of Marine Industry and Maritime Policy Jeju National University Jeju City63243 Korea Republic of Research Institute of Basic Sciences Incheon National University Academy-ro 119 Yeonsu-gu Incheon22012 Korea Republic of Yellow Sea Institute Incheon National University Academy-ro 119 Yeonsu-gu Incheon22012 Korea Republic of

Microplastics (MPs) are found in every ocean and are frequently ingested by marine animals. This study analyzed MPs in the stomach and intestinal contents of 12 large marine animals-consisting of 1 fin whale (Balaenoptera physalus), 7 finless porpoises (Neophocaena asiaeorientalis), 2 loggerhead turtles (Caretta caretta), 1 Indo-Pacific bottlenose dolphin (Tursiops aduncus), and 1 common dolphin (Delphinus delphis)— stranded off the Republic of Korea between 2019 and 2021. MPs that are fragment shaped, transparent-white colored, polypropylene type and small than 200 µm were predominant. The abundance of MPs found did not correlate with the biological information (maturity, body length) of the finless porpoises and there was no significant difference in the abundance of MPs between in the stomachs and intestines. These results indicate that the finless porpoises' ingested MPs were probably a result of their feeding activities within a marine environment contaminated by MPs. © 2022, The Authors. All rights reserved.

关键词： Microplastic

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