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Next-generation sequencing yields the complete mitochondrial genome of mud spiny lobster, Panulirus polyphagus (Crustacea: Decapoda) from Madura water

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IOP Conference Series: Earth and Environmental Science 2019年第1期348卷

作者： S Andriyono M J Alam H Pramono A A Abdillah H W Kim Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan 48513 Republic of Korea Department of Marine Fisheries and Marine Faculty C Campus Jl. Mulyorejo Surabaya 60115. Universitas Airlangga Surabaya East Java Indonesia Department of Marine Biology Pukyong National University Busan 48513 Republic of Korea

The circular mitochondrial genome of the mud spiny lobster, Panulirus polyphagus was determined by next-generation sequencing (NGS) platform. The mitochondrial genome of P. polyphagus was 15,707 bp in length, which comprised 13 protein-coding genes, 22 tRNAs, two ribosomal RNAs (12S and 16S). A non-coding putative control region (739 bp) was located between 12S ribosomal RNA and tRNA-Ile. Except for COX1, 13 protein-coding genes initiated with the conventional start codon (ATG). The phylogenetic analysis with the mitogenomes in family Palinuridae showed P. polyphagus was clustered together with four congener species forming a clade, and Panulirus versicolor is closest to P. polyphagus.

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Corrigendum to "Intratumoral injection of thermogelling and sustained-release carboplatin-loaded hydrogel simplifies the administration and remains the synergistic effect with radiotherapy for mice gliomas" [Biomaterials, 151(2018):38-52]

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Biomaterials 2019年 212卷 126-127页

作者： Hsiang-Kuang Tony Liang Xue-Shi Lai Ming-Feng Wei Szu-Huai Lu Wen-Fen Wen Sung-Hsin Kuo Chung-Ming Chen Wen-Yih Isaac Tseng Feng-Huei Lin Institute of Biomedical Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei 10617 Taiwan Division of Radiation Oncology Department of Oncology National Taiwan University Hospital National Taiwan University College of Medicine No. 7 Chung Shan South Rd. Zhongzheng Dist. Taipei 10002 Taiwan Radiation Science and Proton Therapy Center National Taiwan University College of Medicine No. 7 Chung Shan South Rd. Zhongzheng Dist. Taipei 10002 Taiwan Department of Neurology National Taiwan University Hospital National Taiwan University College of Medicine No. 7 Chung Shan South Rd. Zhongzheng Dist. Taipei 10002 Taiwan. Institute of Biomedical Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei 10617 Taiwan. Division of Radiation Oncology Department of Oncology National Taiwan University Hospital National Taiwan University College of Medicine No. 7 Chung Shan South Rd. Zhongzheng Dist. Taipei 10002 Taiwan. Department of Pathology National Taiwan University Hospital National Taiwan University College of Medicine No. 7 Chung Shan South Rd. Zhongzheng Dist. Taipei 10002 Taiwan. Division of Radiation Oncology Department of Oncology National Taiwan University Hospital National Taiwan University College of Medicine No. 7 Chung Shan South Rd. Zhongzheng Dist. Taipei 10002 Taiwan Graduate Institute of Oncology College of Medicine National Taiwan University No. 1 Jen Ai Rd. Sec. 1 Zhongzheng Dist. Taipei 10051 Taiwan. Institute of Medical Device and Imaging National Taiwan University College of Medicine No. 1 Jen Ai Rd. Sec. 1 Zhongzheng Dist. Taipei 10051 Taiwan. Institute of Biomedical Engineering and Nano-medicine National Health Research Institutes No. 35 Keyan Road Zhunan Town Miaoli County 35053 Taiwan. Electronic address: double@ntu.edu.tw.

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Radiosensitization effects and ROS generation by high Z metallic nanoparticles on human colon carcinoma cell (HCT116) irradiated under 150 MeV proton beam

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OpenNano 2019年 4卷

作者： Abdul Rashid, Raizulnasuha Zainal Abidin, Safri Khairil Anuar, Muhammad Afiq Tominaga, Takahiro Akasaka, Hiroaki Sasaki, Ryohei Kie, Katahira Abdul Razak, Khairunisak Pham, Binh. T.T. Hawkett, Brian S. Carmichael, Mary-Ann Geso, Moshi Rahman, Wan Nordiana Medical Radiation Programme School of Health Sciences Universiti Sains Malaysia Kubang Kerian 16150 Kelantan Malaysia Department of Clinical Radiology Hiroshima International University Hiroshima 739-452 Japan Division of Radiation Oncology Kobe University Graduate School of Medicine Kobe Japan Hyogo Ion Beam Medical Centre Hyogo 679-5165 Japan School of Materials and Mineral Resources Engineering Universiti Sains Malaysia Nibong Tebal 14300 Penang Malaysia Key Centre for Polymers and Colloids School of Chemistry The University of Sydney 2006 NSW Australia Discipline of Medical Radiation School of Health & Biomedical Sciences RMIT University Bundoora 3083 Victoria Australia

Radiosensitization effects caused by high atomic number (Z) metallic nanoparticles have been extensively explored with a wide range of X-ray based radiotherapy beams that seem to provide intriguing results for potential clinical applications. In this study, the radiobiological consequences of gold nanoparticles (AuNPs), superparamagnetic iron oxide nanoparticles (SPIONs), Platinum Nanodendrites (PtNDs) and Bismuth Oxide Nanorods (BiNRs) on Human Colon Carcinoma cells (HCT 116) irradiated with 150 MeV proton beams were investigated. Cell survival and reactive oxygen species (ROS) generation were measured. BiNRs demonstrates the highest sensitization enhancement ratio (SER) of 4.93 followed by PtNDs with SER value of 3.08. Meanwhile, AuNPs and SPIONs depict SER values around 2.64 and 1.95 respectively. The ROS generation corresponded to the level of radiosensitization with the highest ROS obtained for BiNRs and followed by PtNDs, AuNPs and SPIONs. In conclusion, high Z nanoparticles possess the potential to be clinically applied in proton beam therapy. © 2018

关键词： Bismuth nanoparticles Gold nanoparticles Platinum nanoparticles Proton beam Radiosensitization Superparamagnetic iron oxide nanoparticles

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Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks

arXiv

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arXiv 2017年

作者： Vigneault, Davis M. Xie, Weidi Ho, Carolyn Y. Bluemke, David A. Alison Noble, J. Institute of Biomedical Engineering Department of Engineering University of Oxford Department of Radiology and Imaging Sciences Clinical Center National Institutes of Health Tufts University School of Medicine Sackler School of Graduate Biomedical Sciences Cardiovascular Division Brigham and Women's Hospital University of Wisconsin-Madison School of Medicine and Public Health

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): a novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image;second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation;and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA;four-chamber, 4C;two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained on a cohort of patients with hypertrophic cardiomyopathy (HCM, N = 42) and healthy control subjects (N = 21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancemen

关键词： Semantic Segmentation

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Organic Metal Halide Hybrids: Bulk Assembly of Corrugated 1D Metal Halides with Broadband Yellow Emission (Advanced Optical Materials 6/2019)

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Advanced Optical Materials 2019年第6期7卷

作者： Haoran Lin Chenkun Zhou Jennifer Neu Yan Zhou Dan Han Shiyou Chen Michael Worku Maya Chaaban Sujin Lee Ella Berkwits Theo Siegrist Mao-Hua Du Biwu Ma Department of Chemistry and Biochemistry Florida State University Tallahassee FL 32306 USA Department of Chemical and Biomedical Engineering FAMU-FSU College of Engineering Tallahassee FL 32310 USA National High Magnetic Field Laboratory Florida State University Tallahassee FL 32310 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge TN 37831 USA Department of Physics East China Normal University Shanghai 200241 China Materials Science and Engineering Program Florida State University Tallahassee FL 32306 USA

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Translation and cultural adaptation of tools to assess diverse Asian American and Asian Canadian subgroups: The Asian Cohort for Alzheimer's Disease (ACAD) Study

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第6期21卷 e70311页

作者： Haeok Lee Marian Tzuang Tiffany W Chow Younhee Kang Boon Lead Tee Clara Li Eleanor Lam Yian Gu SangA Lee Pei-Chuan Ho Guerry Peavy Eun Hyun Seo Kyungmin Kim Binh Tran Wonjeong Chae Dat Nguyen Namkhue Vo Deanna Dang Jessica Spat-Lemus Yun-Beom Choi Howard Feldman Gyungah R Jun Li-San Wang Wai Haung Yu Van Ta M Park Meyers College of Nursing New York University New York New York USA. Department of Community Health Systems University of California San Francisco School of Nursing San Francisco California USA. Penn Neurodegeneration Genomics Center Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. College of Nursing Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Republic of Korea. Memory and Aging Center Department of Neurology Weill Institute for Neurosciences University of California San Francisco California USA. Global Brain Health Institute University of California San Francisco California USA. Department of Psychiatry Icahn School of Medicine at Mount Sinai New York New York USA. Brain Health and Imaging Center and Geriatric Mental Health Services Centre for Addiction and Mental Health Toronto Ontario Canada. Department of Neurology Columbia University Medical Center New York New York USA. Mo-Im Kim Nursing Research Institute College of Nursing Yonsei University Seoul Republic of Korea. The Leonard and Davis Institute of Health Economics University of Pennsylvania Philadelphia Pennsylvania USA. Department of Neurosciences University of California San Diego La Jolla California USA. Premedical Science College of Medicine Chosun University Gwangju Republic of Korea. Department of Child Development and Family Studies College of Human Ecology Seoul National University Seoul Republic of Korea. American Pacific Health Foundation La Jolla California USA. Graduate School of Public Health Yonsei University Seoul Republic of Korea. Retired Fountain Valley California USA. Alzheimer's Disease Cooperative Study University of California San Diego La Jolla California USA. Institute for Memory Impairments and Neurological Disorders University of California Irvine Irvine California USA. Department of Medicine/Neurology Englewood Health Engle

INTRODUCTION:The availability of sociocultural and language-appropriate study materials and instruments is critical for the assessment of cognitive function in people from diverse backgrounds. This report describes the translations and cultural adaptations of study materials for the Asian Cohort for Alzheimer's Disease (ACAD) study. METHODS:We performed translations and cultural adaptations in accordance with the World Health Organization (WHO) translation guidelines to ensure reliable, complete, and culturally appropriate translations from English to the specified Asian languages. RESULTS:We developed Asian language versions of the ACAD documents (consent, data collection packet, and community and social media outreach materials) reflecting the sociocultural backgrounds of the ACAD target population (i.e., older Asian adults) DISCUSSION: The multistep translation process accounting for distinctive Asian sociocultural and language backgrounds provides an important guideline for Alzheimer's disease and related dementias (ADRD) researchers to promote health literacy and research with underrepresented Asian American and Canadian adults. HIGHLIGHTS:Asian American and Asian Canadian older adults are the fastest-growing populations. A lack of linguistically and culturally appropriate cognitive assessment tools creates barriers for quality healthcare and clinical research. We report the translations and cultural adaptations of the Asian Cohort for Alzheimer's Disease (ACAD) study materials into Chinese, Korean, and Vietnamese. This translation methodology should be extended to Asian Indians, Filipinos, and other Asian American or Asian Canadian populations.

关键词： Alzheimer's disease Asian cognitive assessment community‐based participatory research cultural adaptation dementia translation

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Development of biosignal recording board system with agile control of circuit characteristics for various biosignals

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2017年第2期137卷 348-353页

作者： Ito, Keita Tani, Takaharu Iwagami, Takuma Nishino, Satoru Kiyoyama, Koji Tanaka, Tetsu Graduate School of Engineering Tohoku University 6-6-12 Aza-aoba Aramaki Aoba-ku Sendai980-8579 Japan Graduate School of Engineering Nagasaki Institute of Applied Science 536 Aba-machi Nagasaki851-0193 Japan Graduate School of Biomedical Engineering Tohoku University 6-6-12 Aza-aoba Aramaki Aoba-ku Sendai980-8579 Japan

Recently, a novel recording system is strongly required for agile and precise recording of biosignals in various fields, such as medicine, neuroscience, BMI (Brain Machine Interface), and so on. To enable such various biosignal recording, we have proposed an agile biosignal recording system with a newly designed analog front-end LSI chip having amplification, filtering, and A/D conversion functions. In this paper, we designed and evaluated a neural signal recording circuit and a portable front-end motherboard. The neural signal recording circuit chip and discrete ICs were mounted on the motherboard. We confirmed that our functional LNA (Low Noise Amplifier) had variable gains and cutoff frequencies appropriate for various biosignals, and also confirmed that biosignal recording board system successfully measured a human electrocardiogram. © 2017 The Institute of Electrical Engineers of Japan.

关键词： Brain computer interface

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Application of artificial neural network for type 2 diabetes mellitus detection using buccal cell images

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Journal of International Dental and Medical Research 2017年第2期10卷 253-259页

作者： Wardhani, Priyanka Kusuma Widiyanti, Prihartini Arisgraha, Franky Chandra Satria Biomedical Engineering Study Program Faculty of Science and Technology Universitas Airlangga Indonesia Institute of Tropical Disease Universitas Airlangga Indonesia

Diabetes mellitus (DM) is metabolic disease causing hyperglicemia due to insulin action anomali. DM can cause cellular changes, including buccal cell. Blood tests are used to diagnosis diabetes, so non-invasive test is required for diagnosis of diabetes. Accordingly, this research aims to design non-invasive system based on artificial neural network for type 2 DM detection using buccal cell images. Buccal cells smears were obtained from 30 subjects suffering from type 2 DM and 30 normal subjects. The smears were stained by using Papanicolaou method. Each slide were observed under digital microscope and were evaluated. The system was designed by using MATLAB with image processing and Probabilistic Neural Network (PNN) algorithm to classify features. Buccal cell images were segmented to get features. The features used in this study were nucleus area, nucleus perimeter and nucleus circularity. Nucleus areas and perimeters in type 2 DM group were higher than those in control group with similar nucleus roundness in both groups. Forty nucleus feature datasets were used for training process, while 20 nucleus feature datasets were used for testing process. The optimal PNN value was 0.4 g constant. The optimal accuracy of training was 92.5%, while the optimal accuracy of testing was 90%.

关键词： Artificial neural network Buccal cell images Type 2 diabetes mellitus

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GM-CSF disruption in cart cells ameliorates cart cell activation and reduces activation-induced cell death

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Cytotherapy 2021年第5期23卷 S29-S30页

作者： M.J. Cox C. Manriquez Roman R. Sakemura E. Tapper E. Siegler S. Sinha D. Chappell O. Ahmed C. Durrant M. Hefazi K. Schick P. Horvei M. Ruff I. Can M. Adada E. Bezerra L. Kankeu Fonkoua S. Parikh S. Kenderian T Cell Engineering Mayo Foundation for Medical Education and Research Rochester MN United States Division of Hematology Mayo Foundation for Medical Education and Research Rochester MN United States Bioinformatics and Computational Biology University of Minnesota Graduate School Minneapolis MN United States Department of Molecular Medicine Mayo Foundation for Medical Education and Research Rochester MN United States Mayo Clinic Graduate School of Biomedical Sciences Rochester MN United States Humanigen Inc. Burlingame CA United States Department of Molecular Pharmacology & Experimental Therapeutics Mayo Foundation for Medical Education and Research Rochester MN United States Department of Pediatric Hematology/Oncology Mayo Foundation for Medical Education and Research Rochester MN United States Department of Neurology Mayo Foundation for Medical Education and Research Rochester MN United States Department of Biochemistry and Molecular Biology Mayo Foundation for Medical Education and Research Rochester MN United States Department of Oncology Mayo Foundation for Medical Education and Research Rochester MN United States Department of Immunology Mayo Foundation for Medical Education and Research Rochester MN United States

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Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

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JMIR medical informatics 2021年第8期9卷 e23230页

作者： Pei-Fu Chen Ssu-Ming Wang Wei-Chih Liao Lu-Cheng Kuo Kuan-Chih Chen Yu-Cheng Lin Chi-Yu Yang Chi-Hao Chiu Shu-Chih Chang Feipei Lai Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Anesthesiology Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Internal Medicine National Taiwan University Hospital National Taiwan University College of Medicine Taipei Taiwan. Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Medical Affairs Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Healthcare Administration Oriental Institute of Technology New Taipei City Taiwan. Department of Information Technology Far Eastern Memorial Hospital New Taipei City Taiwan. Section of Cardiovascular Medicine Cardiovascular Center Far Eastern Memorial Hospital New Taipei City Taiwan. Section of Health Insurance Department of Medical Affairs Far Eastern Memorial Hospital New Taipei City Taiwan. Medical Records Department Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan. Department of Electrical Engineering National Taiwan University Taipei Taiwan.

BACKGROUND:The International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning- and natural language processing-related approaches have been studied to assist disease coders. OBJECTIVE:This paper aims at constructing a deep learning model for ICD-10 coding, where the model is meant to automatically determine the corresponding diagnosis and procedure codes based solely on free-text medical notes to improve accuracy and reduce human effort. METHODS:We used diagnosis records of the National Taiwan University Hospital as resources and apply natural language processing techniques, including global vectors, word to vectors, embeddings from language models, bidirectional encoder representations from transformers, and single head attention recurrent neural network, on the deep neural network architecture to implement ICD-10 auto-coding. Besides, we introduced the attention mechanism into the classification model to extract the keywords from diagnoses and visualize the coding reference for training freshmen in ICD-10. Sixty discharge notes were randomly selected to examine the change in the F-score and the coding time by coders before and after using our model. RESULTS:In experiments on the medical data set of National Taiwan University Hospital, our prediction results revealed F-scores of 0.715 and 0.618 for the ICD-10 Clinical Modification code and Procedure Coding System code, respectively, with a bidirectional encoder representations from transformers embedding approach in the Gated Recurrent Unit classification model. The well-trained models were applied on the ICD-10 web service for coding and training to ICD-10 users. Wit

关键词： International Classification of Diseases Recurrent Neural Network deep learning natural language processing text classification

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