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Zhou T, Li JX, Zhang CY, Li YG, Peng J, Wei CL, Chen MH, Zhou HF. Risk factors for one-year mortality following discharge in patients with acute aortic dissection: development and validation of a predictive model in a cross-sectional study. BMC Cardiovasc Disord 2024; 24:129. [PMID: 38424525 PMCID: PMC10903037 DOI: 10.1186/s12872-024-03766-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/03/2024] [Indexed: 03/02/2024] Open
Abstract
PURPOSE This study was aimed to identify the risk factors that influence the mortality risk in patients with acute aortic dissection (AAD) within one year after discharge, and aimed to construct a predictive model for assessing mortality risk. METHODS The study involved 320 adult patients obtained from the Medical Information Mart for Intensive Care (MIMIC) database. Logistic regression analysis was conducted to identify potential risk factors associated with mortality in AAD patients within one year after discharge and to develop a predictive model. The performance of the predictive model was assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). To further validate the findings, patient data from the First Affiliated Hospital of Guangxi Medical University (157 patients) were analyzed. RESULTS Univariate and multivariate logistic regression analyses revealed that gender, length of hospital stay, highest blood urea nitrogen (BUN_max), use of adrenaline, and use of amiodarone were significant risk factors for mortality within one year after discharge (p < 0.05). The constructed model exhibited a consistency index (C-index) and an area under the ROC curve of 0.738. The calibration curve and DCA demonstrated that these indicators had a good degree of agreement and utility. The external validation results of the model also indicated good predictability (AUC = 0.700, p < 0.05). CONCLUSION The personalized scoring prediction model constructed by gender, length of hospital stays, BUN_max levels, as well as the use of adrenaline and amiodarone, can effectively identify AAD patients with high mortality risk within one year after discharge.
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Affiliation(s)
- Ting Zhou
- Cardiothoracic Surgery Intensive Care Unit, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
| | - Jing-Xiao Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
| | - Chao-Yong Zhang
- The First Affiliated Hospital of Guangxi Medical University Coronary Care Unit, Nanning, Guangxi, P.R. China
| | - Yu-Gui Li
- Department of Cardiac Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
| | - Jun Peng
- Department of Cardiac Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
| | - Chun-Lou Wei
- Cardiothoracic Surgery Intensive Care Unit, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
| | - Meng-Hua Chen
- The Second Affiliated Hospital of Guangxi Medical University Intensive Care Unit, Nanning, Guangxi, P.R. China.
| | - Hua-Fu Zhou
- Department of Thoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China.
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Chattopadhyay A, Lee CY, Lee YC, Liu CL, Chen HK, Li YH, Lai LC, Tsai MH, Ni YH, Chiu HM, Lu TP, Chuang EY. Twnbiome: a public database of the healthy Taiwanese gut microbiome. BMC Bioinformatics 2023; 24:474. [PMID: 38097965 PMCID: PMC10722848 DOI: 10.1186/s12859-023-05585-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
With new advances in next generation sequencing (NGS) technology at reduced costs, research on bacterial genomes in the environment has become affordable. Compared to traditional methods, NGS provides high-throughput sequencing reads and the ability to identify many species in the microbiome that were previously unknown. Numerous bioinformatics tools and algorithms have been developed to conduct such analyses. However, in order to obtain biologically meaningful results, the researcher must select the proper tools and combine them to construct an efficient pipeline. This complex procedure may include tens of tools, each of which require correct parameter settings. Furthermore, an NGS data analysis involves multiple series of command-line tools and requires extensive computational resources, which imposes a high barrier for biologists and clinicians to conduct NGS analysis and even interpret their own data. Therefore, we established a public gut microbiome database, which we call Twnbiome, created using healthy subjects from Taiwan, with the goal of enabling microbiota research for the Taiwanese population. Twnbiome provides users with a baseline gut microbiome panel from a healthy Taiwanese cohort, which can be utilized as a reference for conducting case-control studies for a variety of diseases. It is an interactive, informative, and user-friendly database. Twnbiome additionally offers an analysis pipeline, where users can upload their data and download analyzed results. Twnbiome offers an online database which non-bioinformatics users such as clinicians and doctors can not only utilize to access a control set of data, but also analyze raw data with a few easy clicks. All results are customizable with ready-made plots and easily downloadable tables. Database URL: http://twnbiome.cgm.ntu.edu.tw/ .
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Affiliation(s)
- Amrita Chattopadhyay
- Bioinformatics and Biostatistics Core, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
| | - Chien-Yueh Lee
- Department of Biomedical Engineering, China Medical University, Taichung, Taiwan
| | - Ya-Chin Lee
- Department of Public Health, Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan
| | - Chiang-Lin Liu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Hsin-Kuang Chen
- Center for Biotechnology, National Taiwan University, Taipei, Taiwan
| | - Yung-Hua Li
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Liang-Chuan Lai
- Bioinformatics and Biostatistics Core, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Physiology, National Taiwan University, Taipei, Taiwan
| | - Mong-Hsun Tsai
- Bioinformatics and Biostatistics Core, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
- Center for Biotechnology, National Taiwan University, Taipei, Taiwan
- Institute of Biotechnology, National Taiwan University, Taipei, Taiwan
| | - Yen-Hsuan Ni
- College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Han-Mo Chiu
- College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Tzu-Pin Lu
- Bioinformatics and Biostatistics Core, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan.
- Department of Public Health, Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan.
- Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan.
| | - Eric Y Chuang
- Bioinformatics and Biostatistics Core, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan.
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan.
- Division Research and Development Center for Medical Devices, National Taiwan University, Taipei, Taiwan.
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Yoo H, Moon J, Kim JH, Joo HJ. Design and technical validation to generate a synthetic 12-lead electrocardiogram dataset to promote artificial intelligence research. Health Inf Sci Syst 2023; 11:41. [PMID: 37662618 PMCID: PMC10468461 DOI: 10.1007/s13755-023-00241-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/12/2023] [Indexed: 09/05/2023] Open
Abstract
Purpose The purpose of this study is to construct a synthetic dataset of ECG signal that overcomes the sensitivity of personal information and the complexity of disclosure policies. Methods The public dataset was constructed by generating synthetic data based on the deep learning model using a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM), and the effectiveness of the dataset was verified by developing classification models for ECG diagnoses. Results The synthetic 12-lead ECG dataset generated consists of a total of 6000 ECGs, with normal and 5 abnormal groups. The synthetic ECG signal has a waveform pattern similar to the original ECG signal, the average RMSE between the two signals is 0.042 µV, and the average cosine similarity is 0.993. In addition, five classification models were developed to verify the effect of the synthetic dataset and showed performance similar to that of the model made with the actual dataset. In particular, even when the real dataset was applied as a test set to the classification model trained with the synthetic dataset, the classification performance of all models showed high accuracy (average accuracy 93.41%). Conclusion The synthetic 12-lead ECG dataset was confirmed to perform similarly to the real-world 12-lead ECG in the classification model. This implies that a synthetic dataset can perform similarly to a real dataset in clinical research using AI. The synthetic dataset generation process in this study provides a way to overcome the medical data disclosure challenges constrained by privacy rights, a way to encourage open data policies, and contribute significantly to promoting cardiovascular disease research.
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Affiliation(s)
- Hakje Yoo
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seongbuk-gu, Seoul, Republic of Korea
- Department of Bio-Mechatronic Engineering, Sungkyunkwan University College of Biotechnology and Bioengineering, Jangan-gu, Suwon, Gyeonggi Republic of Korea
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Gangnam-gu, Seoul, Republic of Korea
| | - Jose Moon
- Department of Medical Informatics, Korea University College of Medicine, Seongbuk-gu, Seoul, Republic of Korea
| | - Jong-Ho Kim
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seongbuk-gu, Seoul, Republic of Korea
- Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seongbuk-gu, Seoul, Republic of Korea
| | - Hyung Joon Joo
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seongbuk-gu, Seoul, Republic of Korea
- Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seongbuk-gu, Seoul, Republic of Korea
- Department of Medical Informatics, Korea University College of Medicine, Seongbuk-gu, Seoul, Republic of Korea
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Qiu BQ, Lin XH, Lai SQ, Lu F, Lin K, Long X, Zhu SQ, Zou HX, Xu JJ, Liu JC, Wu YB. ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation. Cancer Cell Int 2021; 21:665. [PMID: 34906142 PMCID: PMC8670189 DOI: 10.1186/s12935-021-02380-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 11/30/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Lung cancer is one of the most lethal malignant tumors that endangers human health. Lung adenocarcinoma (LUAD) has increased dramatically in recent decades, accounting for nearly 40% of all lung cancer cases. Increasing evidence points to the importance of the competitive endogenous RNA (ceRNA) intrinsic mechanism in various human cancers. However, behavioral characteristics of the ceRNA network in lung adenocarcinoma need further study. METHODS Groups based on SLC2A1 expression were used in this study to identify associated ceRNA networks and potential prognostic markers in lung adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to obtain the patients' lncRNA, miRNA, and mRNA expression profiles, as well as clinical data. Informatics techniques were used to investigate the effect of hub genes on prognosis. The Cox regression analyses were performed to evaluate the prognostic effect of hub genes. The methylation, GSEA, and immune infiltration analyses were utilized to explore the potential mechanisms of the hub gene. The CCK-8, transwell, and colony formation assays were performed to detect the proliferation and invasion of lung cancer cells. RESULTS We eventually identified the ITGB1-DT/ARNTL2 axis as an independent fact may promote lung adenocarcinoma progression. Furthermore, methylation analysis revealed that hypo-methylation may cause the dysregulated ITGB1-DT/ARNTL2 axis, and immune infiltration analysis revealed that the ITGB1-DT/ARNTL2 axis may affect the immune microenvironment and the progression of lung adenocarcinoma. The CCK-8, transwell, and colonu formation assays suggested that ITGB1-DT/ARNTL2 promotes the progression of lung adenocarcinoma. And hsa-miR-30b-3p reversed the ITGB1/ARNTL2-mediated oncogenic processes. CONCLUSION Our study identified the ITGB1-DT/ARNTL2 axis as a novel prognostic biomarker affects the prognosis of lung adenocarcinoma.
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Affiliation(s)
- Bai-Quan Qiu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xia-Hui Lin
- Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China
| | - Song-Qing Lai
- Institute of Cardiovascular Disease, Jiangxi Academy of Clinical Medical Sciences, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Feng Lu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Kun Lin
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xiang Long
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Shu-Qiang Zhu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Hua-Xi Zou
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Jian-Jun Xu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Ji-Chun Liu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
| | - Yong-Bing Wu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
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Prommarit K, Wonnapinij P. The complete mitochondrial genome of Dictyostelium intermedium. Mitochondrial DNA B Resour 2021; 6:3174-3176. [PMID: 34746396 PMCID: PMC8567905 DOI: 10.1080/23802359.2021.1989332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Dictyostelium intermedium is a member of dictyostelids, the unicellular eukaryotes with a unique life cycle, including a social cycle. Despite the high diversity of dictyostelids, only five species' complete mitochondrial genome sequences were reported. This study aimed to add the D. intermedium mitochondrial genome sequence to the list. The size of this genome is 58,627 bp, with 73.99% A/T, containing 62 genes located on one strand: 41 protein-coding genes, three ribosomal RNA genes, and 18 transfer RNA genes. The 41 protein-coding genes comprised 18 oxidative phosphorylation-related, 16 ribosomal, and seven hypothetical protein-coding genes. The cox1/2 and rnl gene contained introns, similar to other species of Dictyostelium. The phylogenetic tree built based on 34 protein sequences supported the monophyletic clade of Dictyostelium and the dictyostelids' ancestor's position between the two dictyostelids orders: Dictyosteliales and Acytosteliales.
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Affiliation(s)
- Kamonchat Prommarit
- Department of Genetics, Faculty of Science, Kasetsart University, Bangkok, Thailand
| | - Passorn Wonnapinij
- Department of Genetics, Faculty of Science, Kasetsart University, Bangkok, Thailand.,Centre for Advanced Studies in Tropical Natural Resources, Kasetsart University, Bangkok, Thailand.,Omics Center for Agriculture, Bioresources, Food and Health, Kasetsart University (OmiKU), Bangkok, Thailand
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6
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Yang C, Cronin MTD, Arvidson KB, Bienfait B, Enoch SJ, Heldreth B, Hobocienski B, Muldoon-Jacobs K, Lan Y, Madden JC, Magdziarz T, Marusczyk J, Mostrag A, Nelms M, Neagu D, Przybylak K, Rathman JF, Park J, Richarz AN, Richard AM, Ribeiro JV, Sacher O, Schwab C, Vitcheva V, Volarath P, Worth AP. COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals. Comput Toxicol 2021; 19:100175. [PMID: 34405124 PMCID: PMC8351204 DOI: 10.1016/j.comtox.2021.100175] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/19/2021] [Accepted: 05/27/2021] [Indexed: 11/19/2022]
Abstract
The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.
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Key Words
- AOP, Adverse Outcome Pathway
- Analogue selection
- CERES, Chemical Evaluation and Risk Estimation System
- CFSAN, Center for Food Safety and Applied Nutrition
- CMS-ID, COSMOS Identification Number
- COSMOS DB, COSMOS Database
- COSMOS MINIS, Minimum Inclusion Criteria of Studies in COSMOS DB
- COSMOS NG, COSMOS Next Generation
- CRADA, Cooperative Research and Development Agreement
- CosIng, Cosmetic Ingredient Database
- DART, Developmental & Reproductive Toxicity
- DB, Database
- DST, Dempster Shafer Theory
- Database
- ECHA, European Chemicals Agency
- EFSA, European Food Safety Authority
- Guided workflow
- HESS, Hazard Evaluation Support System
- HNEL, Highest No Effect Level
- HTS, High throughput screening
- ILSI, International Life Sciences Institute
- IUCLID, International Uniform Chemical Information Database
- Knowledge hub
- LEL, Lowest Effect Level
- LOAEL, Lowest Observed Adverse Effect Level
- LogP, Logarithm of the octanol:water partition coefficient
- NAM, New Approach Methodology
- NGRA, Next Generation Risk-Assessment
- NITE, National Institute of Technology and Evaluation (Japan)
- NOAEL, No Observed Adverse Effect Level
- NTP, National Toxicology Program
- OECD, Organisation for Economic Co-operation and Development
- OpenFoodTox, EFSA’s OpenFoodTox database
- PAFA, Priority-based Assessment of Food Additive database
- PK/TK, Pharmacokinetics/Toxicokinetics
- Public database
- QA, Quality Assurance
- QC, Quality Control
- REACH, Registration, Evaluation, Authorisation and Restriction of Chemicals
- SCC, Science Committee on Cosmetics (EU)
- SCCNFP, Scientific Committee of Cosmetic Products and Non-food Products intended for Consumers (EU)
- SCCP, Scientific Committee on Consumer Products (EU)
- SCCS, Scientific Committee on Consumer Safety (EU)
- Study reliability
- TTC, Threshold of Toxicological Concern
- ToxRefDB, Toxicity Reference Database
- Toxicity
- US EPA, United States Environmental Protection Agency
- US FDA, United States Food and Drug Administration
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Affiliation(s)
- C Yang
- MN-AM, Columbus, OH, USA
- MN-AM Nürnberg, Germany
| | - M T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, UK
| | | | | | - S J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, UK
| | - B Heldreth
- Cosmetic Ingredient Review, Washington, DC, USA
| | | | | | - Y Lan
- University of Bradford, UK
| | - J C Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, UK
| | | | | | | | - M Nelms
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, UK
| | | | - K Przybylak
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, UK
| | - J F Rathman
- MN-AM, Columbus, OH, USA
- The Ohio State University, Columbus OH, USA
| | | | - A-N Richarz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, UK
| | | | | | | | | | - V Vitcheva
- MN-AM, Columbus, OH, USA
- MN-AM Nürnberg, Germany
| | | | - A P Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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Sánchez-Reyes LL, Kandziora M, McTavish EJ. Physcraper: a Python package for continually updated phylogenetic trees using the Open Tree of Life. BMC Bioinformatics 2021; 22:355. [PMID: 34187366 PMCID: PMC8244228 DOI: 10.1186/s12859-021-04274-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 06/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Phylogenies are a key part of research in many areas of biology. Tools that automate some parts of the process of phylogenetic reconstruction, mainly molecular character matrix assembly, have been developed for the advantage of both specialists in the field of phylogenetics and non-specialists. However, interpretation of results, comparison with previously available phylogenetic hypotheses, and selection of one phylogeny for downstream analyses and discussion still impose difficulties to one that is not a specialist either on phylogenetic methods or on a particular group of study. RESULTS Physcraper is a command-line Python program that automates the update of published phylogenies by adding public DNA sequences to underlying alignments of previously published phylogenies. It also provides a framework for straightforward comparison of published phylogenies with their updated versions, by leveraging upon tools from the Open Tree of Life project to link taxonomic information across databases. The program can be used by the nonspecialist, as a tool to generate phylogenetic hypotheses based on publicly available expert phylogenetic knowledge. Phylogeneticists and taxonomic group specialists will find it useful as a tool to facilitate molecular dataset gathering and comparison of alternative phylogenetic hypotheses (topologies). CONCLUSION The Physcraper workflow showcases the benefits of doing open science for phylogenetics, encouraging researchers to strive for better scientific sharing practices. Physcraper can be used with any OS and is released under an open-source license. Detailed instructions for installation and usage are available at https://physcraper.readthedocs.io.
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Affiliation(s)
| | - Martha Kandziora
- School of Natural Sciences, University of California, Merced, USA.,Department of Botany, Faculty of Science, Charles University, Prague, Czech Republic
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Potočnik B, Munda J, Reljič M, Rakić K, Knez J, Vlaisavljević V, Sedej G, Cigale B, Holobar A, Zazula D. Public database for validation of follicle detection algorithms on 3D ultrasound images of ovaries. Comput Methods Programs Biomed 2020; 196:105621. [PMID: 32615494 DOI: 10.1016/j.cmpb.2020.105621] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 06/15/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Automated follicle detection in ovarian ultrasound volumes remains a challenging task. An objective comparison of different follicle-detection approaches is only possible when all are tested on the same data. This paper describes the development and structure of the first publicly accessible USOVA3D database of annotated ultrasound volumes with ovarian follicles. METHODS The ovary and all follicles were annotated in each volume by two medical experts. The USOVA3D database is supplemented by a general verification protocol for unbiased assessment of detection algorithms that can be compared and ranked by scoring according to this protocol. This paper also introduces two baseline automated follicle-detection algorithms, the first based on Directional 3D Wavelet Transform (3D DWT) and the second based on Convolutional Neural Networks (CNN). RESULTS The USOVA3D testing data set was used to verify the variability and reliability of follicle annotations. The intra-rater overall score yielded around 83 (out of a maximum of 100), while both baseline algorithms pointed out just a slightly lower performance, with the 3D DWT-based algorithm being better, with an overall score around 78. CONCLUSIONS On the other hand, the development of the CNN-based algorithm demonstrated that the USOVA3D database contains sufficient data for successful training without overfitting. The inter-rater reliability analysis and the obtained statistical metrics of effectiveness for both baseline algorithms confirmed that the USOVA3D database is a reliable source for developing new automated detection methods.
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Affiliation(s)
- Božidar Potočnik
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia.
| | - Jurij Munda
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia
| | | | | | - Jure Knez
- University Medical Centre, Maribor, Slovenia
| | | | - Gašper Sedej
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia
| | | | - Aleš Holobar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia
| | - Damjan Zazula
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia
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Kaushik AC, Mehmood A, Upadhyay AK, Paul S, Srivastava S, Mali P, Xiong Y, Dai X, Wei DQ, Sahi S. CytoMegaloVirus Infection Database: A Public Omics Database for Systematic and Comparable Information of CMV. Interdiscip Sci 2020; 12:169-77. [PMID: 31813095 DOI: 10.1007/s12539-019-00350-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 10/24/2019] [Accepted: 11/13/2019] [Indexed: 12/11/2022]
Abstract
CytoMegaloVirus (CMV) is known to cause infection in humans and may remain dormant throughout the life span of an individual. CMV infection has been reported to be fatal in patients with weak immunity. It is transmitted through blood, saliva, urine, semen and breast milk. Although medications are available to treat the infected patients, there is no cure for CMV. This concern prompted us to construct a comprehensive database having exhaustive information regarding CMV, its infections and therapies to be available on a single platform. Thus, we propose a newly designed database that includes all the information from various public resources such as biological databases, virus taxonomy databanks, viral databases, and drug bank, integrated into this database, named as cytomegalovirus database (CMVdb). It features all the relevant data regarding the strains of CMV, genes, expressed proteins, the genomic sequence of CMV and drugs used in the treatment of cytomegalovirus infection. CMVdb has a unique feature of in-house data analysis, so all the data obtained from various resources are processed within the system. The user interface is more responsive because of the integrated platform that will highly facilitate the researchers. Based on CMVdb functionality and quality of the data, it will accelerate the research and development in the field of infectious diseases and immunology with a special focus on CMV. The obtained data would be useful in designing better therapeutic strategies and agents for the treatment of CMV infections. The proposed database (CMVdb) is freely accessible at http://shaktisahislab.com/include/CMV/ or http://weislab.com/WeiDOCK/include/content/CMV/.
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Abstract
Public databases provide a wealth of freely available information about chemicals, genes, proteins, biological networks, phenotypes, diseases, and exposure science that can be integrated to construct pathways for systems toxicology applications. Relating this disparate information from public repositories, however, can be challenging since databases use a variety of ways to represent, describe, and make available their content. The use of standard vocabularies to annotate key data concepts, however, allows the information to be more easily exchanged and combined for discovery of new findings. We explore some of the many public data sources currently available to support systems toxicology, and demonstrate the value of standardizing data to help construct chemical-induced outcome pathways.
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Affiliation(s)
- Allan Peter Davis
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Jolene Wiegers
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Thomas C Wiegers
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Carolyn J Mattingly
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, United States
- Center for Human Health and the Environment, North Carolina State University, Raleigh, North Carolina 27695, United States
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Mo CH, Gao L, Zhu XF, Wei KL, Zeng JJ, Chen G, Feng ZB. The clinicopathological significance of UBE2C in breast cancer: a study based on immunohistochemistry, microarray and RNA-sequencing data. Cancer Cell Int 2017; 17:83. [PMID: 29021715 PMCID: PMC5613379 DOI: 10.1186/s12935-017-0455-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 09/18/2017] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Ubiquitin-conjugating enzyme E2C (UBE2C) has been previously reported to correlate with the malignant progression of various human cancers, however, the exact molecular function of UBE2C in breast carcinoma (BRCA) remained elusive. We aimed to investigate UBE2C expression in BRCA and its clinical significance. METHODS The expression of UBE2C in 209 BRCA tissue samples and 53 adjacent normal tissue samples was detected using immunohistochemistry. The clinical role of UBE2C was analyzed. Public databases including the human protein atlas and Oncomine were used to assess UBE2C expression in BRCA. Moreover, the cancer genome atlas (TCGA) database was employed to investigate the prognostic value of UBE2C in BRCA. RESULTS The positive expression rate of UBE2C in BRCA was 70.8% (148/209), and UBE2C expression in the adjacent breast tissue was negative. The expression of UBE2C was positively correlated with tumor size (r = 0.32, P < 0.001), histological grade (r = 0.237, P = 0.001), clinical stage (r = 0.198, P = 0.004), lymph node metastasis (r = 0.155, P = 0.026), HER2 expression level (r = 0.356, P < 0.001), Ki-67 expression level (r = 0.504, P < 0.001), and P53 expression level (r = 0.32, P = 0.001). Negative correlations were found between UBE2C expression and the ER (r = - 0.403, P < 0.001) and PR (r = - 0.468, P < 0.001) status. UBE2C gene expression data from the public databases all proved that UBE2C was overexpressed in BRCA. According to the TCGA data analysis, a higher positive expression of UBE2C was associated with worse survival of BRCA patients (P = 0.0428), and data from cBioPortal indicated that 11% of all sequenced BRCA patients possessed a gene alteration of UBE2C, predominately gene amplification and mRNA regulation. CONCLUSION Ubiquitin-conjugating enzyme E2C might pose an oncogenic effect on the progression of BRCA.
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Affiliation(s)
- Chao-Hua Mo
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021 Guangxi Zhuang Autonomous Region China
| | - Li Gao
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021 Guangxi Zhuang Autonomous Region China
| | - Xiao-Fei Zhu
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021 Guangxi Zhuang Autonomous Region China.,Department of Pathology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou Worker's Hospital, 1 Liushi Road, Liuzhou, 545005 Guangxi Zhuang Autonomous Region China
| | - Kang-Lai Wei
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021 Guangxi Zhuang Autonomous Region China
| | - Jing-Jing Zeng
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021 Guangxi Zhuang Autonomous Region China
| | - Gang Chen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021 Guangxi Zhuang Autonomous Region China
| | - Zhen-Bo Feng
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021 Guangxi Zhuang Autonomous Region China
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