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Xia M, Jin C, Zheng Y, Wang J, Zhao M, Cao S, Xu T, Pei B, Irwin MG, Lin Z, Jiang H. Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study. Anaesthesia 2024; 79:399-409. [PMID: 38093485 DOI: 10.1111/anae.16194] [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] [Accepted: 11/03/2023] [Indexed: 03/07/2024]
Abstract
While videolaryngoscopy has resulted in better overall success rates of tracheal intubation, airway assessment is still an important prerequisite for safe airway management. This study aimed to create an artificial intelligence model to identify difficult videolaryngoscopy using a neural network. Baseline characteristics, medical history, bedside examination and seven facial images were included as predictor variables. ResNet-18 was introduced to recognise images and extract features. Different machine learning algorithms were utilised to develop predictive models. A videolaryngoscopy view of Cormack-Lehane grade of 1 or 2 was classified as 'non-difficult', while grade 3 or 4 was classified as 'difficult'. A total of 5849 patients were included, of whom 5335 had non-difficult and 514 had difficult videolaryngoscopy. The facial model (only including facial images) using the Light Gradient Boosting Machine algorithm showed the highest area under the curve (95%CI) of 0.779 (0.733-0.825) with a sensitivity (95%CI) of 0.757 (0.650-0.845) and specificity (95%CI) of 0.721 (0.626-0.794) in the test set. Compared with bedside examination and multivariate scores (El-Ganzouri and Wilson), the facial model had significantly higher predictive performance (p < 0.001). Artificial intelligence-based facial analysis is a feasible technique for predicting difficulty during videolaryngoscopy, and the model developed using neural networks has higher predictive performance than traditional methods.
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Affiliation(s)
- M Xia
- Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - C Jin
- Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Y Zheng
- State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - J Wang
- Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - M Zhao
- State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - S Cao
- Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - T Xu
- Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - B Pei
- Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - M G Irwin
- Department of Anaesthesiology, University of Hong Kong, Hong Kong
| | - Z Lin
- State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - H Jiang
- Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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2
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Abstract
Targeted protein degradation (TPD) strategies have revolutionized how scientists tackle challenging protein targets deemed undruggable with traditional small molecule inhibitors. Many promising campaigns to inhibit proteins have failed due to factors surrounding inhibition selectivity and targeting of compounds to specific tissues and cell types. One of the major improvements that PROTAC (proteolysis targeting chimera) and molecular glue technology can exert is highly selective control of target inhibition. Multiple studies have shown that PROTACs can gain selectivity for their protein targets beyond that of their parent ligands via optimization of linker length and stabilization of ternary complexes. Due to the bifunctional nature of PROTACs, the tissue selective nature of E3 ligases can be exploited to uncover novel targeting mechanisms. In this review, we provide critical analysis of the recent progress towards making selective PROTAC molecules and new PROTAC technologies that will continue to push the boundaries of achieving selectivity. These efforts have wide implications in the future of treating disease as they will broaden the possible targets that can be addressed by small molecules, like undruggable proteins or broadly active targets that would benefit from degradation in specific tissue types.
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Affiliation(s)
| | - Seung Wook Yang
- Induced Proximity Platform, Amgen, Thousand Oaks, CA 91320, USA.
| | - Jaeki Min
- Induced Proximity Platform, Amgen, Thousand Oaks, CA 91320, USA.
| | - Baikang Pei
- Genome Analysis Unit, Amgen, Thousand Oaks, CA 91320, USA
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3
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Sun DZ, Ye M, Ju DW, Xiu LJ, Pei B, Zhang CA, Lu Y, Jiao JP, Zhang X, Xu JY, Zhao Y, Wei PK, Yue XQ. The effects of gastric cancer interstitial fluid on tumors based on traditional Chinese medicine 'phlegm' theory and the investigation on the mechanism through microRNA-21 regulation. J Physiol Pharmacol 2021; 72. [PMID: 34810290 DOI: 10.26402/jpp.2021.3.07] [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] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 06/30/2021] [Indexed: 06/13/2023]
Abstract
This study aimed to investigate the effects of gastric cancer interstitial fluid (GCIF) on tumors and explore the possible mechanism of Xiaotan Sanjie decoction (XTSJ) on treatment of gastric cancer from the view of regulating microRNA-21 (miR-21) expression. The GCIF was extracted and identified by measuring the levels of interleukin-8 (IL-8), intercellular adhesion molecule 1 (ICAM-1) and miR-21. The effects of GCIF on the proliferation of SGC-7901 cells and tumor growing were assessed by cell counting kit-8 (CCK-8) assay and subcutaneously transplanted tumor-bearing nude mice model, respectively. Additionally, inhibition effect of XTSJ decoction on proliferation of SGC-7901 cells intervened by GCIF were assessed in vitro and anti-cancer effect of it was further assessed using orthotopic transplanted tumor-bearing nude mice model. The concentration of SGC-7901 gastric cancer cells were dependent on the concentration of the added GCIF. After 72 hours of continuous culture, the interstitial fluid had an obvious proliferative effect on the SGC-7901 tumor cells, which was the most significant in the high concentration group. XTSJ decoction could inhibit the growth-promoting effect (P < 0.01) of GCIF on gastric cancer cells. Intervention of the GCIF might promote the growth (P < 0.05) of the subcutaneously transplanted tumors in nude mice and decrease the net weight of the tumor-bearing nude mice (P < 0.05) after tumor removal. The GCIF was able to up-regulate the expression (P < 0.001) of miR-21 in the subcutaneously transplanted tumors. XTSJ decoction could downregulate the expression (P < 0.05) of miR-21 in SGC-7901 orthotopically transplanted tumors. XTSJ decoction can inhibit the multiplicative effect of GCIF on gastric cancer cells, growth of gastric tumor and promotion effect of GCIF on tumors, probably due to the down-regulating miR-21 expression in tumor tissues.
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Affiliation(s)
- D-Z Sun
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
| | - M Ye
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - D-W Ju
- Department of Outpatient, Central War Zone General Hospital of the Chinese People's Liberation Army, Wuhan, China
| | - L-J Xiu
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - B Pei
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - C-A Zhang
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Y Lu
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - J-P Jiao
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - X Zhang
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - J-Y Xu
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Y Zhao
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - P-K Wei
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - X-Q Yue
- Department of Traditional Chinese Medicine, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
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4
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Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I, Berry A, Bignell A, Boix C, Carbonell Sala S, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, García Girón C, Gonzalez JM, Grego T, Hardy M, Hourlier T, Howe KL, Hunt T, Izuogu OG, Johnson R, Martin FJ, Martínez L, Mohanan S, Muir P, Navarro FCP, Parker A, Pei B, Pozo F, Riera FC, Ruffier M, Schmitt BM, Stapleton E, Suner MM, Sycheva I, Uszczynska-Ratajczak B, Wolf MY, Xu J, Yang YT, Yates A, Zerbino D, Zhang Y, Choudhary JS, Gerstein M, Guigó R, Hubbard TJP, Kellis M, Paten B, Tress ML, Flicek P. GENCODE 2021. Nucleic Acids Res 2021; 49:D916-D923. [PMID: 33270111 PMCID: PMC7778937 DOI: 10.1093/nar/gkaa1087] [Citation(s) in RCA: 500] [Impact Index Per Article: 166.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/21/2020] [Accepted: 10/24/2020] [Indexed: 12/14/2022] Open
Abstract
The GENCODE project annotates human and mouse genes and transcripts supported by experimental data with high accuracy, providing a foundational resource that supports genome biology and clinical genomics. GENCODE annotation processes make use of primary data and bioinformatic tools and analysis generated both within the consortium and externally to support the creation of transcript structures and the determination of their function. Here, we present improvements to our annotation infrastructure, bioinformatics tools, and analysis, and the advances they support in the annotation of the human and mouse genomes including: the completion of first pass manual annotation for the mouse reference genome; targeted improvements to the annotation of genes associated with SARS-CoV-2 infection; collaborative projects to achieve convergence across reference annotation databases for the annotation of human and mouse protein-coding genes; and the first GENCODE manually supervised automated annotation of lncRNAs. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org.
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Affiliation(s)
- Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Irwin Jungreis
- MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, USA.,Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Julien Lagarde
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, Barcelona, E-08003 Catalonia, Spain
| | - Jane E Loveland
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Cristina Sisu
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.,Department of Bioscience, Brunel University London, Uxbridge UB8 3PH, UK
| | - James C Wright
- Functional Proteomics, Division of Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Joel Armstrong
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - If Barnes
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Andrew Berry
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alexandra Bignell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Carles Boix
- MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, USA.,Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.,Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Silvia Carbonell Sala
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, Barcelona, E-08003 Catalonia, Spain
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tomás Di Domenico
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Sarah Donaldson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ian T Fiddes
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Carlos García Girón
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jose Manuel Gonzalez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tiago Grego
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Matthew Hardy
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Thibaut Hourlier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Kevin L Howe
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Toby Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Osagie G Izuogu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rory Johnson
- Department of Medical Oncology, Inselspital, University Hospital, University of Bern, Bern, Switzerland.,Department of Biomedical Research (DBMR), University of Bern, Bern, Switzerland
| | - Fergal J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Laura Martínez
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Shamika Mohanan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Paul Muir
- Department of Molecular, Cellular & Developmental Biology, Yale University, New Haven, CT 06520, USA.,Systems Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Fabio C P Navarro
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Anne Parker
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Baikang Pei
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Fernando Pozo
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Ferriol Calvet Riera
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Magali Ruffier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Bianca M Schmitt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Eloise Stapleton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Marie-Marthe Suner
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Irina Sycheva
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Maxim Y Wolf
- Department of Biomedical Informatics at Harvard Medical School, 10 Shattuck Street, Suite 514, Boston, MA 02115, USA
| | - Jinuri Xu
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yucheng T Yang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.,Program in Computational Biology & Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Andrew Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Daniel Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Yan Zhang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.,Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Jyoti S Choudhary
- Functional Proteomics, Division of Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.,Program in Computational Biology & Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA.,Department of Computer Science, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, Barcelona, E-08003 Catalonia, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, E-08003 Catalonia, Spain
| | - Tim J P Hubbard
- Department of Medical and Molecular Genetics, King's College London, Guys Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Manolis Kellis
- MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, USA.,Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Michael L Tress
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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5
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Frankish A, Diekhans M, Ferreira AM, Johnson R, Jungreis I, Loveland J, Mudge JM, Sisu C, Wright J, Armstrong J, Barnes I, Berry A, Bignell A, Carbonell Sala S, Chrast J, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, García Girón C, Gonzalez JM, Grego T, Hardy M, Hourlier T, Hunt T, Izuogu OG, Lagarde J, Martin FJ, Martínez L, Mohanan S, Muir P, Navarro FC, Parker A, Pei B, Pozo F, Ruffier M, Schmitt BM, Stapleton E, Suner MM, Sycheva I, Uszczynska-Ratajczak B, Xu J, Yates A, Zerbino D, Zhang Y, Aken B, Choudhary JS, Gerstein M, Guigó R, Hubbard TJ, Kellis M, Paten B, Reymond A, Tress ML, Flicek P. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 2019; 47:D766-D773. [PMID: 30357393 PMCID: PMC6323946 DOI: 10.1093/nar/gky955] [Citation(s) in RCA: 1713] [Impact Index Per Article: 342.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 09/20/2018] [Accepted: 10/08/2018] [Indexed: 02/06/2023] Open
Abstract
The accurate identification and description of the genes in the human and mouse genomes is a fundamental requirement for high quality analysis of data informing both genome biology and clinical genomics. Over the last 15 years, the GENCODE consortium has been producing reference quality gene annotations to provide this foundational resource. The GENCODE consortium includes both experimental and computational biology groups who work together to improve and extend the GENCODE gene annotation. Specifically, we generate primary data, create bioinformatics tools and provide analysis to support the work of expert manual gene annotators and automated gene annotation pipelines. In addition, manual and computational annotation workflows use any and all publicly available data and analysis, along with the research literature to identify and characterise gene loci to the highest standard. GENCODE gene annotations are accessible via the Ensembl and UCSC Genome Browsers, the Ensembl FTP site, Ensembl Biomart, Ensembl Perl and REST APIs as well as https://www.gencodegenes.org.
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Affiliation(s)
- Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Anne-Maud Ferreira
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Rory Johnson
- Department of Medical Oncology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
- Department of Biomedical Research (DBMR), University of Bern, Bern, Switzerland
| | - Irwin Jungreis
- MIT Computer Science and Artificial Intelligence Laboratory, 32 Vasser St, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Jane Loveland
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Cristina Sisu
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Bioscience, Brunel University London, Uxbridge UB8 3PH, UK
| | - James Wright
- Functional Proteomics, Division of Cancer Biology, Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Joel Armstrong
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - If Barnes
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Andrew Berry
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alexandra Bignell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Silvia Carbonell Sala
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, Barcelona, E-08003 Catalonia, Spain
| | - Jacqueline Chrast
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tomás Di Domenico
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Sarah Donaldson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ian T Fiddes
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Carlos García Girón
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jose Manuel Gonzalez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tiago Grego
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Matthew Hardy
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Thibaut Hourlier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Toby Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Osagie G Izuogu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Julien Lagarde
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, Barcelona, E-08003 Catalonia, Spain
| | - Fergal J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Laura Martínez
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Shamika Mohanan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Paul Muir
- Department of Molecular, Cellular & Developmental Biology, Yale University, New Haven, CT 06520, USA
- Systems Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Fabio C P Navarro
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Anne Parker
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Baikang Pei
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Fernando Pozo
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Magali Ruffier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Bianca M Schmitt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Eloise Stapleton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Marie-Marthe Suner
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Irina Sycheva
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Jinuri Xu
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Andrew Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Daniel Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Yan Zhang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Bronwen Aken
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jyoti S Choudhary
- Functional Proteomics, Division of Cancer Biology, Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Program in Computational Biology & Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, Barcelona, E-08003 Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, E-08003 Catalonia, Spain
| | - Tim J P Hubbard
- Department of Medical and Molecular Genetics, King's College London, Guys Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Manolis Kellis
- MIT Computer Science and Artificial Intelligence Laboratory, 32 Vasser St, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Michael L Tress
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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He Y, Li J, Mao W, Zhang D, Liu M, Shan X, Zhang B, Zhu C, Shen J, Deng Z, Wang Z, Yu W, Chen Q, Guo W, Su P, Lv R, Li G, Li G, Pei B, Jiao L, Shen G, Liu Y, Feng Z, Su Y, Xie Y, Di W, Liu X, Yang X, Wang J, Qi J, Liu Q, Han Y, He J, Cai J, Zhang Z, Zhu F, Du D. HLA common and well-documented alleles in China. HLA 2018; 92:199-205. [DOI: 10.1111/tan.13358] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 06/22/2018] [Accepted: 07/29/2018] [Indexed: 11/29/2022]
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Dai YG, Gan P, Li WM, Yao Q, Li Y, Pei B, Cui J. [Effects of tetrahydrobiopterin on the angiogenesis in hepatocellular carcinoma]. Zhonghua Zhong Liu Za Zhi 2016; 38:806-811. [PMID: 27998437 DOI: 10.3760/cma.j.issn.0253-3766.2016.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the effect and mechanism of tetrahydrobiopterin (BH4) on the angiogenesis in hepatocellular carcinoma (HCC). Methods: BALB/c-nu mice were subcutaneously injected with HepG-2 cells and randomly divided into control and BH4 groups. The BH4 group and control group received 20 mg/kg BH4 or saline by intraperitoneal injection daily for two weeks, respectively. The level of BH4 was measured by high performance liquid chromatography (HPLC), the level of nitric oxide (NO) was measured by Griess test array, the transcriptional level of K-ras was measured by quantitative RT-PCR, and the protein expressions of guanosine triphosphate cyclohydrolase Ⅰ(GTPCH), endothelial nitric oxide synthase (eNOS), phospho-Akt and Akt were determined by Western blot. Results: BH4 level in the tumor tissues of BH4 group was (0.24±0.02) μg/ml, significantly higher than the (0.17±0.01) μg/ml in the control group (P<0.01). The level of NO in the tumor tissues of BH4 group was (51.44±2.90) mmol/L, significantly higher than the (24.77±0.54) mmol/L in the control group (P<0.01). The tumor volume of BH4 group was (191.05±8.70) mm3, significantly higher than the (103.10±5.03) mm3 in the control group (P<0.01). The expressions of CD34, K-ras, phospho-eNOS, phospho-Akt and GTPCH were significantly up-regulated in the tumor tissues of BH4 group when compared with those of the control group (P<0.01). Conclusions: BH4 recognized as an essential cofactor of eNOS can increase tumor-produced NO by activating the wild-type Ras-PI3K/Akt pathway, thus induces angiogenesis. This might provide a novel and promising way to control the progression of hepatocellular carcinoma through targeting BH4 synthesis pathway and inhibiting angiogenesis.
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Affiliation(s)
- Y G Dai
- Department of Abdominal Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - P Gan
- Department of Abdominal Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - W M Li
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Q Yao
- Yunnan Cancer Research Institute, Kunming 650118, China
| | - Y Li
- Department of Abdominal Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - B Pei
- Department of Abdominal Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - J Cui
- Department of Pathology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
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Zhao Y, Xue R, Shi N, Xue Y, Zong Y, Lin W, Pei B, Sun C, Fan R, Jiang Y. Aggravation of spinal cord compromise following new osteoporotic vertebral compression fracture prevented by teriparatide in patients with surgical contraindications. Osteoporos Int 2016; 27:3309-3317. [PMID: 27245056 DOI: 10.1007/s00198-016-3651-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 05/24/2016] [Indexed: 02/07/2023]
Abstract
UNLABELLED Patients with spinal cord deficits following new unstable osteoporotic compression fracture and surgical contraindications were considered to receive conservative treatment. Teriparatide was better than alendronate at improving bone mineral density and bone turnover parameters, as well as preventing aggravation of spinal cord compromise. INTRODUCTION This study compared the preventive effects of teriparatide and alendronate on aggravation of spinal cord compromise following new unstable osteoporotic vertebral compression fracture (OVCF) in patients with surgical contraindications. METHODS This was a 12-month, randomized, open-label study of teriparatide versus alendronate in 49 patients with new unstable OVCF and surgical contraindications. Neurological function was evaluated using modified Japanese Orthopedic Association (mJOA) score (11-point scale, the maximum score of 11 implies normalcy). Visual analog scale (VAS) scores, kyphotic angles, anterior-border heights and diameters of the spinal canal of the fractured vertebrae, any incident of new OVCFs (onset of OVCF during follow-up), spine bone mineral density (BMD), and serum markers of bone resorption and bone formation were also examined at baseline and 1, 3, 6, and 12 months after initiation of the medication regimen. RESULTS At 12 months, mean mJOA score had improved in the teriparatide group and decreased in the alendronate group. Mean concentrations of bone formation and bone resorption biomarkers, mean spine BMD, and mean anterior-border height and spinal canal diameter of the fractured vertebrae were significantly greater in the teriparatide group than in the alendronate group. Mean VAS score, mean kyphotic angle of the fractured vertebrae, and incidence of new OVCFs were significantly smaller in the teriparatide group than in the alendronate group. CONCLUSIONS In patients with neurological deficits following new unstable OVCF and with surgical contraindications, teriparatide was better than alendronate at improving the BMD and the bone turnover parameters, as well as preventing aggravation of spinal cord compromise.
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Affiliation(s)
- Y Zhao
- Department of Orthopaedics, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China
- Department of Radiology, The Secondary Affiliated Hospital of Baotou Medical College, No. 22 Hudemulin Road, Qingshan District, Inner Mongolia, China
| | - R Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China
- School of Medical Imaging, Tianjin Medical University, No. 1 Guandong Road, Hexi District, Tianjin, China
| | - N Shi
- Department of Operative Surgery, Tianjin Medical University, No. 22 Qixiangtai Road, Heping District, Tianjin, China
| | - Y Xue
- Department of Orthopaedics, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China.
| | - Y Zong
- Department of Orthopaedics, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China
| | - W Lin
- Department of Orthopaedics, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China
| | - B Pei
- Department of Orthopaedics, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China
| | - C Sun
- Department of Orthopaedics, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China
| | - R Fan
- Department of Orthopaedics, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China
| | - Y Jiang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, General Hospital of Tianjin Medical University, No. 154 Anshan Road, Heping District, Tianjin, China
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Gerstein MB, Rozowsky J, Yan KK, Wang D, Cheng C, Brown JB, Davis CA, Hillier L, Sisu C, Li JJ, Pei B, Harmanci AO, Duff MO, Djebali S, Alexander RP, Alver BH, Auerbach R, Bell K, Bickel PJ, Boeck ME, Boley NP, Booth BW, Cherbas L, Cherbas P, Di C, Dobin A, Drenkow J, Ewing B, Fang G, Fastuca M, Feingold EA, Frankish A, Gao G, Good PJ, Guigó R, Hammonds A, Harrow J, Hoskins RA, Howald C, Hu L, Huang H, Hubbard TJP, Huynh C, Jha S, Kasper D, Kato M, Kaufman TC, Kitchen RR, Ladewig E, Lagarde J, Lai E, Leng J, Lu Z, MacCoss M, May G, McWhirter R, Merrihew G, Miller DM, Mortazavi A, Murad R, Oliver B, Olson S, Park PJ, Pazin MJ, Perrimon N, Pervouchine D, Reinke V, Reymond A, Robinson G, Samsonova A, Saunders GI, Schlesinger F, Sethi A, Slack FJ, Spencer WC, Stoiber MH, Strasbourger P, Tanzer A, Thompson OA, Wan KH, Wang G, Wang H, Watkins KL, Wen J, Wen K, Xue C, Yang L, Yip K, Zaleski C, Zhang Y, Zheng H, Brenner SE, Graveley BR, Celniker SE, Gingeras TR, Waterston R. Comparative analysis of the transcriptome across distant species. Nature 2014; 512:445-8. [PMID: 25164755 PMCID: PMC4155737 DOI: 10.1038/nature13424] [Citation(s) in RCA: 239] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2013] [Accepted: 04/30/2014] [Indexed: 12/30/2022]
Abstract
The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters.
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Affiliation(s)
- Mark B Gerstein
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3] Department of Computer Science, Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA [4] [5]
| | - Joel Rozowsky
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3]
| | - Koon-Kiu Yan
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3]
| | - Daifeng Wang
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3]
| | - Chao Cheng
- 1] Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA [2] Institute for Quantitative Biomedical Sciences, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, USA [3]
| | - James B Brown
- 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA [3]
| | - Carrie A Davis
- 1] Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA [2]
| | - LaDeana Hillier
- 1] Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA [2]
| | - Cristina Sisu
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3]
| | - Jingyi Jessica Li
- 1] Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA [2] Department of Statistics, University of California, Los Angeles, California 90095-1554, USA [3] Department of Human Genetics, University of California, Los Angeles, California 90095-7088, USA [4]
| | - Baikang Pei
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3]
| | - Arif O Harmanci
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3]
| | - Michael O Duff
- 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2]
| | - Sarah Djebali
- 1] Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain [2] Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain [3]
| | - Roger P Alexander
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
| | - Burak H Alver
- Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, Massachusetts 02115, USA
| | - Raymond Auerbach
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
| | - Kimberly Bell
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Peter J Bickel
- Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA
| | - Max E Boeck
- Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA
| | - Nathan P Boley
- 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] Department of Biostatistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA
| | - Benjamin W Booth
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Lucy Cherbas
- 1] Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA [2] Center for Genomics and Bioinformatics, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA
| | - Peter Cherbas
- 1] Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA [2] Center for Genomics and Bioinformatics, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA
| | - Chao Di
- MOE Key Lab of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Alex Dobin
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Jorg Drenkow
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Brent Ewing
- Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA
| | - Gang Fang
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
| | - Megan Fastuca
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Elise A Feingold
- National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Adam Frankish
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Guanjun Gao
- MOE Key Lab of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Peter J Good
- National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Roderic Guigó
- 1] Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain [2] Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
| | - Ann Hammonds
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Jen Harrow
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Roger A Hoskins
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Cédric Howald
- 1] Center for Integrative Genomics, University of Lausanne, Genopode building, Lausanne 1015, Switzerland [2] Swiss Institute of Bioinformatics, Genopode building, Lausanne 1015, Switzerland
| | - Long Hu
- MOE Key Lab of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Haiyan Huang
- Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA
| | - Tim J P Hubbard
- 1] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK [2] Medical and Molecular Genetics, King's College London, London WC2R 2LS, UK
| | - Chau Huynh
- Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA
| | - Sonali Jha
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Dionna Kasper
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520-8005, USA
| | - Masaomi Kato
- Department of Molecular, Cellular and Developmental Biology, PO Box 208103, Yale University, New Haven, Connecticut 06520, USA
| | - Thomas C Kaufman
- Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA
| | - Robert R Kitchen
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
| | - Erik Ladewig
- Sloan-Kettering Institute, 1275 York Avenue, Box 252, New York, New York 10065, USA
| | - Julien Lagarde
- 1] Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain [2] Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
| | - Eric Lai
- Sloan-Kettering Institute, 1275 York Avenue, Box 252, New York, New York 10065, USA
| | - Jing Leng
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
| | - Zhi Lu
- MOE Key Lab of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Michael MacCoss
- Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA
| | - Gemma May
- 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2] Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 USA
| | - Rebecca McWhirter
- Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, Tennessee 37232-8240, USA
| | - Gennifer Merrihew
- Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA
| | - David M Miller
- Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, Tennessee 37232-8240, USA
| | - Ali Mortazavi
- 1] Developmental and Cell Biology, University of California, Irvine, California 92697, USA [2] Center for Complex Biological Systems, University of California, Irvine, California 92697, USA
| | - Rabi Murad
- 1] Developmental and Cell Biology, University of California, Irvine, California 92697, USA [2] Center for Complex Biological Systems, University of California, Irvine, California 92697, USA
| | - Brian Oliver
- Section of Developmental Genomics, Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Sara Olson
- Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA
| | - Peter J Park
- Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, Massachusetts 02115, USA
| | - Michael J Pazin
- National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA
| | - Norbert Perrimon
- 1] Department of Genetics and Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA [2] Howard Hughes Medical Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA
| | - Dmitri Pervouchine
- 1] Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain [2] Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
| | - Valerie Reinke
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520-8005, USA
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Genopode building, Lausanne 1015, Switzerland
| | - Garrett Robinson
- Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA
| | - Anastasia Samsonova
- 1] Department of Genetics and Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA [2] Howard Hughes Medical Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA
| | - Gary I Saunders
- 1] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK [2] European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Felix Schlesinger
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Anurag Sethi
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
| | - Frank J Slack
- Department of Molecular, Cellular and Developmental Biology, PO Box 208103, Yale University, New Haven, Connecticut 06520, USA
| | - William C Spencer
- Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, Tennessee 37232-8240, USA
| | - Marcus H Stoiber
- 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] Department of Biostatistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA
| | - Pnina Strasbourger
- Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA
| | - Andrea Tanzer
- 1] Bioinformatics and Genomics Programme, Center for Genomic Regulation, Universitat Pompeu Fabra (CRG-UPF), 08003 Barcelona, Catalonia, Spain [2] Institute for Theoretical Chemistry, Theoretical Biochemistry Group (TBI), University of Vienna, Währingerstrasse 17/3/303, A-1090 Vienna, Austria
| | - Owen A Thompson
- Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA
| | - Kenneth H Wan
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Guilin Wang
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520-8005, USA
| | - Huaien Wang
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Kathie L Watkins
- Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, Tennessee 37232-8240, USA
| | - Jiayu Wen
- Sloan-Kettering Institute, 1275 York Avenue, Box 252, New York, New York 10065, USA
| | - Kejia Wen
- MOE Key Lab of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Chenghai Xue
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Li Yang
- 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2] Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kevin Yip
- 1] Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong [2] 5 CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Chris Zaleski
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Yan Zhang
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
| | - Henry Zheng
- 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
| | - Steven E Brenner
- 1] Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA [2] Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA [3]
| | - Brenton R Graveley
- 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2]
| | - Susan E Celniker
- 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2]
| | - Thomas R Gingeras
- 1] Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA [2]
| | - Robert Waterston
- 1] Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA [2]
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Abyzov A, Iskow R, Gokcumen O, Radke DW, Balasubramanian S, Pei B, Habegger L, Lee C, Gerstein M. Analysis of variable retroduplications in human populations suggests coupling of retrotransposition to cell division. Genome Res 2013; 23:2042-52. [PMID: 24026178 PMCID: PMC3847774 DOI: 10.1101/gr.154625.113] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
In primates and other animals, reverse transcription of mRNA followed by genomic integration creates retroduplications. Expressed retroduplications are either “retrogenes” coding for functioning proteins, or expressed “processed pseudogenes,” which can function as noncoding RNAs. To date, little is known about the variation in retroduplications in terms of their presence or absence across individuals in the human population. We have developed new methodologies that allow us to identify “novel” retroduplications (i.e., those not present in the reference genome), to find their insertion points, and to genotype them. Using these methods, we catalogued and analyzed 174 retroduplication variants in almost one thousand humans, which were sequenced as part of Phase 1 of The 1000 Genomes Project Consortium. The accuracy of our data set was corroborated by (1) multiple lines of sequencing evidence for retroduplication (e.g., depth of coverage in exons vs. introns), (2) experimental validation, and (3) the fact that we can reconstruct a correct phylogenetic tree of human subpopulations based solely on retroduplications. We also show that parent genes of retroduplication variants tend to be expressed at the M-to-G1 transition in the cell cycle and that M-to-G1 expressed genes have more copies of fixed retroduplications than genes expressed at other times. These findings suggest that cell division is coupled to retrotransposition and, perhaps, is even a requirement for it.
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11
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Abstract
Bayesian network model is widely used for reverse engineering of biological network structures. An advantage of this model is its capability to integrate prior knowledge into the model learning process, which can lead to improving the quality of the network reconstruction outcome. Some previous works have explored this area with focus on using prior knowledge of the direct molecular links, except for a few recent ones proposing to examine the effects of molecular orderings. In this study, we propose a Bayesian network model that can integrate both direct links and orderings into the model. Random weights are assigned to these two types of prior knowledge to alleviate bias toward certain types of information. We evaluate our model performance using both synthetic data and biological data for the RAF signaling network, and illustrate the significant improvement on network structure reconstruction of the proposing models over the existing methods. We also examine the correlation between the improvement and the abundance of ordering prior knowledge. To address the issue of generating prior knowledge, we propose an approach to automatically extract potential molecular orderings from knowledge resources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) database and Gene Ontology (GO) annotation.
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Affiliation(s)
- Baikang Pei
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.
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12
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Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, Barnes I, Bignell A, Boychenko V, Hunt T, Kay M, Mukherjee G, Rajan J, Despacio-Reyes G, Saunders G, Steward C, Harte R, Lin M, Howald C, Tanzer A, Derrien T, Chrast J, Walters N, Balasubramanian S, Pei B, Tress M, Rodriguez JM, Ezkurdia I, van Baren J, Brent M, Haussler D, Kellis M, Valencia A, Reymond A, Gerstein M, Guigó R, Hubbard TJ. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 2013; 22:1760-74. [PMID: 22955987 PMCID: PMC3431492 DOI: 10.1101/gr.135350.111] [Citation(s) in RCA: 3491] [Impact Index Per Article: 317.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.
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Affiliation(s)
- Jennifer Harrow
- Wellcome Trust Sanger Institute, Wellcome Trust Campus, Hinxton, Cambridge CB10 1SA, United Kingdom.
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13
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Pei B, Xu S, Liu T, Pan F, Xu J, Ding C. Associations of theIL-1F7gene polymorphisms with rheumatoid arthritis in Chinese Han population. Int J Immunogenet 2012; 40:199-203. [PMID: 23171316 DOI: 10.1111/iji.12007] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2012] [Revised: 09/14/2012] [Accepted: 09/24/2012] [Indexed: 11/30/2022]
Affiliation(s)
- B. Pei
- Department of Rheumatology & Immunology; the First Affiliated Hospital, Anhui Medical University; Hefei; China
| | - S. Xu
- Department of Rheumatology & Immunology; the First Affiliated Hospital, Anhui Medical University; Hefei; China
| | - T. Liu
- Department of Rheumatology & Immunology; the First Affiliated Hospital, Anhui Medical University; Hefei; China
| | - F. Pan
- Department of Epidemiology and Biostatistics; School of Public Health, Anhui Medical University; Hefei; China
| | - J. Xu
- Department of Rheumatology & Immunology; the First Affiliated Hospital, Anhui Medical University; Hefei; China
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14
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Pei B, Sisu C, Frankish A, Howald C, Habegger L, Mu XJ, Harte R, Balasubramanian S, Tanzer A, Diekhans M, Reymond A, Hubbard TJ, Harrow J, Gerstein MB. The GENCODE pseudogene resource. Genome Biol 2012; 13:R51. [PMID: 22951037 PMCID: PMC3491395 DOI: 10.1186/gb-2012-13-9-r51] [Citation(s) in RCA: 253] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2012] [Revised: 05/30/2012] [Accepted: 06/25/2012] [Indexed: 12/11/2022] Open
Abstract
Background Pseudogenes have long been considered as nonfunctional genomic sequences. However, recent evidence suggests that many of them might have some form of biological activity, and the possibility of functionality has increased interest in their accurate annotation and integration with functional genomics data. Results As part of the GENCODE annotation of the human genome, we present the first genome-wide pseudogene assignment for protein-coding genes, based on both large-scale manual annotation and in silico pipelines. A key aspect of this coupled approach is that it allows us to identify pseudogenes in an unbiased fashion as well as untangle complex events through manual evaluation. We integrate the pseudogene annotations with the extensive ENCODE functional genomics information. In particular, we determine the expression level, transcription-factor and RNA polymerase II binding, and chromatin marks associated with each pseudogene. Based on their distribution, we develop simple statistical models for each type of activity, which we validate with large-scale RT-PCR-Seq experiments. Finally, we compare our pseudogenes with conservation and variation data from primate alignments and the 1000 Genomes project, producing lists of pseudogenes potentially under selection. Conclusions At one extreme, some pseudogenes possess conventional characteristics of functionality; these may represent genes that have recently died. On the other hand, we find interesting patterns of partial activity, which may suggest that dead genes are being resurrected as functioning non-coding RNAs. The activity data of each pseudogene are stored in an associated resource, psiDR, which will be useful for the initial identification of potentially functional pseudogenes.
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Affiliation(s)
- Baikang Pei
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
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Abstract
A Bayesian network model can be used to study the structures of gene regulatory networks. It has the ability to integrate information from both prior knowledge and experimental data. In this study, we propose an approach to efficiently integrate global ordering information into model learning, where the ordering information specifies the indirect relationships among genes. We demonstrate that, compared with a traditional Bayesian network model that uses only local prior knowledge, utilising additional global ordering knowledge can significantly improve the model's performance. The magnitude of this improvement depends on abundance of global ordering information and data quality.
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Affiliation(s)
- Baikang Pei
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.
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16
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Shin DG, Kazmi SA, Pei B, Kim YA, Maddox J, Nori R, Wong A, Krueger W, Rowe D. Computing consistency between microarray data and known gene regulation relationships. ACTA ACUST UNITED AC 2009; 13:1075-82. [PMID: 19783507 DOI: 10.1109/titb.2009.2032540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Microarray experiments produce expression patterns for thousands of genes at once. On the other hand, biomedical literature contains large amounts of gene regulation relationship information accumulated over the years. One obvious requirement is an automated way of comparing microarray data with the collection of known gene regulation relationships. Such an automated comparison is imperative because it can help biologists rapidly understand the context of a given microarray experiment. In addition, the consistency measure can be used to either validate or refute the hypothesis being tested using the microarray experiment. In this paper we present a systematic way of examining the consistency between a given set of microarray data and known gene regulation relationships. We first introduce a simple gene regulation network model with two separate algorithms designed to isolate a maximally consistent network. Subsequently, we extend the model to take into account multiple regulating factors for a single gene while highlighting both consistencies and inconsistencies. We illustrate the effectiveness of our approach with two practical examples, one that picks the peroxisome proliferator-activated receptor (PPAR) pathway as highly consistent from multiple pathways of Kyoto encyclopedia of genes and genomes (KEGG), and another that isolates key regulatory relationships involving nfkb1 and others known for macrophage's counter response to inflammation.
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Affiliation(s)
- Dong-Guk Shin
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.
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17
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Reddy S, Dalal S, Swafford J, El Osta B, Pei B, Palmer J, Bruera E. The effect of methadone on the QTc interval in advanced cancer patients. J Clin Oncol 2007. [DOI: 10.1200/jco.2007.25.18_suppl.9064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
9064 Background: Methadone (ME) has been used increasingly for pain control and for maintenance in drug addiction programs. Its use is increasing in cancer patients (pts), mainly as part of opioid rotation. Some recent reports suggest that ME may prolong QTc interval and cause torsade de pointes in pts on high dose ME. The purpose of our study was to determine the effect of initiation of ME on QTc interval in pts with cancer pain. Methods: We enrolled 101 pts in this prospective study. These pts had never been on ME before. Pts were followed clinically and electrocardiographically for QTc changes from baseline. EKG was obtained at baseline (QTc0), 2 (QTc2), 4 (QTc4), and 8 (QTc8) weeks. We recorded other contributing factors for QTc prolongation such as medication interactions with ME and electrolyte disturbances. QTc is prolongation is defined as > 430 ms in males and > 450 ms in females. In our study, significant QTc prolongation was defined as = 10% increase from baseline or QTc = 500 msec. Results: 74 pts (73%) had normal QTc (group A) and 27 pts (27%) had prolonged QTc (group B) at baseline. Significant increase in QTc in group A males occured in 1 of 16 (6%) at week 2, 2 of 10 (20%) at week 4, and 0 of 8 (0%) at week 8. Significant increase in QTc in group A females occured in 3 of 32 (10%) at week 2, 1 of 21 (5%) at week 4, and 0 of 12 (0%) at week 8. These pts had multiple contributing factors for QTc prolongation. QTc > reference for males occured in 6 of 16 (37%) at week 2, 4 of 10 (40%) at week 4, and 1 of 8 (12%) at week 8; in females, 1 of 32 (3%) at week 2, 3 of 21 (14%) at week 4, and 1 of 12 (8%) at week 8 ( Table 1 ). 2 of 27 pts (7%) from group B had a significant prolongation at week 2: one of them had an increase from 498 to 509 ms, then to 512 at week 4 and 486 at week 8. Conclusions: Baseline prolonged QTc is a common finding. QTc prolongation = 500 ms is rare in pts receiving ME for cancer pain. No data exists for other opioids. There was one case of temporary increase in QTc > 500 ms. There was no evidence of severe arrhythmias or torsade de pointes clinically or on EKG. ME dose was = 50 mg/day in the majority of these pts. [Table: see text] No significant financial relationships to disclose.
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Affiliation(s)
- S. Reddy
- UT M. D. Anderson Cancer Center, Houston, TX
| | - S. Dalal
- UT M. D. Anderson Cancer Center, Houston, TX
| | - J. Swafford
- UT M. D. Anderson Cancer Center, Houston, TX
| | - B. El Osta
- UT M. D. Anderson Cancer Center, Houston, TX
| | - B. Pei
- UT M. D. Anderson Cancer Center, Houston, TX
| | - J. Palmer
- UT M. D. Anderson Cancer Center, Houston, TX
| | - E. Bruera
- UT M. D. Anderson Cancer Center, Houston, TX
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18
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El Osta B, Palmer J, Paraskevopoulos T, Pei B, Roberts L, Poulter V, Chacko R, Bruera E. Interval between first palliative care consultation and death in patients with advanced cancer. J Clin Oncol 2007. [DOI: 10.1200/jco.2007.25.18_suppl.9028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
9028 Background: Most referrals to acute palliative care (PC) services occur late in the trajectory of the disease, although an earlier intervention can decrease patients’ (pts) symptoms distress. The purpose of this study was to determine the time interval between first palliative care consultation (PC1) and death (D) in pts diagnosed with advanced cancer (aCA) at our comprehensive cancer center and whether such interval has increased over time. Methods: The study group was 2,868 consecutive pts who had their PC1 during a 30-month period. We reviewed the charts for information about demographics, cancer type, date of cancer diagnosis, aCA diagnosis, PC1, and D. aCA was defined as locally recurrent or metastatic. Results: 1,404 pts (49%) were female, 1,791 (62%) were < 65 years old, 2,563 (89%) had solid cancer, and 2,004 (70%) were white. The median PC1-D, aCA- PC1, and aCA-D intervals were 40, 114, and 243 days respectively. The median PC1-D interval (days) was: 47 for pts with solid cancer vs 14 for pts with hematological malignancy (p < 0.0001); 44 for pts < 65 years old vs 36 for pts = 65 years old (p = 0.002); 45 for females vs 37 for males (p = 0.004); 40 for white pts vs 41 for pts from other ethnicities (p = 0.42). The median PC1-D interval in 5 consecutive half-years was 46, 56, 42, 41, and 34 days respectively (p = 0.02). The total number of pts referred for PC1 in this period increased 20%, from 544 to 654. The ratio of PC involvement period in the aCA-D interval (PC1-D/aCA-D) decreased from 0.30 to 0.26 over the 5 half-year periods (p = 0.0004) ( Table ). Conclusions: Patients with solid cancers, younger pts, and females pts were referred earlier to acute PC. Referral timing was not affected by ethnicity. The interval between first palliative care consult and death has decreased over time. Education is needed among referring physicians to increase this interval. Further research on increasing acute PC access and its impact on PC1-D interval is needed. [Table: see text] No significant financial relationships to disclose.
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Affiliation(s)
- B. El Osta
- University of Texas M. D. Anderson Cancer Center, Houston, TX
| | - J. Palmer
- University of Texas M. D. Anderson Cancer Center, Houston, TX
| | | | - B. Pei
- University of Texas M. D. Anderson Cancer Center, Houston, TX
| | - L. Roberts
- University of Texas M. D. Anderson Cancer Center, Houston, TX
| | - V. Poulter
- University of Texas M. D. Anderson Cancer Center, Houston, TX
| | - R. Chacko
- University of Texas M. D. Anderson Cancer Center, Houston, TX
| | - E. Bruera
- University of Texas M. D. Anderson Cancer Center, Houston, TX
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Bruera E, El Osta B, Valero V, Driver L, Palmer J, Pei B, Shen L, Poulter V. Donepezil for cancer-related fatigue: A double-blind, randomized, placebo-controlled study. J Clin Oncol 2007. [DOI: 10.1200/jco.2007.25.18_suppl.9003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
9003 Background: Fatigue is the most frequent symptom in advanced cancer. No standard treatment is available. We previously found that open-label donepezil significantly improved fatigue by day 3 and 7 in patients (pts) on opioids for cancer pain (Fisch et al, ASCO 2003). The purpose of this study was to compare donepezil (D) with placebo (P) for fatigue in pts with advanced cancer. Methods: In this randomized, double-blind, placebo-controlled trial, pts with fatigue score = 4 on a 0 to 10 scale (10 = worst fatigue) for > 1 week, hemoglobin = 10g/dl for = 4 weeks, and no major contraindication to D were randomized to receive D 5 mg or P orally every morning for 7 days. All pts were offered open-label D during week 2. Assessment included: research nurse daily phone call for fatigue and toxicity evaluation, Edmonton Symptom Assessment System (ESAS), Functional Assessment for Chronic Illness Therapy-Fatigue (FACIT-F), Sleeping Pattern Assessment, and overall effectiveness of the treatment. The FACIT-F fatigue subscale score on day 8 was considered the primary endpoint. Results: 103 pts were evaluable for final analysis. Mean difference in scores for symptoms intensity between baseline and day 8 are shown in Table 1 . FACIT-F fatigue subscale score at day 8 decreased a mean of 6 (10.6 SD) in the D arm (p < 0.001) and 7.2 (9.5 SD) in the P arm (p < 0.001). There was no significant difference in fatigue improvement between both arms according to the FACIT-F subscale (p = 0.57) and ESAS fatigue (p = 0.18) scores, and no significant difference in sleep quality score between D and P. On day 15 of the open-label phase, mean fatigue intensity remained significantly improved as compared to baseline on FACIT-F fatigue subscale (p < 0.001) and ESAS fatigue (p < 0.001) scores. No significant toxicities were observed. Conclusions: Both donepezil and placebo resulted in significant fatigue improvement. Donepezil was not significantly superior to placebo after one week. Our pilot findings are probably due to placebo effect. No significant financial relationships to disclose. [Table: see text]
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Affiliation(s)
- E. Bruera
- M. D. Anderson Cancer Center, Houston, TX
| | - B. El Osta
- M. D. Anderson Cancer Center, Houston, TX
| | - V. Valero
- M. D. Anderson Cancer Center, Houston, TX
| | - L. Driver
- M. D. Anderson Cancer Center, Houston, TX
| | - J. Palmer
- M. D. Anderson Cancer Center, Houston, TX
| | - B. Pei
- M. D. Anderson Cancer Center, Houston, TX
| | - L. Shen
- M. D. Anderson Cancer Center, Houston, TX
| | - V. Poulter
- M. D. Anderson Cancer Center, Houston, TX
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20
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Thaker PH, Sun C, Bodurka DC, Palmer J, Pei B, Willey J, Bruera E, Ramondetta L. Spirituality, quality of life, and locus of control in a palliative care setting. J Clin Oncol 2006. [DOI: 10.1200/jco.2006.24.18_suppl.18529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
18529 Background: A patient’s spirituality/religious beliefs have a profound role on how one copes with disease & on quality of life (QOL). Perceptions of control play an important role in coping not only with stressful experiences, but also in health outcomes. Therefore, the primary objective was to determine whether patients’ spirituality/ religiosity correlates with quality of life and locus of control. Methods: As part of a pilot study, pts presenting for initial outpatient evaluation in the Department of Symptom Control & Palliative Care were enrolled and completed self-report measures: Functional Assessment of Chronic Illness Therapy-General (FACT-G), FACT-Spiritual Well-Being Scale (FACT-Sp), Duke University Religion Index (DUREL), Locus of Control (LOC), Herth Hope Scale (HHS), Predestination (PDQ), and Hospital Anxiety & Depression Scale (HADS). LOC contained 3 subscales: perceived occurrence of chance, dependence on powerful others, and internal control. Pearson correlation coefficients were calculated to explore the relationship between measures. The Mann-Whitney t-test was used to compare patient scores. Results: One hundred patients (48 men & 52 women) completed the surveys & 90% reported a Christian affiliation. QOL was positively correlated with FACT-Sp (p ≤ 0.001, r = .614) and the DUREL which measures both external/internal religiosity (p ≤ .01, r = .291). Interestingly, there was no gender difference in spirituality as measured by FACT-Sp; however, by the DUREL women engaged more frequently in private religious activity when compared with men (p < 0.001). Men had more perceived internal control with less emphasis on the occurrence of chance events or dependence on powerful others on LOC (p = 0.07), as well as a positive correlation with controlling of one’s own fate as measured by the PDQ (p = 0.1). Conclusions: As oncologists committed to providing comprehensive care, we need to be receptive to the spiritual needs of our patients since it augments their QOL and to empower them to have a sense of control. Future studies need to further define these complex relationships and to recognize possible gender differences. No significant financial relationships to disclose.
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Affiliation(s)
| | - C. Sun
- M. D. Anderson Cancer Center, Houston, TX
| | | | - J. Palmer
- M. D. Anderson Cancer Center, Houston, TX
| | - B. Pei
- M. D. Anderson Cancer Center, Houston, TX
| | - J. Willey
- M. D. Anderson Cancer Center, Houston, TX
| | - E. Bruera
- M. D. Anderson Cancer Center, Houston, TX
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Walker PW, Bruera E, Pei B, Kaur G, Zhang K, Jeanine H, Curry E, Palla S, Mansell M. Switching from methadone to a different opioid: What is the equianalgesic dose ratio? J Clin Oncol 2006. [DOI: 10.1200/jco.2006.24.18_suppl.8617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
8617 Background: Methadone (ME) is a highly effective opioid agonist used for difficult pain syndromes. However, the rotation from ME to another opioid may be difficult because of the absence of a uniformly accepted conversion ratio. Methods: We retrospectively reviewed consecutive medical records of Pts undergoing an opioid rotation from ME to an alternative opioid. For inclusion, Pts were required to have received ME for at least 3 days prior to the switch and reach a stable dose of the alternative opioid(s) during 7 days following. Stable dose was defined as a 30% or less change in opioid dose from one day to the next. For purposes of analysis, on the day before the switch, doses, were divided into ME doses and the oral morphine equivalent daily dose (MEDD), based on medication and route of all other opioids taken on that day, using standard equinalgesic tables. All doses after the switch were converted to the MEDD. For Pts receiving ME and a second opioid prior to the switch, the MEDD of the second opioid was subtracted from the MEDD calculated for the day when stable dose was reached. The remainder was used to calculate the equianalgesic raio with the previous ME dose. Results: Records on 39 Pts met inclusion criteria. Excluded from analysis were 5 Pts who were restarted on ME in < 8 days, 2 whose opioid dose markedly decreased of post switch, and 3 due to concerns about reliability of multiple routes used for fentanyl. Data from 29 Pts, 10 female, mean age 48 ±14.4 were evaluable. The ratio for: oral ME to MEDD was 1:4.7 (CL 3.0–6.5)(n=16), IV ME to MEDD was 1:13.5 (CL6.6–20.5)(n=13), p=0.06. ME dose is significantly correlated to stable MEDD after switching opioids for both ME IV and oral (Spearman=0.86,p=0.0001 and Spearman=0.72, p=0.0024, respectively. Mean day of achieving stable dose was on day 2.5 ±0.2 for IV ME and day 2.6±0.3 for oral ME. Conclusions: These dose ratios are new findings that will assist in switching Pts more safely to alternative opioids, when side effects or pain problems occur.An important difference in analgesic potency appears to exist between IV and oral ME. Further research with prospective studies is required. No significant financial relationships to disclose.
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Affiliation(s)
| | - E. Bruera
- UT M. D. Anderson Cancer Center, Houston, TX
| | - B. Pei
- UT M. D. Anderson Cancer Center, Houston, TX
| | - G. Kaur
- UT M. D. Anderson Cancer Center, Houston, TX
| | - K. Zhang
- UT M. D. Anderson Cancer Center, Houston, TX
| | - H. Jeanine
- UT M. D. Anderson Cancer Center, Houston, TX
| | - E. Curry
- UT M. D. Anderson Cancer Center, Houston, TX
| | - S. Palla
- UT M. D. Anderson Cancer Center, Houston, TX
| | - M. Mansell
- UT M. D. Anderson Cancer Center, Houston, TX
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22
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Zhukovsky DS, Palmer J, Bruera E, Pei B, Zhang T, Nekolaichuk C, Fainsinger R. Characterization of cancer pain syndromes (PS) seen at a Comprehensive Cancer Center (CCC) and pain response (PR) to palliative care consultation (PCC). J Clin Oncol 2006. [DOI: 10.1200/jco.2006.24.18_suppl.8551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
8551 Background: Comparison of cancer PS across settings is challenging due to differences in prognostic features. Data from 1 CCC participating in a multi-site international study of a pain classification system is presented to characterize cancer PS & response to PCC. Methods: The Edmonton Classification System for Cancer Pain was completed by prospective chart review to characterize PS of 100 consecutive hospitalized patients (pts) seen in PCC. Pts were followed until major PR, hospital discharge or death. Major PR was defined as <2 p.r.n. opioid doses/d & pain intensity (PI) <3/10 for 3 consecutive days (d). Results: 85% of pts had pain (n=85), with age 62.9+13.3, 47.1% male & KPS 44.5+23.1. The most common tumor diagnoses were lung (24.7%) & GU (21.2%). Pts were followed for a median of 4 d (0–27). 39% achieved a major PR. Except for steroids (49.4%) & anticonvulsants (29.4%), other adjuvant analgesic use was all <10%. Pain-associated features: *Numeric Rating Scale 0–10, 10=worst suggestivie of alcoholism + Mean morphine equivalent dailydose On univariate analysis, older age (p=.006), lower initial PI (p=.003), lower final PI (p=.001) & lower final MEDD (p=.002) were significantly associated with achieving major PR. On multivariate analysis, lower initial PI (p=.03) & lower final MEDD (p=.02) retained significance for achieving major PR. Conclusions: Only 39%of pts with cancer pain seen in PCC achieve a major PR by discharge or death. Despite aggressive opioid titration, 61% do not achieve a major PR & require better pain management. Potential strategies for achieving improved PR include earlier PCC, identification of more sensitive prognostic variables &critical evaluation of targeted therapies. [Table: see text] No significant financial relationships to disclose.
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Affiliation(s)
- D. S. Zhukovsky
- UT M. D. Anderson Cancer Center, Houston, TX; University of Alberta, Edmonton, AB, Canada
| | - J. Palmer
- UT M. D. Anderson Cancer Center, Houston, TX; University of Alberta, Edmonton, AB, Canada
| | - E. Bruera
- UT M. D. Anderson Cancer Center, Houston, TX; University of Alberta, Edmonton, AB, Canada
| | - B. Pei
- UT M. D. Anderson Cancer Center, Houston, TX; University of Alberta, Edmonton, AB, Canada
| | - T. Zhang
- UT M. D. Anderson Cancer Center, Houston, TX; University of Alberta, Edmonton, AB, Canada
| | - C. Nekolaichuk
- UT M. D. Anderson Cancer Center, Houston, TX; University of Alberta, Edmonton, AB, Canada
| | - R. Fainsinger
- UT M. D. Anderson Cancer Center, Houston, TX; University of Alberta, Edmonton, AB, Canada
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23
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Pei B, Hu JH, Li DS. [Clinical application of sural nerve island flap pedicled with collateral vessels]. Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi 2000; 14:223-5. [PMID: 12078307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
OBJECTIVE To sum up the application experience of the sural nerve island flap pedicled with the collateral vessels. METHODS From 1997, the retrograde-flow sural nerve island flaps pedicled with collateral vessels were performed to repair the soft tissues defects of the shank in 3 cases, ankle in 3 cases and foot in 8 cases. RESULTS Twelve flaps were survived, one flap was partially necrosed and one flap was necrosed. Among them, 10 wounds healed by first intention, 3 cases were healed after changing dressing and the one necrosed flap was repaired by free flap transplantation. Nine cases were followed up for 3 to 21 months and had fine appearance and function. The flap texture was similar to normal skin, the sensation of flap partially recovered after 6 months. CONCLUSION The flap has more reliable blood supply and great rotation arc, it is easy to resect with little injury. It is excellent for repairing the soft tissues defect in the anterior leg, ankle and proximal half of foot. It is more significant while the main blood vessels are damaged.
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Affiliation(s)
- B Pei
- Department of Hand Surgery, First Hospital of Xiangfan, Xiangfan, Hubei, P. R. China 441000
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24
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Shang DQ, Li LY, Pei B. [An epidemiological investigation of eperythrozoon infection in human and animals (II)]. Zhonghua Liu Xing Bing Xue Za Zhi 1996; 17:221-4. [PMID: 9387587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This paper reported an epidemiological investigation on human and animals infection of eperythrozoon in 1 provinces. The results showed that eperythrozoon infection appeared in human as well as in swines, sheep, cats, donkeis and chickens. Due to geographical variations, the infection rates showed a significant difference, both in human and animals. The infection rate was not associated with sex, age or occupation in human, but was associated with seasons in animals. High peak of infection rates in animals was in May, June, July and August.
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Affiliation(s)
- D Q Shang
- Institute of Epidemiology and Microbiology, Chinese Academy of Preventive Medicine, Beijing
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