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Articles relevant to OMICS and Systems Medicine
Please send references to articles that are not listed below to the webmaster!
Reviews
Microarrays/proteomics
Benson M, Cardell LO, Jernås M, Carlsson B, Reinholdt J, Svensson PA, Carlsson L. DNA microarrays to profile gene expression in allergic rhinitis. Clin Exp Allergy 2002; 31:301-8.
Pawliczak R, Shelhamer JH. Application of functional genomics in allergy and clinical immunology. Allergy. 2003;58:973-80.
Harwanegg C, Hiller R. Protein microarrays in diagnosing IgE-mediated diseases: spotting allergy at the molecular level. Expert Rev Mol Diagn 2004;4:539-48.
Systems biology
Alon U. Biological networks: the tinkerer as an engineer. Science 2003;301:1866-7.
Hood R.
Systems biology and new technologies enable predictive and preventative medicine.
Science 2004;306:640-3.
Original microarray articles
Selected microarray articles in fields other than allergy
Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001;7:673-9.
Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000; 403:503-11.
Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 2002;62:4427-33.
Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, Gaasenbeek et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 2002;8:68-74.
Microarray articles in allergy/immunology research
Adner M, Zhang Y, Swärd K, Benson M, Lars Olaf Cardell. Up-regulation of 5-HT2A receptor-mediated contractile responses in mousetrachea following long-term exposure to TNFa. Brit J Pharmacol 2002; 137:971-82
Argyropoulos C, Nikiforidis GC, Theodoropoulou M, Adamopoulos P, Boubali S, et al. Mining microarray data to identify transcription factors expressed in naive resting but not activated T lymphocytes. Genes Immun 2004;5:16-25.
Benson M, Adner M, Jansson L, Lutz A, Uddman R, Cardell LO. Gene profiling reveals increased expression of uteroglobin and other anti-inflammatory genes in nasal fluid cells from patients with allergic rhinitis. Clin Exp Allergy 2005 (in press)
Benson M, Carlsson LM, Adner M, Jernås M, Rudemo M, Sjögren A, Uddman R Cardell LO. Gene profiling reveals increased expression of uteroglobin and other anti-inflammatory genes in nasal polyps after treatment with glucocorticoids. J Allergy and Clinical Immunology 2004;113:1137
Benson M, Svensson PA, Carlsson B, Carlsson L, Martinsson T, Rudemo M, Cardell.LO Combining linkage- and DNA microarray analysis to identify susceptibility genes in allergic disease. Acta Otolar 2004;124:813-819
Benson M Jernås, B Carlsson, J Reinholdt, PA Svensson, L Carlsson, LO Cardell. DNA microarray analysis of Transforming Growth Factor Beta and related transcripts in nasal biopsies from patients with allergic rhinitis. Cytokine. 2002;18:20-5
Benson M, Fransson M, Wennergren G, Cardell LO. Increased expression of Vascular Endothelial Growth Factor in seasonal allergic rhinitis. Cytokine 2002;20:268-73
Benson M, LO Cardell, M Jernås, B Carlsson, J Reinholdt, PA Svensson, L Carlsson. DNA microarrays to profile gene expression in allergic rhinitis. Clin Exp Allergy 2002; 31:301-8
Brutsche MH, Joos L, Carlen Brutsche IE, Bissinger R, Tamm M, Custovic A, Woodcock A. Array-based diagnostic gene-expression score for atopy and asthma.J Allergy Clin Immunol 2002;109:271-3.
Fritz SB, Terrell JE, Conner ER, Kukowska-Latallo JF, Baker JR. Nasal mucosal gene expression in patients with allergic rhinitis with and without nasal polyps. J Allergy Clin Immunol 2003;112:1057-63.
Hakonarson H, Halapi E, Whelan R, Gulcher J, Stefansson K, Grunstein MM. Association between IL-1beta/TNF-alpha-induced glucocorticoid-sensitive changes in multiple gene expression and altered responsiveness in airway smooth muscle. Am J Respir Cell Mol Biol 2001;25:761-71.
Karp CL, Grupe A, Schadt E, Ewart SL, Keane-Moore M, Cuomo PJ, Kohl J, Wahl L, Kuperman D, Germer S, Aud D, Peltz G, Wills-Karp M. Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nat Immunol 2000;1:221-6.
Lee JH, Kaminski N, Dolganov G, Grunig G, Koth L, Solomon C, Erle DJ, Sheppard D. Interleukin-13 induces dramatically different transcriptional programs in three human airway cell types. Am J Respir Cell Mol Biol 2001; 25:474-85.
Lund R, Aittokallio T, Nevalainen O, Lahesmaa R. Identification of novel genes regulated by IL-12, IL-4, or TGF-beta during the early polarization of CD4+ lymphocytes. J Immunol 2003; 171:5328-36.
Nomura I, Gao B, Boguniewicz M, Darst MA, Travers JB, Leung DY Distinct patterns of gene expression in the skin lesions of atopic dermatitis and psoriasis: a gene microarray analysis. .J Allergy Clin Immunol 2003;112:1195-202.
Wohlfahrt JG, Kunzmann S, Menz G, Kneist W, Akdis CA, Blaser K, Schmidt-Weber CB. T cell phenotype in allergic asthma and atopic dermatitis. Int Arch Allergy Immunol 2003;131(4):272-82
Zimmermann N, King NE, Laporte J, Yang M, Mishra A, Pope SM, et al. Dissection of experimental asthma with DNA microarray analysis identifies arginase in asthma pathogenesis. J Clin Invest. 2003;111:1863-74.
Original proteomics articles
Selected articles in fields other than allergy
Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM, Mills GB, Simone C, Fishman DA, Kohn EC, Liotta LA. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359:572-7.
Allergy/immunology related articles
Bacarese-Hamilton T, Mezzasoma L, Ingham C, Ardizzoni A, Rossi R, Bistoni F, Crisanti A. Detection of allergen-specific IgE on microarrays by use of signal amplification techniques. Clin Chem. 2002;48:1367-70
Hiller R, Laffer S, Harwanegg C, Huber M, Schmidt WM, Twardosz A, Barletta B, Becker WM, Blaser K, Breiteneder H, Chapman M, Crameri R, Duchene M, Ferreira F, Fiebig H, Hoffmann-Sommergruber K, King TP, Kleber-Janke T, Kurup VP, Lehrer SB, Lidholm J, Muller U, Pini C, Reese G, Scheiner O, Scheynius A, Shen HD, Spitzauer S, Suck R, Swoboda I, Thomas W, Tinghino R, Van Hage-Hamsten M, Virtanen T, Kraft D, Muller MW, Valenta R. Microarrayed allergen molecules: diagnostic gatekeepers for allergy treatment. FASEB J. 2002;16:414-6.
Tam SW, Wiese R, Lee S, Gilmore J, Kumble KD. Simultaneous analysis of eight human Th1/Th2 cytokines using microarrays. J Immunol Methods 2002; 261:157-65.
Bioinformatics
Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density array data based on variance and bias. Bioinformatics 2003; 19:185-193.
Stoeckert CJ Jr, Causton HC, Ball CA. Microarray databases: standards and ontologies. Nat Genet 2002;32 Suppl:469-73.
Kooperberg C, Sipione S, LeBlanc ML, Strand AD, Cattaneo E, Olson JM. Evaluating test-statistics to select interesting genes in microarray experiments. Human Molecular Genetics. 2002; 11:2223-32,
Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet 2002; 31:19-20
Jenssen TK, Laegreid A, Komorowski J, Hovig E.. A literature network of human genes for high-throughput analysis of gene expression. Nat Genet 2001;28:21-8.
Chuaqui RF, Bonner RF, Best CJ, Gillespie JW, Flaig MJ, Hewitt SM, Phillips JL, Krizman DB, Tangrea MA, Ahram M, Linehan WM, Knezevic V, Emmert-Buck MR. Post-analysis follow-up and validation of microarray experiments. Nat Genet 2002; 32 Suppl:509-14
Public databases for microarray experiments
Entrez is a linked system of databases to find information about individual genes and literature references (http://www.ncbi.nlm.nih.gov/). One of the databases, Omnibus, contains freely searchable data from more than 500 microarray experiments (http://www.ncbi.nlm.nih.gov/geo/)
BASE (http://base.thep.lu.se/) aims to provide all the tools needed for data storage, quality control, normalization and statistical application in a web-based application (Lao).
Minimum information about a microarray experiment – MIAME gives standards for microarray experiments are described at (http://www.mged.org/Workgroups/MIAME/miame.html)
The Gene Ontology Consortium and Pubgene and provides tools for automated grouping of functionally related genes (http://www.geneontology.org, http://www.pubgene.org/). GenMAPP may be used to identify activated biological pathways in gene expression data (http://www.genmapp.org/)
Additional references on protein-microarrays and allergy
(a kind contribution of Pr. Paolo Matricardi)
Kim TE, et al. Quantitative measurement of serum allergen-specific IgE on protein chip. Exp Mol Med 2002;34:152-8
Jahn-Schmid B, et al. Allergen microarray: comparison of microarray using recombinant allergens with conventional diagnostic methods to detect allergen-specific serum immunoglobulin E. Clin Exp Allergy 2003;33:1443-9
Fall BI, et al. Microarrays for the screening of allergen-specific IgE in human serum. Anal Chem 2003;75:556-62
Harwanegg C, et al. Microarrayed recombinant allergens for diagnosis of allergy. Clin Exp Allergy 2003;33:7-13.
Deinhofer K, et al. Microarrayed allergens for IgE profiling. Methods 2004;32:249-54.
Pittner G, et al. Component-resolved diagnosis of house-dust mite allergy with purified natural and recombinant mite allergens. Clin Exp Allergy 2004;34:597-603.Last updated: 16 January 2015 -
Link to hot topics
Hot topics in Functional Genomics and Proteomics
Complex diseases like allergy depend on altered interactions between multiple genes and environmental factors. At a recent meeting arranged by the Welcome Trust and Nature Genetics researchers compared notes from genome-wide association studies of complex diseases (http://www.nature.com/ng/meetings/genomics). It was predicted that hundreds or even thousands of genes could be involved in each complex disease. On top of this complexity different genes may be involved in different patients that appear to have the same disease. If it were possible to get a grip on this complexity it might be possible to gain increased understanding of disease mechanisms as well as to find new diagnostic or therapeutic targets.
Recent studies indicate that this may be achieved by network-based analysis of genomic high-throughput data. Essentially disease-associated genes are identified and organized in networks that are analyzed in a top-down manner. First, modules of interacting genes with distinct biological functions are identified. Then the modules are dissected to find pathways and finally upstream genes with key regulatory functions. Network-based analyses of cancer and allergic disease have resulted in the identification of new disease mechanisms, genes, single nucleotide polymorphisms and diagnostic markers.
Hot topics in allergy research are to apply network-based analysis to study disease mechanisms in different cells and tissues as well to find new diagnostic and therapeutic targets. Another challenge is to integrate data from different forms of high-throughput studies to form modules that describe disease-associated changes on multiple levels, ranging from DNA to protein.Last updated: 07 November 2014 -
Link to articles
Articles relevant to functional genomics and proteomics
Please send references to articles that are not listed below to the webmaster!
Comment on the must read papers of the last 3-6 months:
A summary of a recent meeting about genome-wide studies of complex diseases addresses the huge challenges and possible solutions:
Petretto E, Aitman TJ. A gene harvest revealing the archeology and complexity of human disease. Nature Gen 2007; 39: 1299-3002
This study is a very elegant example of how integrated network-based analysis of high-throughput data from humans and model organisms as well as data from the public data can be used to identify new disease mechanisms, genes and polymorphisms:
Pujana et al. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet. 2007;39:1338-1349
This article is a conceptually brilliant discussion about how different complex diseases can be described as nodes in network. The nodes are described both geno- and phenotypically and there is considerable overlap:
Barabasi AL. Network medicine--from obesity to the "diseasome".
N Engl J Med. 2007
Reviews
Microarrays/proteomics
Benson M, Cardell LO, Jernås M, Carlsson B, Reinholdt J, Svensson PA, Carlsson L. DNA microarrays to profile gene expression in allergic rhinitis. Clin Exp Allergy 2002; 31:301-8.
Pawliczak R, Shelhamer JH. Application of functional genomics in allergy and clinical immunology. Allergy. 2003;58:973-80.
Harwanegg C, Hiller R. Protein microarrays in diagnosing IgE-mediated diseases: spotting allergy at the molecular level. Expert Rev Mol Diagn 2004;4:539-48.
Systems biology
Alon U. Biological networks: the tinkerer as an engineer. Science 2003;301:1866-7.
Hood R.
Systems biology and new technologies enable predictive and preventative medicine.
Science 2004;306:640-3.
Original microarray articles
Selected microarray articles in fields other than allergy
Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001;7:673-9.
Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000; 403:503-11.
Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 2002;62:4427-33.
Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, Gaasenbeek et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 2002;8:68-74.
Microarray articles in allergy/immunology research
Adner M, Zhang Y, Swärd K, Benson M, Lars Olaf Cardell. Up-regulation of 5-HT2A receptor-mediated contractile responses in mousetrachea following long-term exposure to TNFa. Brit J Pharmacol 2002; 137:971-82
Argyropoulos C, Nikiforidis GC, Theodoropoulou M, Adamopoulos P, Boubali S, et al. Mining microarray data to identify transcription factors expressed in naive resting but not activated T lymphocytes. Genes Immun 2004;5:16-25.
Benson M, Adner M, Jansson L, Lutz A, Uddman R, Cardell LO. Gene profiling reveals increased expression of uteroglobin and other anti-inflammatory genes in nasal fluid cells from patients with allergic rhinitis. Clin Exp Allergy 2005 (in press)
Benson M, Carlsson LM, Adner M, Jernås M, Rudemo M, Sjögren A, Uddman R Cardell LO. Gene profiling reveals increased expression of uteroglobin and other anti-inflammatory genes in nasal polyps after treatment with glucocorticoids. J Allergy and Clinical Immunology 2004;113:1137
Benson M, Svensson PA, Carlsson B, Carlsson L, Martinsson T, Rudemo M, Cardell.LO Combining linkage- and DNA microarray analysis to identify susceptibility genes in allergic disease. Acta Otolar 2004;124:813-819
Benson M Jernås, B Carlsson, J Reinholdt, PA Svensson, L Carlsson, LO Cardell. DNA microarray analysis of Transforming Growth Factor Beta and related transcripts in nasal biopsies from patients with allergic rhinitis. Cytokine. 2002;18:20-5
Benson M, Fransson M, Wennergren G, Cardell LO. Increased expression of Vascular Endothelial Growth Factor in seasonal allergic rhinitis. Cytokine 2002;20:268-73
Benson M, Olsson M, Rudemo M, Wennergren G, CardellLO. Pros and cons of microarray technology in allergy research.Clin Exp Allergy. 2004; 34:1001-6.
Brutsche MH, Joos L, Carlen Brutsche IE, Bissinger R, Tamm M, Custovic A, Woodcock A. Array-based diagnostic gene-expression score for atopy and asthma.J Allergy Clin Immunol 2002;109:271-3.
Fritz SB, Terrell JE, Conner ER, Kukowska-Latallo JF, Baker JR. Nasal mucosal gene expression in patients with allergic rhinitis with and without nasal polyps. J Allergy Clin Immunol 2003;112:1057-63.
Hakonarson H, Halapi E, Whelan R, Gulcher J, Stefansson K, Grunstein MM. Association between IL-1beta/TNF-alpha-induced glucocorticoid-sensitive changes in multiple gene expression and altered responsiveness in airway smooth muscle. Am J Respir Cell Mol Biol 2001;25:761-71.
Karp CL, Grupe A, Schadt E, Ewart SL, Keane-Moore M, Cuomo PJ, Kohl J, Wahl L, Kuperman D, Germer S, Aud D, Peltz G, Wills-Karp M. Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nat Immunol 2000;1:221-6.
Lee JH, Kaminski N, Dolganov G, Grunig G, Koth L, Solomon C, Erle DJ, Sheppard D. Interleukin-13 induces dramatically different transcriptional programs in three human airway cell types. Am J Respir Cell Mol Biol 2001; 25:474-85.
Lund R, Aittokallio T, Nevalainen O, Lahesmaa R. Identification of novel genes regulated by IL-12, IL-4, or TGF-beta during the early polarization of CD4+ lymphocytes. J Immunol 2003; 171:5328-36.
Nomura I, Gao B, Boguniewicz M, Darst MA, Travers JB, Leung DY Distinct patterns of gene expression in the skin lesions of atopic dermatitis and psoriasis: a gene microarray analysis. .J Allergy Clin Immunol 2003;112:1195-202.
Wohlfahrt JG, Kunzmann S, Menz G, Kneist W, Akdis CA, Blaser K, Schmidt-Weber CB. T cell phenotype in allergic asthma and atopic dermatitis. Int Arch Allergy Immunol 2003;131(4):272-82
Zimmermann N, King NE, Laporte J, Yang M, Mishra A, Pope SM, et al. Dissection of experimental asthma with DNA microarray analysis identifies arginase in asthma pathogenesis. J Clin Invest. 2003;111:1863-74.
Original proteomics articles
Selected articles in fields other than allergy
Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM, Mills GB, Simone C, Fishman DA, Kohn EC, Liotta LA. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359:572-7.
Allergy/immunology related articles
Bacarese-Hamilton T, Mezzasoma L, Ingham C, Ardizzoni A, Rossi R, Bistoni F, Crisanti A. Detection of allergen-specific IgE on microarrays by use of signal amplification techniques. Clin Chem. 2002;48:1367-70
Hiller R, Laffer S, Harwanegg C, Huber M, Schmidt WM, Twardosz A, Barletta B, Becker WM, Blaser K, Breiteneder H, Chapman M, Crameri R, Duchene M, Ferreira F, Fiebig H, Hoffmann-Sommergruber K, King TP, Kleber-Janke T, Kurup VP, Lehrer SB, Lidholm J, Muller U, Pini C, Reese G, Scheiner O, Scheynius A, Shen HD, Spitzauer S, Suck R, Swoboda I, Thomas W, Tinghino R, Van Hage-Hamsten M, Virtanen T, Kraft D, Muller MW, Valenta R. Microarrayed allergen molecules: diagnostic gatekeepers for allergy treatment. FASEB J. 2002;16:414-6.
Tam SW, Wiese R, Lee S, Gilmore J, Kumble KD. Simultaneous analysis of eight human Th1/Th2 cytokines using microarrays. J Immunol Methods 2002; 261:157-65.
Bioinformatics
Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density array data based on variance and bias. Bioinformatics 2003; 19:185-193.
Stoeckert CJ Jr, Causton HC, Ball CA. Microarray databases: standards and ontologies. Nat Genet 2002;32 Suppl:469-73.
Kooperberg C, Sipione S, LeBlanc ML, Strand AD, Cattaneo E, Olson JM. Evaluating test-statistics to select interesting genes in microarray experiments. Human Molecular Genetics. 2002; 11:2223-32,
Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet 2002; 31:19-20
Jenssen TK, Laegreid A, Komorowski J, Hovig E.. A literature network of human genes for high-throughput analysis of gene expression. Nat Genet 2001;28:21-8.
Chuaqui RF, Bonner RF, Best CJ, Gillespie JW, Flaig MJ, Hewitt SM, Phillips JL, Krizman DB, Tangrea MA, Ahram M, Linehan WM, Knezevic V, Emmert-Buck MR. Post-analysis follow-up and validation of microarray experiments. Nat Genet 2002; 32 Suppl:509-14
Public databases for microarray experiments
Entrez is a linked system of databases to find information about individual genes and literature references (http://www.ncbi.nlm.nih.gov/). One of the databases, Omnibus, contains freely searchable data from more than 500 microarray experiments (http://www.ncbi.nlm.nih.gov/geo/)
BASE (http://base.thep.lu.se/) aims to provide all the tools needed for data storage, quality control, normalization and statistical application in a web-based application (Lao).
Minimum information about a microarray experiment – MIAME gives standards for microarray experiments are described at (http://www.mged.org/Workgroups/MIAME/miame.html)
The Gene Ontology Consortium and Pubgene and provides tools for automated grouping of functionally related genes (http://www.geneontology.org, http://www.pubgene.org/). GenMAPP may be used to identify activated biological pathways in gene expression data (http://www.genmapp.org/)Last updated: 07 November 2014 -
Link to centers and projects
Current international allergy projects in Functional Genomics and Proteomics
- The EU project ComplexDis (http://www.complexdis.org.gu.se/) aims to develop methods to find markers for personalized medication in complex diseases. Seasonal allergic rhinitis is used as a disease model. Allergen-challenged CD4 + cells are examined with high-throughput techniques and the data subjected to network-based analysis. Candidate diagnostic markers are tested in clinical studies of SAR. The bioinformatic methods will be made freely available on the Internet. The project which has a budget of 1.8 million Euro involves three pediatric allergists from Italy, Hungary and Sweden and experts in genomics, bioinformatics, modeling, computer science and systems biology from Spain, Italy, Belgium, Norway and the US.
- Sens-it-iv, the acronym for “Novel Testing Strategies for In Vitro Assessment of Allergens” is an EU funded consortium within the 6th framework, consisting of 28 European universities, institutes, companies and organizations which aims to design a human cell-based assay to test the propensity of new chemicals and proteins used by the e.g. the cosmetic and pharmaceutical industries to cause allergy (www.sens-it-iv.eu). The test system will be based on in vivo-like human epithelial and DC lines, cultured together to mimic the environment in lung or skin, and will hopefully reduce the need for animal experiments. Mechanisms for allergen recognition and markers involved will be identified with functional genomics, proteomics and metabolomics. All data generated are collected in an inductive database allowing queries for data patterns and predictive models, which will be freely available after project completion.
Please send information about your center to professor Lars Olaf Cardell (Lars-Olaf.Cardell@ki.se).Last updated: 22 September 2014
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