14th ICID - Poster Abstracts - International Society for Infectious ...

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When citing these abstracts please use the following reference: Author(s) of abstract. Title of abstract [abstract]. Int J Infect Dis 2010;14S1: Abstract number. Please note that the official publication of the International Journal of Infectious Diseases 2010, Volume 14, Supplement 1 is available electronically on http://www.sciencedirect.com Final Abstract Number: 84.040 Session: Virology and Viral Infections (Non-HIV) Date: Friday, March 12, 2010 Time: 12:30-13:30 Room: Poster & Exhibition Area/Ground Level Type: Poster Presentation Amplification of early genes of Human Papilloma Virus targeting nine virus genotypes. Mérida, Venezuela E. Michelli 1 , L. Tellez 1 , J. Mendoza 1 , C. Jurgensen 1 , W. Botello 1 , M. Correnti 2 , M. Cavazza 2 , S. Vielma 1 1 Universidad de Los Andes, Merida, Merida, Venezuela, 2 Universidad Central de Venezuela, Caracas, Distrito Capital, Venezuela Background: Subtyping of Human Papilloma Virus (HPV) by molecular biology tools may enhanced cervical cytological assessments information for patient´s management and for prognosis of cervical cancer evolution. The aim of this study was to subtype HPV from cervical samples of sexually active women (15-69yr-old) seeing in cervic-gyn public clinic (IHAULA) of Merida State, Venezuela. Methods: DNA-collection device (Digene®) were used to collect and transport 250 cervical smears, DNA were isolated using QIAamp® DNA Mini Kit (Qiagen®) and storage at -20ºC until used. A Nested-PCR-Multiplex assay was standardized for amplification of early E6/E7 HPV virus. A first run included amplification of a 630bp-length region of E6/E7 gene and a second run allowed to genotype HPV virus in a multiplex format. Amplicons sizes varied from 151-457bp for HPV-16,-18,-31,-45; and HPV-33,-6/11,-58,-52,-56. Simultaneously, HPV detection assays were performed using Hybrid-Capture II (Digene®) technology and an end-point PCR assays for L1 and E6/E7 regions. Results: HPV were detected in 27.2% samples, 94.12% (64/68) were positive for at least one of the genotypes assayed. High risk HPV were identify in 98.44% (63/64) samples; where HPV18 (50/63) was the most common genotype isolated, along with HPV16 (24/63). One or several types were detected in 56.25% (36/64) of the cases, being VPH-18-6/11 (14/64) combination the most common one. Conclusion: we found a high frecuency of cervical infection with high-oncogenic-risk HPV genotypes, specially caused by HPV18, alone or in multiple types infection.
When citing these abstracts please use the following reference: Author(s) of abstract. Title of abstract [abstract]. Int J Infect Dis 2010;14S1: Abstract number. Please note that the official publication of the International Journal of Infectious Diseases 2010, Volume 14, Supplement 1 is available electronically on http://www.sciencedirect.com Final Abstract Number: 84.041 Session: Virology and Viral Infections (Non-HIV) Date: Friday, March 12, 2010 Time: 12:30-13:30 Room: Poster & Exhibition Area/Ground Level Type: Poster Presentation Development of a new genotyping system for predicting TTV genotype using evolutionary restriction map and artificial neural network A. Kenarkoohi 1 , M. Ravanshad 2 , S. Falahi 1 , M. Rasouli 3 1 Department of Virology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran, Islamic Republic of, 2 Department of Virology, Faculty of Medical Sciences, , 14115-331, Iran, Islamic Republic of, 3 Shiraz University of Medical Sciences-Clinical Microbiology Research Center, Shiraz, Iran, Islamic Republic of Background: TT virus is belonging to the family Circoviridae and was discovered in 1997. On the basis of sequence variation in both coding and noncoding regions, many classification systems have been proposed. Several genotyping technique , including DNA sequence analysis as well as primer specific PCR are used. None of the reported systems was able to determine the TTV genotype in all major types and the common subtypes on the basis of the TTV sequence. To date, TTV genotypes have been primarily determined by sequence-based PCR products analysis techniques, including direct sequencing. Here we proposed a new simple method for genotyping based on neural network method and restriction map. Methods: This method classifies genes into groups which are made distinct from each other by evolutionary changes in their restriction site. Our objective was to accurately predict, from complex restriction site patterns, TT virus, by use of artificial intelligence. Neural network models were constructed based on changes at restriction site in the untranslated region of virus. Models were trained, validated, and tested with 330-UTR sequence. A procedure of determining the optimal neural network parameters was proposed to speed up the training processes. Results: The results suggested that the restriction site map is a more accurate predictor of TTV genotype. To show the ability of the model in genotyping, the internal representations developed by the networks are analyzed using principal component analysis (PCA). This analysis shows that the networks are able to discover relevant features on the basis of the association between the restriction map and the virus genotype. Conclusion: The proposed predictor shows vigorous clusterization of both predicted and observed TTV genotype. The current study is designed to find the algorithm for genotype prediction using the restriction site information in combination with artificially created neural network. The applied properties for the model must be mighty and low time consumption. After the pattern recognition model is constructed the algorithm can be used to determine the repertory of restriction site properties required for each genotype. This complex can be defined as genotypic pattern. The pattern can be used for further correlation analysis with known and unknown viral sequence.
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