88 research outputs found

    Microbiological and Molecular Characterization of Staphylococcus hominis Isolates from Blood

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    Background: Among Coagulase-Negative Staphylococci (CoNS), Staphylococcus hominis represents the third most common organism recoverable from the blood of immunocompromised patients. The aim of this study was to characterize biofilm formation, antibiotic resistance, define the SCCmec (Staphylococcal Chromosomal Cassette mec) type, and genetic relatedness of clinical S. hominis isolates. Methodology: S. hominis blood isolates (n = 21) were screened for biofilm formation using crystal violet staining. Methicillin resistance was evaluated using the cefoxitin disk test and the mecA gene was detected by PCR. Antibiotic resistance was determined by the broth microdilution method. Genetic relatedness was determined by pulsed-field gel electrophoresis (PFGE) and SCCmec typed by multiplex PCR using two different methodologies described for Staphylococcus aureus. Results: Of the S. hominis isolates screened, 47.6% (10/21) were categorized as strong biofilm producers and 23.8% (5/21) as weak producers. Furthermore, 81% (17/21) of the isolates were methicillin resistant and mecA gene carriers. Resistance to ampicillin, erythromycin, and trimethoprim was observed in .70% of isolates screened. Each isolate showed a different PFGE macrorestriction pattern with similarity ranging between 0–95%. Among mecA-positive isolates, 14 (82%) harbored a non-typeable SCCmec type: eight isolates were not positive for any ccr complex; four contained the mec complex A ccrAB1 and ccrC, one isolate contained mec complex A, ccrAB4 and ccrC, and one isolate contained the mec complex A, ccrAB1, ccrAB4, and ccrC. Two isolates harbored the association: mec complex A and ccrAB1. Only one strain was typeable as SCCmec III. Conclusions: The S. hominis isolates analyzed were variable biofilm producers had a high prevalence of methicillin resistance and resistance to other antibiotics, and high genetic diversity. The results of this study strongly suggested that S. hominis isolates harbor new SCCmec structural elements and might be reservoirs of ccrC1 in addition to ccrAB1 and mec complex A

    Antibiotic Susceptibility of Biofilm Cells and Molecular Characterisation of Staphylococcus hominis Isolates from Blood

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    Objectives We aimed to characterise the staphylococcal cassette chromosome mec (SCCmec) type, genetic relatedness, biofilm formation and composition, icaADBC genes detection, icaD expression, and antibiotic susceptibility of planktonic and biofilm cells of Staphylococcus hominis isolates from blood. Methods The study included 67 S. hominis blood isolates. Methicillin resistance was evaluated with the cefoxitin disk test. mecA gene and SCCmec were detected by multiplex PCR. Genetic relatedness was determined by pulsed-field gel electrophoresis. Biofilm formation and composition were evaluated by staining with crystal violet and by detachment assay, respectively; and the biofilm index (BI) was determined. Detection and expression of icaADB Cgenes were performed by multiplex PCR and real-time PCR, respectively. Antibiotic susceptibilities of planktonic cells (minimum inhibitory concentration, MIC) and biofilm cells (minimum biofilm eradication concentration, MBEC) were determined by the broth dilution method. Results Eighty-five percent (57/67) of isolates were methicillin resistant and mecA positive. Of the mecA-positive isolates, 66.7% (38/57) carried a new putative SCCmec type. Four clones were detected, with two to five isolates each. Among all isolates, 91% (61/67) were categorised as strong biofilm producers. Biofilm biomass composition was heterogeneous (polysaccharides, proteins and DNA). All isolates presented the icaD gene, and 6.66% (1/15) isolates expressed icaD. This isolate presented the five genes of ica operon. Higher BI and MBEC values than the MIC values were observed for amikacin, vancomycin, linezolid, oxacillin, ciprofloxacin, and chloramphenicol. Conclusions S. hominis isolates were highly resistant to methicillin and other antimicrobials. Most of the detected SCCmec types were different than those described for S. aureus. Isolates indicated low clonality. The results indicate that S. hominis is a strong biofilm producer with an extracellular matrix with similar composition of proteins, DNA and N-acetylglucosamine; and presents high frequency and low expression of icaD gene. Biofilm production is associated with increased antibiotic resistance

    Practical application of brief cognitive tests

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    RESUMEN Introducción.- Los test cognitivos breves (TCB) pueden ayudar a detectar el deterioro cognitivo (DC) en el ámbito asistencial. Se han desarrollado y/o validado varios TCB en nuestro país, pero no existen recomendaciones específicas para su uso. Desarrollo.- Revisión de estudios de rendimiento diagnóstico llevados a cabo en España con TCB que requieran menos de 20 minutos. Recomendaciones de uso consensuadas por expertos sobre la base de las características de los TCB y de los estudios disponibles. Conclusión.- El Fototest, el Memory Impairment Screen (MIS) y el Mini-Mental State Examination (MMSE) son las opciones más recomendables para el primer nivel asistencial, pudiendo añadirse otros test (Test del Reloj [TR], test de fluidez verbal [TFV]) en caso de resultado negativo y queja o sospecha persistente (aproximación escalonada). En el segundo nivel asistencial es conveniente una evaluación sistemática de las distintas áreas cognitivas, que puede llevarse a cabo con instrumentos como el Montreal Cognitive Assessment, MMSE, Rowland Universal Dementia Assessment o Addenbrooke's Cognitive Examination, o bien mediante el uso escalonado o combinado de herramientas más simples (TR, TFV, Fototest, MIS, Test de Alteración de la Memoria, Eurotest). El uso asociado de cuestionarios cumplimentados por un informador (CCI) aporta valor añadido a los TCB en la detección del DC. La elección de los instrumentos vendrá condicionada por las características del paciente, la experiencia del clínico y el tiempo disponible. Los TCB y CCI deben reforzar -pero nunca suplantar- el juicio clínico, la comunicación con el paciente y el diálogo interprofesional

    Effectiveness and cost-effectiveness of an internet intervention for family caregivers of people with dementia: design of a randomized controlled trial

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    <p>Abstract</p> <p>Background</p> <p>The number of people with dementia is rising rapidly as a consequence of the greying of the world population. There is an urgent need to develop cost effective approaches that meet the needs of people with dementia and their family caregivers. Depression, feelings of burden and caregiver stress are common and serious health problems in these family caregivers. Different kinds of interventions are developed to prevent or reduce the negative psychological consequences of caregiving. The use of internet interventions is still very limited, although they may be a cost effective way to support family caregivers in an earlier stage and diminish their psychological distress in the short and longer run.</p> <p>Methods/design</p> <p>A pragmatic randomized controlled trial is designed to evaluate the effectiveness and cost-effectiveness of ‘Mastery over Dementia’, an internet intervention for caregivers of people with dementia. The intervention aims at prevention and decrease of psychological distress, in particular depressive symptoms. The experimental condition consists of an internet course with 8 sessions and a booster session over a maximum period of 6 months guided by a psychologist. Caregivers in the comparison condition receive a minimal intervention. In addition to a pre and post measurement, an intermediate measurement will be conducted. In addition, there will be two follow-up measurements 3 and 6 months after post-treatment in the experimental group only. To study the effectiveness of the intervention, depressive symptoms are used as the primary outcome, whereas symptoms of anxiety, role overload and caregiver perceived stress are used as secondary outcomes. To study which caregivers profit most of the internet intervention, several variables that may modify the impact of the intervention are taken into account. Regarding the cost-effectiveness, an economic evaluation will be conducted from a societal perspective.</p> <p>Discussion</p> <p>This study will provide evidence about the effectiveness and cost-effectiveness of an internet intervention for caregivers. If both can be shown, this might set the stage for the development of a range of internet interventions in the field of caregiving for people with dementia. This is even more important because future generations of caregivers will be more familiar with the use of internet.</p> <p>Trial registration</p> <p>NTR-2051/RCT-DDB</p

    Who leads research productivity growth? Guidelines for R&D policy-makers

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    [EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). Public financing of cooperative R&D projects in Spain: the concerted projects under the national R&D plan. Research Policy, 30, 625–641.Arbel, A. (1981). Policy evaluation in the dynamic input–output model. International Journal of Systems Science, 12, 255–260.Arnold, E. (2004). Evaluation research and innovation policy: A systems world needs systems evaluations. Research Evaluation, 13, 3–17.Arrow, J. K. (1962). Economic welfare and the allocation of resources for inventions. In R. Nelson (Ed.), The rate and direction of inventive activity: Economic and social factor (pp. 609–625). Princeton: Princeton University Press and NBER.Autio, E. (1997). New, technology-based firms in innovation networks symplectic and generative impacts. Research Policy, 26, 263–281.Balk, B. (2001). Scale efficiency and productivity change. Journal of Productivity Analysis, 15, 153–183.Balzat, M., & Hanusch, H. (2004). Recent trends in the research on national innovation systems. Journal of Evolutionary Economics, 14, 197–210.Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking. Scandinavian Journal of Economics, 94, S211–S228.Bergek, A., Carlsson, B., Lindmark, S., Rickne, A., & Jacobsson, S. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37, 407–429.Bonaccorsi, A., & Daraio, C. (2005). Exploring size and agglomeration effects on public research productivity. Scientometrics, 63(1), 87–120.Buisseret, T. J., Cameron, H., & Georghiou, L. (1995). What difference does it make? Additionality in the public support of R&D in large firms. International Journal of Technology Management, 10, 587–600.Bustelo, M. (2006). The potential role of standards and guidelines in the development of an evaluation culture in Spain. Evaluation, 12, 437–453.Chavas, J. P., & Cox, T. M. (1999). A generalized distance function and the analysis of production efficiency. Southern Economic Journal, 66, 295–318.CICYT. (1987). Programa Nacional de Tecnología de los Alimentos. Madrid: Ministerio de Educación y Ciencia.CICYT (1988). Plan Nacional de Investigación Científica y Desarrollo Tecnológico 1988–1991. Ministerio de Educación y Ciencia, Secretaría de Estado de Universidades e Investigación, Madrid.Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-software. Boston: Kluwer Academic Publishers.David, P., Mowery, D., & Steinmueller, W. E. (1994). Analyzing the economic payoffs from basic research. In D. Mowery (Ed.), Science and technology policy in interdependent economies (pp. 57–78). Boston: Kluwer Academic Publishers.Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263–279.Edquist, C., & Hommen, L. (2008). Comparing national systems of innovation in Asia and Europe: Theory and comparative framework. In C. Edquist & L. Hommen (Eds.), Small country innovation systems: Globalisation, change and policy in Asia and Europe (pp. 1–28). Cheltenham: Edward Elgar.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84, 66–83.Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General, 120(3), 253–281.Førsund, F. R. (1993). Productivity growth in Norwegian ferries. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 352–373). New York: Oxford University Press.Førsund, F. R. (1997). The Malmquist productivity index, TFP and scale. University of Oslo, Oslo: Working Paper, Department of Economics and Business Administration.Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. London: Printer Publishers.García-Martínez, M., & Briz, J. (2000). Innovation in the Spanish food & drink industry. International Food and Agribusiness Management Review, 3, 155–176.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage Publications.Grammatikopoulos, V., Kousteiios, A., Tsigilis, N., & Theodorakis, Y. (2004). Applying dynamic evaluation approach in education. Studies in Educational Evaluation, 30, 255–263.Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81–101.Grimpe, C., & Sofka, W. (2007). Search patterns and absorptive capacity: A comparison of low- and high-technology firms from thirteen European countries. Discussion paper no. 07-062. Centre for European Economic Research (ZEW), Mannheim, Germany.Guan, J., & Wang, J. (2004). Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59(1), 131–155.Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.Jiménez-Sáez, F. (2005). Una Evaluación del Programa Nacional de Tecnología de Alimentos: análisis de la articulación fomentada sobre el Sistema Alimentario de Innovación en España. PhD dissertation, Servicio de Publicaciones de la Universidad Politécnica de Valencia, Valencia.Jiménez-Sáez, F., Zabala-Iturriagagoitia, J. M., Zofío, J. L., & Castro-Martínez, E. (2011). Evaluating research efficiency within National R&D Programmes. Research Policy, 40, 230–241.Kao, C. (2008). Efficiency analysis of university departments: An empirical study. OMEGA, 36, 653–664.Kuhlmann, S. (2003). Evaluation of research and innovation policies: A discussion of trends with examples from Germany. International Journal of Technology Management, 26, 131–149.Laitinen, E. K. (2002). A dynamic performance measurement system: Evidence from small Finnish technology companies. Scandinavian Journal of Management, 18, 65–99.Laranja, M., Uyarra, E., & Flanagan, K. (2008). Policies for science, technology and innovation: Translating rationales into regional policies in a multi-level setting. Research Policy, 37(5), 823–835.Lee, T.-L., & von Tunzelman, N. (2005). A dynamic analytic approach to national innovation systems: The IC industry in Taiwan. Research Policy, 34, 425–440.Lipsey, R., & Carlaw, K. (1998). A structuralist assessment of technology policies: Taking Schumpeter seriously on policy. Ottawa: Industry Canada Research Publications Program.Lipsey, R., Carlaw, K., & Bekar, C. (2005). Economic transformations: General purpose technologies and long term economic growth. Oxford: Oxford University Press.Lundvall, B. Å. (1992). National systems of innovation: Toward a theory of innovation and interactive learning. London: Printer Publishers.Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31, 213–231.Markard, J., & Truffer, B. (2008). Actor-oriented analysis of innovation systems: Exploring micro-meso level linkages in the case of stationary fuel cells. Technology Analysis & Strategic Management, 20, 443–464.Metcalfe, J. S. (2002). Equilibrium and evolutionary foundations of competition and technology policy: New perspectives on the division of labour and the innovation process. CRIC Working Papers series, University of Manchester.Miettinen, R. (1999). The riddle of things. Activity theory and actor network theory as approaches of studying innovations. Mind, Culture and Activity, 6, 170–195.Molas-Gallart, J., & Davies, A. (2006). Toward theory-led evaluation: The experience of European science, technology, and innovation policies. American Journal of Evaluation, 27, 64–82.Mytelka, L. K., & Smith, K. (2002). Policy learning and innovation theory: An interactive and co-evolving process. Research Policy, 31, 1467–1479.Olazarán, M., Lavía, C., & Otero, B. (2004). ¿Hacia una segunda transición en la ciencia? Política científica y grupos de investigación. Revista Española de Sociología, 4, 143–172.Potts, J. (2007). The innovation system & economic evolution. Productivity commission submission, public support for science & innovation, productivity commission, Camberra.Ray, S., & Desli, E. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. American Economic Review, 87(5), 1033–1039.Rip, A., & Nederhof, A. J. (1986). Between dirigism and laissez-faire: Effects of implementing the science policy priority for biotechnology in the Netherlands. Research Policy, 15, 253–268.Schmidt, E. K., Graversen, E. K., & Langberg, K. (2003). Innovation and dynamics in public research environments in Denmark: A research-policy perspective. Science and Public Policy, 30, 107–116.Schmoch, U., & Schubert, T. (2009). Sustainability of incentives for excellent research—The German case. Scientometrics, 81(1), 195–218.Shephard, R. (1970). Theory of cost and production functions. New Jersey: Princeton University Press.Simar, L., & Wilson, P. W. (1998). Productivity growth in industrialized countries. Discussion paper 9810, Universite Catholique de Louvain, Belgium.Van Raan, A. F. J. (2000). R&D evaluation at the beginning of the new century. Research Evaluation, 8, 81–86.Zofio, J. L. (2007). Malmquist productivity index decompositions: A unifying framework. Applied Economics, 39, 2371–2387.Zofio, J. L., & Lovell, C. A. K. (1998). Yet another Malmquist productivity index decomposition. Working paper, Department of Economics, University of Georgia, Athens, GA 30602, USA.Zofio, J. L., & Lovell, C. A. K. (2001). Graph efficiency and productivity measures: An application to US agriculture. Applied Economics, 33(10), 1433–1442.Zofio, J. L., & Prieto, A. M. (2006). Return to dollar, generalized distance function and the Fisher productivity index. Spanish Economic Review, 8, 113–138

    Innovation Practices in Emerging Economies: Do University Partnerships Matter?

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    Enterprises’ resources and capabilities determine their ability to achieve competitive advantage. In this regard, the key innovation challenges that enterprises face are liabilities associated with their age and size, and the entry barriers imposed on them. In this line, a growing number of enterprises are starting to implement innovation practices in which they employ both internal/external flows of knowledge in order to explore/exploit innovation in collaboration with commercial or scientific agents. Within this context, universities play a significant role providing fertile knowledge-intensive environments to support the exploration and exploitation of innovative and entrepreneurial ideas, especially in emerging economies, where governments have created subsidies to promote enterprise innovation through compulsory university partnerships. Based on these ideas, the purpose of this exploratory research is to provide a better understanding about the role of universities on enterprises’ innovation practices in emerging economies. More concretely, in the context of Mexico, we explored the enterprises’ motivations to collaborate with universities in terms of innovation purposes (exploration and exploitation) or alternatives to access to public funds (compulsory requirement of being involved in a university partnership). Using a sample of 10,167 Mexican enterprises in the 2012 Research and Technological Development Survey collected by the Mexican National Institute of Statistics and Geography, we tested a multinomial regression model. Our results provide insights about the relevant role of universities inside enterprises’ exploratory innovation practices, as well as, in the access of R&D research subsidies

    Aplicación práctica de los test cognitivos breves

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    Introducción.- Los test cognitivos breves (TCB) pueden ayudar a detectar el deterioro cognitivo (DC) en el ámbito asistencial. Se han desarrollado y/o validado varios TCB en nuestro país, pero no existen recomendaciones específicas para su uso. Desarrollo.- Revisión de estudios de rendimiento diagnóstico llevados a cabo en España con TCB que requieran menos de 20 minutos. Recomendaciones de uso consensuadas por expertos sobre la base de las características de los TCB y de los estudios disponibles. Conclusión.- El Fototest, el Memory Impairment Screen (MIS) y el Mini-Mental State Examination (MMSE) son las opciones más recomendables para el primer nivel asistencial, pudiendo añadirse otros test (Test del Reloj [TR], test de fluidez verbal [TFV]) en caso de resultado negativo y queja o sospecha persistente (aproximación escalonada). En el segundo nivel asistencial es conveniente una evaluación sistemática de las distintas áreas cognitivas, que puede llevarse a cabo con instrumentos como el Montreal Cognitive Assessment, MMSE, Rowland Universal Dementia Assessment o Addenbrooke's Cognitive Examination, o bien mediante el uso escalonado o combinado de herramientas más simples (TR, TFV, Fototest, MIS, Test de Alteración de la Memoria, Eurotest). El uso asociado de cuestionarios cumplimentados por un informador (CCI) aporta valor añadido a los TCB en la detección del DC. La elección de los instrumentos vendrá condicionada por las características del paciente, la experiencia del clínico y el tiempo disponible. Los TCB y CCI deben reforzar -pero nunca suplantar- el juicio clínico, la comunicación con el paciente y el diálogo interprofesional

    Sexually transmitted pathogens, coinfections and risk factors in patients attending obstetrics and gynecology clinics in Jalisco, Mexico

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    Objetivo. Determinar la frecuencia de nueve patógenos de transmisión sexual, coinfecciones y factores de riesgo en pacientes que acudieron a una consulta de ginecología y obstetricia en Jalisco, México. Material y métodos. Se analizaron muestras de 662 pacientes que asistieron a la consulta de ginecología y obstetricia. Se detectaron Treponema pallidum, VIH y VHC mediante serología. Se detectó VPH por Reacción de Cadena de Polimerasa (PCR) y sus genotipos se detectaron por Polimorfismos de Longitud de Fragmentos de Restricción (RFLP). Se detectaron Trichomonas vaginalis, VHS-1, VHS-2, Mycoplasma genitalium, Neisseria gonorrhoeae y T. pallidum por PCR múltiple. Resultados. Por serología, la frecuencia de VIH fue 6.8%, de T. pallidum fue 2.26% y de VHC fue 0.15%. Por PCR, la frecuencia más alta fue de VPH (13.9%, el genotipo más frecuente fue el 16, 33.7%), seguida de T. vaginalis (14.2%), VHS-1 (8.5%), M. genitalium (2.41%), N. gonorrhoeae (2.11%), VHS-2 (1.8%) y T. pallidum (1.05%). Los pacientes infectados con T. vaginalis presentaron más probabilidades de tener múltiples coinfecciones (p = 0.01). Conclusiones. La frecuencia de infección por VPH, VHS-1, VHS-2, M. genitalium y T. vaginalis fue menor a lo reportado. Sin embargo, se detectó una alta frecuencia de VIH, T. pallidum, y N. gonorrhoeae. ABSTRACT Objective. To determine the frequency of nine sexually transmitted pathogens, coinfections and risk factors in patients attending obstetrics and gynecology clinics in Jalisco, Mexico. Materials and methods. Samples from 662 patients attending obstetrics and gynecology clinics were analyzed. Treponema pallidum, HIV, and HCV were detected by serology. HPV was detected by Polimerase Chain Reac- tion (PCR), and its genotype was determined by Restriction Fragment Length Polymorphism (RFLP). Trichomonas vaginalis, HSV-1, HSV-2, Mycoplasma genitalium, Neisseria gonorrhoeae and T. pallidum were detected by multiplex PCR. Results. By serology, HIV frequency was 6.8%, T. pallidum was 2.26%, and HCV was 0.15%. By PCR, HPV frequency was 13.9%, (more frequent genotype was 16, 33.7%), followed by T. vaginalis (14.2%), HSV-1 (8.5%), M. genitalium (2,41%), N. gonorrhoeae (2.11%), HSV-2 (1.8%), and T. pallidum (1.05%). Patients infected with T. vaginalis were more likely to have multiple coinfections (p = 0.01). Conclusion. The frequency of HPV, HVS-1, HSV-2, M. genitalium and T. vaginalis was lower than that reported. However, a high frequency of HIV, T. pallidum, and N. gonorrhoeae was detected
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