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Microbial Profiles and Detection Techniques in Peri-Implant Diseases: a Systematic Review

OBJECTIVES: To describe the microbial profiles of peri-implant diseases and the main detection methods. MATERIAL AND METHODS: A literature search was performed in MEDLINE via PubMed database to identify studies on microbial composition of peri-implant surfaces in humans published in the last 5 years...

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Detalles Bibliográficos
Autores principales: Padial-Molina, Miguel, López-Martínez, Jesús, O’Valle, Francisco, Galindo-Moreno, Pablo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Stilus Optimus 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5100635/
https://www.ncbi.nlm.nih.gov/pubmed/27833735
http://dx.doi.org/10.5037/jomr.2016.7310
Descripción
Sumario:OBJECTIVES: To describe the microbial profiles of peri-implant diseases and the main detection methods. MATERIAL AND METHODS: A literature search was performed in MEDLINE via PubMed database to identify studies on microbial composition of peri-implant surfaces in humans published in the last 5 years. Studies had to have clear implant status definition for health, peri-implant mucositis and/or peri-implantitis and specifically study microbial composition of the peri-implant sulcus. RESULTS: A total of 194 studies were screened and 47 included. Peri-implant sites are reported to be different microbial ecosystems compared to periodontal sites. However, differences between periodontal and peri-implant health and disease are not consistent across all studies, possibly due to the bias introduced by the microbial detection technique. New methods non species-oriented are being used to find ‘unexpected’ microbiota not previously described in these scenarios. CONCLUSIONS: Microbial profile of peri-implant diseases usually includes classic periodontopathogens. However, correlation between studies is difficult, particularly because of the use of different detection methods. New metagenomic techniques should be promoted for future studies to avoid detection bias.