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Antibacterial Activity of Commercial Dentine Bonding Systems against E. faecalis-Flow Cytometry Study.

ABSTRACT: Literature presents inconsistent results on the antibacterial activity of dentine bonding systems (DBS). Antibacterial activity of adhesive systems depends on several factors, including composition and acidity. Flow cytometry is a novel detection method to measure multiple characteristics of a single cell: total cell number, structural (size, shape), and functional parameters (viability, cell cycle). The LIVE/DEAD® BacLightTM bacterial viability assay was used to evaluate an antibacterial activity of DBS by assessing physical membrane disruption of bacteria mediated by DBS. Ten commercial DBSs: four total-etching (TE), four self-etching (SE) and two selective enamel etching (SEE) were tested. Both total-etching DBS ExciTE F and OptiBond Solo Plus showed comparatively low antibacterial activity against E. faecalis. The lowest activity of all tested TE systems showed Te-Econom Bond. Among SE DBS, G-ænial Bond (92.24% dead cells) followed by Clearfil S3 Bond Plus (88.02%) and Panavia F 2.0 ED Primer II (86.67%) showed the highest antibacterial activity against E. faecalis, which was comparable to isopropranol (positive control). In the present study, self-etching DBS exhibited higher antimicrobial activity than tested total-etching adhesives against E. faecalis.

SUBMITTER: Lukomska-Szymanska M

PROVIDER: S-EPMC5458997 | biostudies-other | 2017 Apr

REPOSITORIES: biostudies-other

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Project description:Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

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Antibacterial Activity of Commercial Dentine Bonding Systems against E. faecalis-Flow Cytometry Study.

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