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A quantitative method to measure biofilm removal efficiency from complex biomaterial surfaces using SEM and image analysis.

ABSTRACT: Biofilm accumulation on biomaterial surfaces is a major health concern and significant research efforts are directed towards producing biofilm resistant surfaces and developing biofilm removal techniques. To accurately evaluate biofilm growth and disruption on surfaces, accurate methods which give quantitative information on biofilm area are needed, as current methods are indirect and inaccurate. We demonstrate the use of machine learning algorithms to segment biofilm from scanning electron microscopy images. A case study showing disruption of biofilm from rough dental implant surfaces using cavitation bubbles from an ultrasonic scaler is used to validate the imaging and analysis protocol developed. Streptococcus mutans biofilm was disrupted from sandblasted, acid etched (SLA) Ti discs and polished Ti discs. Significant biofilm removal occurred due to cavitation from ultrasonic scaling (p?

SUBMITTER: Vyas N

PROVIDER: S-EPMC5013386 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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A quantitative method to measure biofilm removal efficiency from complex biomaterial surfaces using SEM and image analysis.

Vyas N N Sammons R L RL Addison O O Dehghani H H Walmsley A D AD

Scientific reports 20160907

Biofilm accumulation on biomaterial surfaces is a major health concern and significant research efforts are directed towards producing biofilm resistant surfaces and developing biofilm removal techniques. To accurately evaluate biofilm growth and disruption on surfaces, accurate methods which give quantitative information on biofilm area are needed, as current methods are indirect and inaccurate. We demonstrate the use of machine learning algorithms to segment biofilm from scanning electron micr ...[more]

PMID: 27601281

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Project description:Over the last decades, scanning electron microscopy (SEM) proved to be invaluable for ultrastructural investigation, allowing imaging of the overall appearance and/or specific features of oral biofilms, e.g., microbial colonies and individual cells, glycocalyx, the presence of inorganic products. The aim of this study was the observation and evaluation of the morphology of the biofilm of endodontic-periodontal lesions (EPL) with a modified protocol involving a simplified histologic sample preparation and a low-vacuum SEM examination method. Twenty-one teeth with endodontic-periodontal involvement, extracted for periodontal reasons, were carefully washed with saline, underwent fixation in modified Karnovsky solution and were dehydrated in alcohol series. Samples were examined under low-vacuum SEM. Radicular surfaces were evaluated qualitatively and semiquantitatively for several characteristics, including the presence of bacterial types, the biofilm morphology and the content of root resorptions. Radicular surfaces were divided in four conventional zones Surfaces were evaluated for several characteristics: Presence of bacterial types, biofilm morphology, presence of root resorptions. High-quality images, relevant for endodontic-periodontal biofilms were collected. Continuous, established biofilm was found on all examined surfaces, its detection varying from 19% of the samples on the wall of cemental cone to 52.3% on the radicular surface of the periodontal pocket. Observed microorganisms included cocci, rods an filaments. Spirils and motile bacteria were only accidentally found. SEM investigation of surfaces involved in EPL revealed less surfaces covered by mature biofilm (in only 28.5% of the samples in the 'transition zone'), especially rods and filaments associated with cemental resorptions and calculus. Biofilm elements were better represented in periodontal pockets than in other zones of EPL (detected in up to 81% of the samples). A strong correlation between mature biofilm and the presence of cocci appears on all investigated zones (P<0.01). Microbiota appeared to be morphologically similar in apical and periodontal areas, especially in old EPL.

Dataset Information

A quantitative method to measure biofilm removal efficiency from complex biomaterial surfaces using SEM and image analysis.

Publications

A quantitative method to measure biofilm removal efficiency from complex biomaterial surfaces using SEM and image analysis.

Similar Datasets

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