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This study aims to assess contamination with Legionella spp. in water from dental chair units (DCUs) of a hospital dental ward and to perform its molecular characterization by whole-genome sequencing (WGS). We collect eight water samples (250 mL) from four DCUs (sink and water-syringe). Samples are tested for the presence of Legionella spp. (CFUs/mL) by culturing according to the Nederland Norm (NEN) 6265. Three DCUs are found positive for Legionella anisa, and four isolates are cultured (sink n = 2, water-syringe n = 1; two isolates from the same chair) with 1 × 102 CFU/mL. Whole-genome multi-locus sequence typing (wgMLST) results indicate that all strains belong to the same cluster with two to four allele differences. Classical culture combined with WGS allows the identification of a unique clone of L. anisa in several DCUs in the same hospital dental ward. This may indicate a common contamination source in the dental unit waterlines, which was fixed by replacing the chairs and main pipeline of the unit. Our results reveal tap water contamination in direct contact with patients and the usefulness of WGS to investigate bacterial molecular epidemiology.

Mammographic breast density has been found to be a strong risk factor for breast cancer. In most studies, it is assessed with a user-assisted threshold method, which is time consuming and subjective. In this study, we develop a breast density segmentation method that is fully automatic. The method is based on pixel classification in which different approaches known in the literature to segment breast density are integrated and extended. In addition, the method incorporates the knowledge of a trained observer, by using segmentations obtained by the user-assisted threshold method as training data. The method is trained and tested using 1300 digitized film mammographic images acquired with a variety of systems. Results show a high correspondence between the automated method and the user-assisted threshold method. Pearson's correlation coefficient between our method and the user-assisted method is R = 0.911 for percent density and R = 0.895 for dense area, which is substantially higher than the best correlation found in the literature (R = 0.70, R = 0.68). The area under the receiver operating characteristic curve obtained when discriminating between fatty and dense pixels is 0.987. A combination of segmentation strategies outperforms the application of single segmentation techniques. [ABSTRACT FROM AUTHOR]