Bacterial diversity among four healthcare-associated institutes in Taiwan View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Chang-Hua Chen, Yaw-Ling Lin, Kuan-Hsueh Chen, Wen-Pei Chen, Zhao-Feng Chen, Han-Yueh Kuo, Hsueh-Fen Hung, Chuan Yi Tang, Ming-Li Liou

ABSTRACT

Indoor microbial communities have important implications for human health, especially in health-care institutes (HCIs). The factors that determine the diversity and composition of microbiomes in a built environment remain unclear. Herein, we used 16S rRNA amplicon sequencing to investigate the relationships between building attributes and surface bacterial communities among four HCIs located in three buildings. We examined the surface bacterial communities and environmental parameters in the buildings supplied with different ventilation types and compared the results using a Dirichlet multinomial mixture (DMM)-based approach. A total of 203 samples from the four HCIs were analyzed. Four bacterial communities were grouped using the DMM-based approach, which were highly similar to those in the 4 HCIs. The α-diversity and β-diversity in the naturally ventilated building were different from the conditioner-ventilated building. The bacterial source composition varied across each building. Nine genera were found as the core microbiota shared by all the areas, of which Acinetobacter, Enterobacter, Pseudomonas, and Staphylococcus are regarded as healthcare-associated pathogens (HAPs). The observed relationship between environmental parameters such as core microbiota and surface bacterial diversity suggests that we might manage indoor environments by creating new sanitation protocols, adjusting the ventilation design, and further understanding the transmission routes of HAPs. More... »

PAGES

8230

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-017-08679-3

    DOI

    http://dx.doi.org/10.1038/s41598-017-08679-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1091119582

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28811583


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