Cargando…
The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study
BACKGROUND: Understanding sources of microbial contamination in outpatient rehabilitation (REHAB) clinics is important to patients and healthcare providers. PURPOSE: The purpose of this study was to characterize the microbiome of an outpatient REHAB clinic and examine relationships between clinic fa...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159339/ https://www.ncbi.nlm.nih.gov/pubmed/37141300 http://dx.doi.org/10.1371/journal.pone.0281299 |
_version_ | 1785037119984500736 |
---|---|
author | Brigando, Gabriella Sutton, Casey Uebelhor, Olivia Pitsoulakis, Nicholas Pytynia, Matthew Dillon, Thomas Elliott-Burke, Teresa Hubert, Nathaniel Martinez-Guryn, Kristina Bolch, Charlotte Ciancio, Mae J. Evans, Christian C. |
author_facet | Brigando, Gabriella Sutton, Casey Uebelhor, Olivia Pitsoulakis, Nicholas Pytynia, Matthew Dillon, Thomas Elliott-Burke, Teresa Hubert, Nathaniel Martinez-Guryn, Kristina Bolch, Charlotte Ciancio, Mae J. Evans, Christian C. |
author_sort | Brigando, Gabriella |
collection | PubMed |
description | BACKGROUND: Understanding sources of microbial contamination in outpatient rehabilitation (REHAB) clinics is important to patients and healthcare providers. PURPOSE: The purpose of this study was to characterize the microbiome of an outpatient REHAB clinic and examine relationships between clinic factors and contamination. METHODS: Forty commonly contacted surfaces in an outpatient REHAB clinic were observed for frequency of contact and swiped using environmental sample collection kits. Surfaces were categorized based on frequency of contact and cleaning and surface type. Total bacterial and fungal load was assessed using primer sets specific for the 16S rRNA and ITS genes, respectively. Bacterial samples were sequenced using the Illumina system and analyzed using Illumina-utils, Minimum Entropy Decomposition, QIIME2 (for alpha and beta diversity), LEfSe and ANCOM-BC for taxonomic differential abundance and ADONIS to test for differences in beta diversity (p<0.05). RESULTS: Porous surfaces had more bacterial DNA compared to non-porous surfaces (median non-porous = 0.0016ng/μL, 95%CI = 0.0077–0.00024ng/μL, N = 15; porous = 0.0084 ng/μL, 95%CI = 0.0046–0.019 ng/μL, N = 18. p = 0.0066,DNA. Samples clustered by type of surface with non-porous surfaces further differentiated by those contacted by hand versus foot. ADONIS two-way ANOVA showed that the interaction of porosity and contact frequency (but neither alone) had a significant effect on 16S communities (F = 1.7234, R(2) = 0.0609, p = 0.032). DISCUSSION: Porosity of surfaces and the way they are contacted may play an underestimated, but important role in microbial contamination. Additional research involving a broader range of clinics is required to confirm results. Results suggest that surface and contact-specific cleaning and hygiene measures may be needed for optimal sanitization in outpatient REHAB clinics. |
format | Online Article Text |
id | pubmed-10159339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101593392023-05-05 The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study Brigando, Gabriella Sutton, Casey Uebelhor, Olivia Pitsoulakis, Nicholas Pytynia, Matthew Dillon, Thomas Elliott-Burke, Teresa Hubert, Nathaniel Martinez-Guryn, Kristina Bolch, Charlotte Ciancio, Mae J. Evans, Christian C. PLoS One Research Article BACKGROUND: Understanding sources of microbial contamination in outpatient rehabilitation (REHAB) clinics is important to patients and healthcare providers. PURPOSE: The purpose of this study was to characterize the microbiome of an outpatient REHAB clinic and examine relationships between clinic factors and contamination. METHODS: Forty commonly contacted surfaces in an outpatient REHAB clinic were observed for frequency of contact and swiped using environmental sample collection kits. Surfaces were categorized based on frequency of contact and cleaning and surface type. Total bacterial and fungal load was assessed using primer sets specific for the 16S rRNA and ITS genes, respectively. Bacterial samples were sequenced using the Illumina system and analyzed using Illumina-utils, Minimum Entropy Decomposition, QIIME2 (for alpha and beta diversity), LEfSe and ANCOM-BC for taxonomic differential abundance and ADONIS to test for differences in beta diversity (p<0.05). RESULTS: Porous surfaces had more bacterial DNA compared to non-porous surfaces (median non-porous = 0.0016ng/μL, 95%CI = 0.0077–0.00024ng/μL, N = 15; porous = 0.0084 ng/μL, 95%CI = 0.0046–0.019 ng/μL, N = 18. p = 0.0066,DNA. Samples clustered by type of surface with non-porous surfaces further differentiated by those contacted by hand versus foot. ADONIS two-way ANOVA showed that the interaction of porosity and contact frequency (but neither alone) had a significant effect on 16S communities (F = 1.7234, R(2) = 0.0609, p = 0.032). DISCUSSION: Porosity of surfaces and the way they are contacted may play an underestimated, but important role in microbial contamination. Additional research involving a broader range of clinics is required to confirm results. Results suggest that surface and contact-specific cleaning and hygiene measures may be needed for optimal sanitization in outpatient REHAB clinics. Public Library of Science 2023-05-04 /pmc/articles/PMC10159339/ /pubmed/37141300 http://dx.doi.org/10.1371/journal.pone.0281299 Text en © 2023 Brigando et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Brigando, Gabriella Sutton, Casey Uebelhor, Olivia Pitsoulakis, Nicholas Pytynia, Matthew Dillon, Thomas Elliott-Burke, Teresa Hubert, Nathaniel Martinez-Guryn, Kristina Bolch, Charlotte Ciancio, Mae J. Evans, Christian C. The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study |
title | The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study |
title_full | The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study |
title_fullStr | The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study |
title_full_unstemmed | The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study |
title_short | The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study |
title_sort | microbiome of an outpatient rehabilitation clinic and predictors of contamination: a pilot study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159339/ https://www.ncbi.nlm.nih.gov/pubmed/37141300 http://dx.doi.org/10.1371/journal.pone.0281299 |
work_keys_str_mv | AT brigandogabriella themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT suttoncasey themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT uebelhorolivia themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT pitsoulakisnicholas themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT pytyniamatthew themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT dillonthomas themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT elliottburketeresa themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT hubertnathaniel themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT martinezgurynkristina themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT bolchcharlotte themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT cianciomaej themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT evanschristianc themicrobiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT brigandogabriella microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT suttoncasey microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT uebelhorolivia microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT pitsoulakisnicholas microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT pytyniamatthew microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT dillonthomas microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT elliottburketeresa microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT hubertnathaniel microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT martinezgurynkristina microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT bolchcharlotte microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT cianciomaej microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy AT evanschristianc microbiomeofanoutpatientrehabilitationclinicandpredictorsofcontaminationapilotstudy |