Literature DB >> 17397475

Environmental microbial contamination in a stem cell bank.

F Cobo1, A Concha.   

Abstract

AIM: The aim of this study was to evaluate the main environmental microbial contaminants of the clean rooms in our stem cell bank. METHODS AND
RESULTS: We have measured the microbial air contamination by both passive and active air sampling and the microbial monitoring of surfaces by means of Rodac plates. The environmental monitoring tests were carried out in accordance with the guidelines of European Pharmacopeia and US Pharmacopeia. The micro-organisms were identified by means of an automated system (VITEK 2). During the monitoring, the clean rooms are continually under good manufacturing practices specifications. The most frequent contaminants were Gram-positive cocci.
CONCLUSIONS: The main contaminants in our stem cell bank were coagulase-negative staphylococci and other opportunistic human pathogens. In order to assure the levels of potential contamination in both embryonic and adult stem cell lines, a continuous sampling of air particles and testing for viable microbiological contamination is necessary. SIGNIFICANCE AND IMPACT OF THE STUDY: This study is the first evaluation of the environmental contaminants in stem cell banks and can serve as initial evaluation for these establishments. The introduction of environmental monitoring programmes in the processing of stem cell lines could diminish the risk of contamination in stem cell cultures.

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Mesh:

Year:  2007        PMID: 17397475     DOI: 10.1111/j.1472-765X.2006.02095.x

Source DB:  PubMed          Journal:  Lett Appl Microbiol        ISSN: 0266-8254            Impact factor:   2.858


  3 in total

1.  Prospective Evaluation of a Practical Guideline for Managing Positive Sterility Test Results in Cell Therapy Products.

Authors:  Sandhya R Panch; Thejaswi Bikkani; Vanessa Vargas; Jolynn Procter; James W Atkins; Virginia Guptill; Karen M Frank; Anna F Lau; David F Stroncek
Journal:  Biol Blood Marrow Transplant       Date:  2018-08-09       Impact factor: 5.742

2.  Raman spectra-based deep learning: A tool to identify microbial contamination.

Authors:  Murali K Maruthamuthu; Amir Hossein Raffiee; Denilson Mendes De Oliveira; Arezoo M Ardekani; Mohit S Verma
Journal:  Microbiologyopen       Date:  2020-10-16       Impact factor: 3.139

Review 3.  Pathogenicity and Its Implications in Taxonomy: The Brucella and Ochrobactrum Case.

Authors:  Edgardo Moreno; José María Blasco; Jean Jacques Letesson; Jean Pierre Gorvel; Ignacio Moriyón
Journal:  Pathogens       Date:  2022-03-21
  3 in total

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