Dataset Information

Flow cytometry, a powerful novel tool to rapidly assess bacterial viability in metal working fluids: Proof-of-principle.

ABSTRACT: Metalworking fluids (MWF) are water- or oil-based liquids to cool and lubricate tools, work pieces and machines, inhibit corrosion and remove swarf. One of the major problems in the MWF industry is bacterial growth as bacterial enzymes can cause MWF degradation. In addition, bacteria can form biofilms which hamper the functioning of machines. Last but not least, some bacterial by-products are toxic (e.g. endotoxins) and present potential health risks for metalworking machine operators via the formation of aerosols. Therefore, a novel fast yet accurate analytical method to evaluate and predict the antibacterial capacity of MWF would be an important asset. As such a tool is currently lacking, the present study aimed to develop a protocol based on flow cytometry (FCM) to assess the antibacterial potential of newly developed MWF independent of bacterial growth. Results of this novel method were compared to a biochallenge test currently used in MWF industry and also to traditional plate counts. Our results represent a proof-of-principle that FCM can reliably predict the antibacterial capacity of MWF already within one day of incubation with Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Proteus mirabilis, being substantially faster than the current growth-based methods.

SUBMITTER: Vanhauteghem D

PROVIDER: S-EPMC6358156 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Flow cytometry, a powerful novel tool to rapidly assess bacterial viability in metal working fluids: Proof-of-principle.

Vanhauteghem Donna D Audenaert Kris K Demeyere Kristel K Hoogendoorn Fred F Janssens Geert P J GPJ Meyer Evelyne E

PloS one 20190201 2

Metalworking fluids (MWF) are water- or oil-based liquids to cool and lubricate tools, work pieces and machines, inhibit corrosion and remove swarf. One of the major problems in the MWF industry is bacterial growth as bacterial enzymes can cause MWF degradation. In addition, bacteria can form biofilms which hamper the functioning of machines. Last but not least, some bacterial by-products are toxic (e.g. endotoxins) and present potential health risks for metalworking machine operators via the fo ...[more]

PMID: 30707728

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Project description:Fungal contamination of metalworking fluids (MWF) is a dual problem in automated processing plants because resulting fungal biofilms obstruct cutting, drilling, and polishing machines. Moreover, some fungal species of MWF comprise pathogens such as Fusarium solani Therefore, the development of an accurate analytical tool to evaluate conidial viability in MWF is important. We developed a flow cytometric method to measure fungal viability in MWF using F. solani as the model organism. To validate this method, viable and dead conidia were mixed in several proportions and flow was cytometrically analyzed. Subsequently, we assessed the fungicidal activity of two commercial MWF using flow cytometry (FCM) and compared it with microscopic analyses and plating experiments. We evaluated the fungal growth in both MWF after 7 days using quantitative PCR (qPCR) to assess the predictive value of FCM. Our results showed that FCM distinguishes live from dead conidia as early as 5 h after exposure to MWF, whereas the microscopic germination approach detected conidial viability much later and less accurately. At 24 h, microscopic analyses of germinating conidia and live/dead analyses by FCM correlated well, although the former consistently underestimated the proportion of viable conidia. In addition, the reproducibility and sensitivity of the flow cytometric method were high and allowed assessment of the fungicidal properties of two commercial MWF. Importantly, the obtained flow cytometric results on viability of F. solani conidia at both early time points (5 h and 24 h) correlated well with fungal biomass measurements assessed via a qPCR methodology 7 days after the start of the experiment.IMPORTANCE This result shows the predictive power of flow cytometry (FCM) in assessing the fungicidal capacity of MWF formulations. It also implies that FCM can be implemented as a rapid detection tool to estimate the viable fungal load in an industrial processing matrix (MWF).

Project description:In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.

Dataset Information

Flow cytometry, a powerful novel tool to rapidly assess bacterial viability in metal working fluids: Proof-of-principle.

Publications

Flow cytometry, a powerful novel tool to rapidly assess bacterial viability in metal working fluids: Proof-of-principle.

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