Center for Biofilm Engineering (CBE)
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/9334
At the Center for Biofilm Engineering (CBE), multidisciplinary research teams develop beneficial uses for microbial biofilms and find solutions to industrially relevant biofilm problems. The CBE was established at Montana State University, Bozeman, in 1990 as a National Science Foundation Engineering Research Center. As part of the MSU College of Engineering, the CBE gives students a chance to get a head start on their careers by working on research teams led by world-recognized leaders in the biofilm field.
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Item Comparison of quantification methods for an endoscope lumen biofilm model(Elsevier BV, 2023-12) Haas, Bruno; James, Sarah; Parker, Albert E.; Gagnon, Marie-Claude; Goulet, Noémie; Labrie, PhilippeBiofilm has been implicated in multi-drug resistant organism outbreaks following endoscopic procedures. Automated Endoscope Reprocessors (AER) are devices validated to clean and disinfect endoscopes per applicable standards. The ISO 15883 part 4 standard guides performance testing validation of AERs, including cleaning performance using a biofilm test soil. The standard recommends assessment of biofilm reduction using protein or carbohydrate quantification methods. The aim of this study was to assess the suitability of various quantification methods using the ISO biofilm model. The ISO 15883 part 5 biofilm test soil method was used to grow biofilm within lumens representative of endoscopes channels. The biofilm was then quantified using five methods: Crystal Violet (CV), Colony Forming Units (CFU), Total Organic Carbon (TOC), protein assay with Orthophtalaldehyde (OPA), and protein assay by micro bicinchoninic acid (μBCA). The five methods were statistically analyzed for their ability to assess biofilm reduction on samples accurately and precisely. In addition, the quantification methods were compared to demonstrate statistical equivalency, and thus their suitability for assessing biofilm cleaning performance testing of AERs.Item Detection of Microbes in Ice Using Microfabricated Impedance Spectroscopy Sensors(The Electrochemical Society, 2023-12) Kaiser-Jackson, Lauren B.; Dieser, Markus; McGlennen, Matthew; Parker, Albert E.; Foreman, Christine M.; Warnat, StephanDuring the growth of a polycrystalline ice lattice, microorganisms partition into veins, forming an ice vein network highly concentrated in salts and microbial cells. We used microfabricated electrochemical impedance spectroscopy (EIS) sensors to determine the effect of microorganisms on the electrochemical properties of ice. Solutions analyzed consisted of a 176 μS cm−1 conductivity solution, fluorescent beads, and Escherichia coli HB101-GFP to model biotic organisms. Impedance spectroscopy data were collected at −10 °C, −20 °C, and −25 °C within either ice veins or ice grains (i.e., no veins) spanning the sensors. After freezing, the fluorescent beads and E. coli were partitioned into the ice veins. The corresponding impedance data were discernibly different in the presence of ice veins and microbial impurities. The presence of microbial cells in ice veins was evident by decreased electrical characteristics (electrode polarization between electrode and ice matrix) relative to solid ice grains. Further, this electrochemical behavior was reversed in all bead-doped solutions, indicating that microbial processes influence sensor response. Linear mixed-effects models empirically corroborated the differences in polarization associated with the presence and absence of microbial cells in ice. We show that EIS has the potential to detect microbes in ice and differentiate between veins and solid grains.Item Sample sizes for estimating the sensitivity of a monitoring system that generates repeated binary outcomes with autocorrelation(Sage Publications, 2023-11) Parker, Albert E.; Arbogast, James W.Sample size formulas are provided to determine how many events and how many patient care units are needed to estimate the sensitivity of a monitoring system. The monitoring systems we consider generate time series binary data that are autocorrelated and clustered by patient care units. Our application of interest is an automated hand hygiene monitoring system that assesses whether healthcare workers perform hand hygiene when they should. We apply an autoregressive order 1 mixed effects logistic regression model to determine sample sizes that allow the sensitivity of the monitoring system to be estimated at a specified confidence level and margin of error. This model overcomes a major limitation of simpler approaches that fail to provide confidence intervals with the specified levels of confidence when the sensitivity of the monitoring system is above 90%.Item Harvesting and Disaggregation: An Overlooked Step in Biofilm Methods Research(MyJove Corporation, 2022-04) Buckingham-Meyer, Kelli; Miller, Lindsey A.; Parker, Albert E.; Walker, Diane K.; Sturman, Paul; Novak, Ian; Goeres, Darla M.Biofilm methods consist of four distinct steps: growing the biofilm in a relevant model, treating the mature biofilm, harvesting the biofilm from the surface and disaggregating the clumps, and analyzing the sample. Of the four steps, harvesting and disaggregation are the least studied but nonetheless critical when considering the potential for test bias. This article demonstrates commonly used harvesting and disaggregation techniques for biofilm grown on three different surfaces. The three biofilm harvesting and disaggregation techniques, gleaned from an extensive literature review, include vortexing and sonication, scraping and homogenization, and scraping, vortexing and sonication. Two surface types are considered: hard non-porous (polycarbonate and borosilicate glass) and porous (silicone). Additionally, we provide recommendations for the minimum information that should be included when reporting the harvesting technique followed and an accompanying method to check for bias.Item Bacterial transfer and biofilm formation in needleless connectors in a clinically simulated in vitro catheter model(Cambridge University Press, 2023-04) Ryder, Marcia; deLancey-Pulcini, Elinor; Parker, Albert E.; James, Garth A.Objective: Although needleless connectors (NCs) are widely used in clinical practice, they carry significant risk of bloodstream infection (BSI). In this study, we quantified differences in bacterial transfer and biofilm formation between various NCs. Design: Prospective, clinically simulated in vitro experimental study. Methods: We tested 20 NCs in a 5-day clinical simulation of Staphylococcus aureus inoculations onto NC septum surfaces, which were then flushed with saline and cultured for bacterial transfer. Biofilm formation was measured through destructive sampling of the connector-catheter system. Moreover, 8 NC design factors were evaluated for their influence on bacterial transfer and biofilm formation. This study was designed without a disinfection protocol to ascertain the intrinsic risk of each NC. Results: Clave Neutron and MicroClave had the lowest overall mean log density of bacteria in the flush compared to other NCs (P < .05), except there were no statistically significant differences between Clave Neutron, Microclave, SafeTouch, and SafeAccess (P ≥ .05). The amount of biofilm in the NC was positively associated with bacteria in the flush (P < .0005). Among 8 design factors, flow path was most important, with the internal cannula associated with a statistically significant 1 log reduction (LR) in bacteria in the flush (R2 = 49%) and 0.5–2 LR in the connector (R2 = 34%). All factors together best explained bacteria in the flush (R2 = 65%) and biofilm in the connector (R2 = 48%). Conclusions: Bacterial transfer and biofilm formation in the connector-catheter system varied statistically significantly between the 20 NCs, suggesting that NC choice can lower the risk of developing catheter-related BSIs.Item Harvesting and Disaggregation: An Overlooked Step in Biofilm Methods Research(MyJove Corporation, 2022-04) Buckingham-Meyer, Kelli; Miller, Lindsey A.; Parker, Albert E.; Walker, Diane K.; Sturman, Paul; Novak, Ian; Goeres, Darla M.Biofilm methods consist of four distinct steps: growing the biofilm in a relevant model, treating the mature biofilm, harvesting the biofilm from the surface and disaggregating the clumps, and analyzing the sample. Of the four steps, harvesting and disaggregation are the least studied but nonetheless critical when considering the potential for test bias. This article demonstrates commonly used harvesting and disaggregation techniques for biofilm grown on three different surfaces. The three biofilm harvesting and disaggregation techniques, gleaned from an extensive literature review, include vortexing and sonication, scraping and homogenization, and scraping, vortexing and sonication. Two surface types are considered: hard non-porous (polycarbonate and borosilicate glass) and porous (silicone). Additionally, we provide recommendations for the minimum information that should be included when reporting the harvesting technique followed and an accompanying method to check for bias.Item Symmetry-Breaking Bifurcations of the Information Bottleneck and Related Problems(MDPI AG, 2022-09) Parker, Albert E.; Dimitrov, Alexander G.In this paper, we investigate the bifurcations of solutions to a class of degenerate constrained optimization problems. This study was motivated by the Information Bottleneck and Information Distortion problems, which have been used to successfully cluster data in many different applications. In the problems we discuss in this paper, the distortion function is not a linear function of the quantizer. This leads to a challenging annealing optimization problem, which we recast as a fixed-point dynamics problem of a gradient flow of a related dynamical system. The gradient system possesses an 𝑆𝑁 symmetry due to its invariance in relabeling representative classes. Its flow hence passes through a series of bifurcations with specific symmetry breaks. Here, we show that the dynamical system related to the Information Bottleneck problem has an additional spurious symmetry that requires more-challenging analysis of the symmetry-breaking bifurcation. For the Information Bottleneck, we determine that when bifurcations occur, they are only of pitchfork type, and we give conditions that determine the stability of the bifurcating branches. We relate the existence of subcritical bifurcations to the existence of first-order phase transitions in the corresponding distortion function as a function of the annealing parameter, and provide criteria with which to detect such transitionsItem Calculating the limit of detection for a dilution series(Elsevier BV, 2023-05) Sharp, Julia L.; Parker, Albert E.; Hamilton, Martin A.Aims. Microbial samples are often serially diluted to estimate the number of microbes in a sample, whether as colony-forming units of bacteria or algae, plaque forming units of viruses, or cells under a microscope. There are at least three possible definitions for the limit of detection (LOD) for dilution series counts in microbiology. The statistical definition that we explore is that the LOD is the number of microbes in a sample that can be detected with high probability (commonly 0.95). Methods and results. Our approach extends results from the field of chemistry using the negative binomial distribution that overcomes the simplistic assumption that counts are Poisson. The LOD is a function of statistical power (one minus the rate of false negatives), the amount of overdispersion compared to Poisson counts, the lowest countable dilution, the volume plated, and the number of independent samples. We illustrate our methods using a data set from Pseudomonas aeruginosa biofilms. Conclusions. The techniques presented here can be applied to determine the LOD for any counting process in any field of science whenever only zero counts are observed. Significance and impact of study. We define the LOD when counting microbes from dilution experiments. The practical and accessible calculation of the LOD will allow for a more confident accounting of how many microbes can be detected in a sample.Item Imaging and plate counting to quantify the effect of an antimicrobial: A case study of a photo-activated chlorine dioxide treatment(Oxford University Press, 2022-12) Parker, Albert E.; Miller, Lindsey; Adams, Jacob; Pettigrew, Charles; Buckingham-Meyer, Kelli; Summers, Summers; Christen, Andres; Goeres, DarlaAim. To assess removal versus kill efficacies of antimicrobial treatments against thick biofilms with statistical confidence. Methods and results. A photo‐activated chlorine dioxide treatment (Photo ClO2) was tested in two independent experiments against thick (>100 μm) Pseudomonas aeruginosa biofilms. Kill efficacy was assessed by viable plate counts. Removal efficacy was assessed by 3D confocal scanning laser microscope imaging (CSLM). Biovolumes were calculated using an image analysis approach that models the penetration limitation of the laser into thick biofilms using Beer's Law. Error bars are provided that account for the spatial correlation of the biofilm's surface. The responsiveness of the biovolumes and plate counts to the increasing contact time of Photo ClO2 were quite different, with a massive 7 log reduction in viable cells (95% confidence interval [CI]: 6.2, 7.9) but a more moderate 73% reduction in biovolume (95% CI: [60%, 100%]). Results are leveraged to quantitatively assess candidate CSLM experimental designs of thick biofilms. Conclusions. Photo ClO2 kills biofilm bacteria but only partially removes the biofilm from the surface. To maximize statistical confidence in assessing removal, imaging experiments should use fewer pixels in each z‐slice, and more importantly, at least two independent experiments even if there is only a single field of view in each experiment. Significance and impact of study. There is limited penetration depth when collecting 3D confocal images of thick biofilms. Removal can be assessed by optimally fitting Beer's Law to all of the intensities in a 3D image and by accounting for the spatial correlation of the biofilm's surface. For thick biofilms, other image analysis approaches are biased or do not provide error bars. We generate unbiased estimates of removal and assess candidate CSLM experimental designs of thick biofilms with different pixilations, numbers of fields of view and number of experiments using the included design tool.Item The impact of automated hand hygiene monitoring with and without complementary improvement strategies on performance rates(Cambridge University Press, 2022-08) Arbogast, James W.; Moore, Lori D.; DiGiorgio, Megan; Robbins, Greg; Clark, Tracy L.; Thompson, Maria F.; Wagner, Pamela T.; Boyce, John M.; Parker, Albert E.Objective: To determine how engagement of the hospital and/or vendor with performance improvement strategies combined with an automated hand hygiene monitoring system (AHHMS) influence hand hygiene (HH) performance rates. Design: Prospective, before-and-after, controlled observational study. Setting: The study was conducted in 58 adult and pediatric inpatient units located in 10 hospitals. Methods: HH performance rates were estimated using an AHHMS. Rates were expressed as the number of soap and alcohol based hand rub portions dispensed divided by the number of room entries and exits. Each hospital self-assigned to one of the following intervention groups: AHHMS alone (control group), AHHMS plus clinician-based vendor support (vendor-only group), AHHMS plus hospital led unit-based initiatives (hospital-only group), or AHHMS plus clinician-based vendor support and hospital-led unit-based initiatives (vendor-plus-hospital group). Each hospital unit produced 1–2 months of baseline HH performance data immediately after AHHMS installation before implementing initiatives. Results: Hospital units in the vendor-plus-hospital group had a statistically significant increase of at least 46% in HH performance compared with units in the other 3 groups (P ≤ .006). Units in the hospital only group achieved a 1.3% increase in HH performance compared with units that had AHHMS alone (P = .950). Units with AHHMS plus other initiatives each had a larger change in HH performance rates over their baseline than those in the AHHMS-alone group (P < 0.001). Conclusions: AHHMS combined with clinician-based vendor support and hospital-led unit-based initiatives resulted in the greatest improvements in HH performance. These results illustrate the value of a collaborative partnership between the hospital and the AHHMS vendor.