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. 2024 Sep 27;3:100037. doi: 10.1016/j.jfscie.2024.100037

Application of flow cytometry to analyze microbial quality of dental unit water

Clifton M Carey a,∗, Shannon E Mills b, Richard Vigil c, Madison Aungst a, Lacey Favazzo c
PMCID: PMC13229042  PMID: 42239696

Abstract

Background

Dental unit waterline (DUWL) safety is vital for dental practitioners. Unfortunately, the standard heterotrophic plate count (HPC) methods commonly used to identify bacterial contamination dramatically underestimate planktonic bacteria counts. Flow cytometry (FCM) directly counts bacterial cells using laser-activated fluorescence, providing more reliable and timely results.

Methods

Approximately 1,700 DUWL samples were measured by both HPC and FCM methods. HPC samples were plated on R2A agar within 8 hours of receipt, incubated at 25 °C for 7 days, and reported as colony-forming units (CFU)/mL. FCM samples were treated with SYBR Green I fluorescent stain, analyzed and reported as high nucleic acid (HNA) and low nucleic acid (LNA) cell counts. An empirical algorithm was developed using HNA and LNA cell counts compared with the recommended action limit for the Centers for Disease Control and Prevention’s greater than 500 CFU/mL pass or fail.

Results

An FCM threshold limit that included both HNA 250 or greater and LNA 750 or greater cell counts was derived by pairing FCM data with R2A samples dichotomized as passing (≤ 500 CFU/mL) or failing (> 500 CFU/mL) to establish corresponding pass or fail criteria that would minimize false pass and false fail compared with R2A results. 1,388 (81.6%) of samples passed the R2A limit of 500 CFU/mL or fewer, whereas 1,427 (83.9%) FCM samples passed the proposed HNA 250 or fewer and LNA 750 or fewer criteria.

Conclusions

The data show that FCM analysis and the proposed threshold criteria can be used as a powerful adjunctive tool for monitoring DUWL contamination, which includes greater accuracy and the ability to analyze samples immediately on receipt.

Key Words: Dental unit waterline, flow cytometry, biofilm, heterotrophic plate count, water safety, total viable count

Graphical abstract

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Why Is This important?

Dental unit waterline (DUWL) testing for microbial contamination is a major issue in dentistry; numerous infection outbreaks have occurred because of the failure of dental offices to test and remediate their DUWLs effectively. These outbreaks pose a potential risk to children and immunocompromised patients. This article offers a rapid and straightforward solution to enhance health outcomes in dental offices and improve paradigms. Growth-based assays, such as R2A, are the standard for DUWL testing; they are inherently limited in nature as only a portion of microorganisms use the nutrients in an agar plate and grow. Flow cytometry (FCM) is a well-established technology that can be adapted to enhance testing speed and sensitivity outcomes. This study establishes the use of FCM to shorten the length of time between testing and actionable results, which can support clinicians’ ability to limit patient exposure to potentially harmful bacteria. The R2A standard and FCM metrics are directly compared, and an empirical threshold was determined to align results between the 2 technologies.

Introduction

Aquatic biofilms attached to water-bearing surfaces in dental equipment can amplify the number of bacteria and other microorganisms present in water used for dental treatment, including potentially pathogenic microorganisms such as Pseudomonas aeruginosa, nontuberculous mycobacteria (NTM) and Legionella species.1, 2, 3, 4, 5 Outbreaks of pediatric infections caused by NTM in dental practices in Georgia6 and California,7 and fatal cases of legionellosis in Italy8 and Sweden9 associated with contaminated dental procedure water have emphasized the importance of controlling the growth of bacterial biofilm in dental equipment. In the outbreaks among children receiving pulpotomies in Georgia and California, the samples collected months after the outbreaks had R2A plate counts that exceeded the levels recommended by the Centers for Disease Control and Prevention (CDC). Investigators also identified NTM that matched or were similar to those recovered from the infected children. Had the analyses occurred at the time of the pulpotomies, flow cytometry (FCM) would have counted both NTM and heterotrophic plate count (HPC) bacteria, providing a more accurate measure of total organisms present in the sample than would have been possible using plate count methods. An earlier indication of biofilm growth and the shorter time needed to obtain test results using FCM could have resulted in earlier intervention to control biofilm growth in the dental water delivery system.

The 2003 CDC’s Guidelines for Infection Control in Dental Health-Care Settings10 recommends that dentists take measures to ensure that water used for nonsurgical dental procedures has 500 colony-forming units (CFU)/mL or less of heterotrophic water bacteria as measured using the HPC method described the American Water Works Association (AWWA) Standard Methods for the Examination of Water and Wastewater.11 This is consistent with general recommendations for water quality in health care settings defined in the 2003 CDC Guidelines for Environmental Infection Control in Health-Care Facilities.12 This recommendation is derived from the US Environmental Protection Agency National Primary Drinking Water Regulations, which are legally enforceable primary standards and treatment techniques that apply to public water systems.13

Many standard water techniques were developed and optimized using drinking water or wastewater.11,13,14 Dental unit waterlines (DUWLs) present challenges; most dental water testing laboratories use the AWWA spread plate R2A agar method (9215C), membrane filtration method (9215D), or pour plate method (9215B)11,15 to show how well the water system is being maintained. Only a small subset of possible microorganisms can be cultured by the R2A or other HPC methods, thereby missing the ability to identify specific pathogens and estimate the risk for waterborne disease.16, 17, 18 Moreover, bacteria associated with dental case reports and outbreaks, including Legionella species and NTM, are typically not recoverable using R2A media at standard incubation times.19

All test methods that use growth media undercount bacterial numbers to varying degrees.20, 21, 22 According to the World Health Organization publication Heterotrophic Plate Counts and Drinking-water Safety: The Significance of HPCs for Water Quality, only a small proportion of microorganisms present in a water sample may grow and be detected under any given set of HPC test conditions, and the population recovered will differ significantly according to the method used.23 Other factors, such as the presence of residual germicide in samples, may inhibit bacterial growth and lead to false negative results.24 Drinking water is usually disinfected with chlorine or chloramine, both of which can be easily neutralized using sodium thiosulfate, whereas germicides used for dental procedural water, such as silver compounds, may be more difficult to neutralize.25 Culture-based methods also require long incubation times to recover slow-growing heterotrophic water bacteria.26

Researchers in the drinking water treatment community have examined alternatives to traditional culture methods for water quality monitoring. One technology that is rapidly gaining acceptance is FCM, a laser- or impedance-based, biophysical technology that counts suspended cells in a stream of fluid samples that is passed through an electronic detection apparatus.14,26

Moldavan27 first reported the use of a photoelectric technique for the counting of microscopic cells in 1934. Crosland-Taylor28 subsequently applied hydrodynamic focusing technology to automated cell counting systems. Wallace H. Coulter29 patented a detection system based on the electrical conductance of cells suspended in a conductive fluid in 1953. The Coulter Counter became the ubiquitous standard for blood cell counting in medical laboratories around the world. The fluorescence-activated cell sorter was invented in the late 1960s by Herzenberg et al30 to do FCM and cell sorting of viable cells. Their efforts were initially limited by the threshold size for particle detection, nonspecific binding of fluorescent stains, poor sensitivity, and computational capacity.26,31 Advances in the technology led to the development of applications in multiple disciplines, including drinking water quality assessment.26,32, 33, 34

Multiple high-quality reviews14 on the use of FCM for potable water treatment support its use in water testing, with Safford and Bischel26 concluding that:

[W]hile more work is needed to realize the full potential of FCM in water treatment, distribution, and reuse, substantial progress has been made over the past two decades. There is now a sufficiently large body of research documenting successful applications of FCM that the approach could reasonably and realistically see widespread adoption as a routine method for water quality assessment.

However, to date, only the Swiss Federal Office of Public Health has established a standard method for the determination of total cell counts using FCM in fresh water.34 The standard, however, does not assign a maximum contaminant level or other action limit for total cell counts in drinking water.

Research into applications for the field of dental water quality has been limited. In a study investigating the potential of molecular techniques for estimating the amount of biofilm in dental waterlines, Fulford et al35 compared cell counts in samples taken from dental waterlines using FCM to the total viable counts obtained using culture methods. They reported that total viable counts undercounted the number of cells determined using FCM by 50- through 100-fold.35

This study aimed to determine if FCM can provide a reliable alternative or adjunctive method for the evaluation of dental procedural water quality compared with standard culture methods in use by analyzing and comparing the results of water samples submitted by dental practices with a commercial water testing laboratory specializing in dental unit water monitoring (ProEdge Dental Water Labs).

Methods

Study design

All samples were collected from dental clinics and analyzed using HPC culture media and methods in accordance with AWWA11 at ProEdge Dental Water Labs. The samples were collected directly from the DUWLs into sterile containers and shipped with ice packs overnight to the ProEdge Dental Water Labs. One subset of 994 samples was analyzed via FCM methods at the School of Dental Medicine, University of Colorado, and an additional 706 samples were analyzed via FCM methods at ProEdge Dental Water Labs, for a total of 1,700 samples analyzed by AWWA culture methods and FCM between the 2 sites. All samples subjected to FCM analysis were counted within 4 hours after they were plated for R2A analysis.

Bacterial culturing was performed according to the AWWA method 9215C spread plate method using R2A agar with the addition of Dey-Engley neutralizing broth (Microbiology International). Samples were collected by clinic personnel from dental units in practices around the country according to instructions provided by the water testing laboratory. The samples were shipped overnight in laboratory-provided containers. At delivery, samples were vortexed, and a 0.1 mL aliquot was mechanically plated36 onto R2A solid agar media with a biocide inhibitor consisting of a proprietary blend of lecithin, sodium thiosulfate, tween, sodium bisulfate, sodium thioglycolate, and polysorbate 80 (Microbial Research, Inc). After the 9215C methods were followed, the plates were inverted and incubated for 7 days at room temperature (20-28 °C), which is consistent with the temperature of the DUWL in the dental operatory. Colony counts were recorded as CFU/mL at 7 days. The remainder of each sample was transported to the research laboratory at the School of Dental Medicine at the University of Colorado for FCM analysis within 2 hours of R2A plating.

FCM testing was performed using a NovoCyte Flow Cytometer (ACEA Biosciences). The FCM instrument was calibrated daily with fluorescent microspheres to ensure that there was no instrument drift. Total cell counts for live and damaged microbes were determined. Blinding of sample source, investigator observation, and results were maintained throughout the experiments.

Water samples were vortexed and stained with SYBR Green I (S7567; Thermo Fisher Scientific) to identify live bacteria and with propidium iodide (PI) (P3566; Thermo Fisher Scientific) to identify damaged bacteria. The SYBR Green I stain passes through intact cell walls of live bacteria, intercalates within double-stranded DNA, and fluoresces when illuminated with a 488 nm laser light.37 This fluorescence is measured as the sample is passed over the cell counter. In some cells, the SYBR Green-DNA complex fluoresces more brightly than others, resulting in 2 populations of viable or live bacterial counts where the high nucleic acid (HNA) population is associated with greater fluorescence than the low nucleic acid (LNA) population.38 PI does not penetrate the intact cell walls of live bacteria but binds to the DNA of cells that have damaged leaky membranes.39,40 These cells are presumed to be nonviable. FCM depicts the cell counts in a graphical format in which the forward scatter fluorescence is plotted vs the side scatter fluorescence for each cell.33 FCM data analysis uses the principle of gating, in which absolute counts within regions of the plots are determined for each sample. Gates are placed around populations of cells with common characteristics; gating of the samples was established according to manufacturer instructions for water testing.41 When staining according to this protocol, cells fall into 3 regions: HNA, LNA, and damaged.41

DUWL samples (180 μL) were pipetted into 96-well plates. SYBR Green I and PI dyes were diluted 1:10,000, and 20 μL of each dye was added to each sample in the wells of the 96-well plates and incubated at 37 °C for 30 minutes while protected from light to permit dye penetration. The microbial fluorescence was measured as the samples were passed through the NovoCyte system at a medium rate of 35 μL/min using a core tubing diameter of 12.2 μm, with the stop condition set at 20 μL. Gating for data analysis was based on the models presented in Application Note number 12.41 Figure 1 shows examples of the gating that were used for all FCM sample analyses.

Figure 1.

Figure 1

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Examples of gating used for flow cytometry (FCM) data analyses. H: Height of the fluorescent intensity. HNA: High nucleic acid. LNA: Low nucleic acid. PI: Propidium iodide.

The results for the AWWA method 9215C and FCM assays for each sample were combined for graphical and statistical analyses. An empirical algorithm was developed to establish the HNA and LNA counts that would yield the smallest number of false pass and false fail assessments compared with R2A CFU/mL plate counts and the CDC recommended microbial contamination limit of 500 CFU/mL or less.

Results

A weak linear relationship was found, R2 = 0.2436, between the R2A CFU/mL and FCM counts for total viable cells (HNA and LNA), as shown in Figure 2. Because a fitted equation to describe the relationship between R2A agar plated and FCM results was impossible, an empirical algorithm was developed. We developed criteria for our pass or fail algorithm to achieve a minimum number of false-positive (FP) and false-negative (FN) outcomes compared with the R2A results. Because HNA and LNA cell counts have a large degree of variability less than 100 counts, and we assumed that total counts (HNA and LNA) greater than 1,000 would be a fail, we limited the establishment of the pass-fail criteria to within the range from 100 to 1,000 total counts. Therefore, we calculated the number of FP, FN, and total false (TF) outcomes (TF = FP + FN) for each combination of HNA (100, 200, 300, etc) and LNA (900, 800, 700, etc) and plotted the ratio of HNA to LNA vs the false outcomes (FN, FP, TF) or each sample. There was a minimum of TF outcomes at HNA equals 250 and LNA equals 750 counts that can be observed in Figure 3. A total of 1,389 of 1,700 (81.7%) samples passed the R2A limit of 500 CFU/mL or less, 1,017 FCM samples passed a 500 absolute cell numbers/mL or less criterion, and 1,427 of 1,700 (83.9%) FCM samples passed the proposed empirically derived HNA 250 or less and LNA 750 or less criteria (Table). Calculation of cell numbers/mL was determined using the sum of the absolute count of HNA and LNA as previously described.42 The range of R2A CFU/mL was from 0 to too numerous to count, which was plotted as 97,500 CFU/mL with a median of 0 CFU/mL. The range for FCM HNA was 0 through 380,941, with a median of 36. The range for LNA was 0 through 324,552, with a median of 133. An FCM threshold limit of both an HNA 250 or greater and LNA 750 or greater cell counts results in 98 of 1,700 (5.8%) false fail results (a sample passes R2A and fails FCM) and 137 of 1,700 (8.1%) false pass results (a sample fails R2A and passes FCM).

Figure 2.

Figure 2

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Flow cytometry total viable cell count (high nucleic acid and low nucleic acid) vs R2A (colony-forming units [CFU]/mL). The red data points indicate samples that failed the heterotrophic plate count limit of 500 CFU/mL determined by the R2A method, purple data points indicate samples 200 through 499 CFU/mL, and green data points indicate samples less than 200 CFU/mL. The R2 is 0.2436.

Figure 3.

Figure 3

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Number of total false flow cytometry samples vs high nucleic acid (HNA) to low nucleic acid (LNA) count ratio. The black line is the total false counts, the orange line is the false positive counts, and the blue line is the false negative counts. Note the minima located at the HNA to LNA ratio of 0.333 (250 HNA/750 LNA) in which the criteria were derived.

Table.

R2A and FCM∗ pass or fail comparisons.

Measurement Pass R2A Fail R2A Pass FCM Fail FCM
Criteria ≤ 500 colony-forming units/mL ≥ 500 colony-forming units/mL < 250 high nucleic acid and < 750 low nucleic acid > 250 high nucleic acid and > 750 low nucleic acid
Total 1,389 311 1,427 273
% of Total 81.7 18.3 83.9 16.1
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∗FCM: Flow cytometry.

The range of damaged cell counts was from 12 through 679,645, with a median cell count of 2,180. Figure 4 presents the relationship between total viable (HNA and LNA) cell counts and damaged cell counts. There is a weak linear correlation (R2 = 0.0887), indicating that the presence of damaged cells is a poor indicator of the total number of viable cells (Figure 4). Figure 5 presents the relationship between HNA and LNA separately with the R2A results (green data points = pass [≤ 500 CFU/mL], purple data points = marginal pass [200-500 CFU/mL], red data points = fail [> 500 CFU/mL]) for all samples.

Figure 4.

Figure 4

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Total viable cell counts vs damaged cell counts for the R2A groups. The data are shown on logarithmic scales. There is a poor linear correlation between the flow cytometry (FCM) total viable cell count and the FCM damaged cell count at R2 = 0.0887. The red data points indicate samples that failed the heterotrophic plate count limit of 500 colony-forming units (CFU)/mL determined by the R2A method, purple data points indicate samples 200-499 CFU/mL, and green data points indicate samples < 200 CFU/mL.

Figure 5.

Figure 5

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High nucleic acid (HNA) cell counts vs low nucleic acid (LNA) cell counts for the R2A groups. The data are shown on logarithmic scales and depict the relationship between HNA and LNA for the R2A samples, in which the red data points indicate samples that failed the heterotrophic plate count limit of 500 CFU/mL determined by the R2A method, purple data points indicate marginal samples 200-499 CFU/mL, and green data points indicate samples < 200 CFU/mL. The red lines indicate the pass or fail limit for HNA and LNA. The red data points in the lower left quadrant (HNA < 250 and LNA < 750) are false passes compared with the R2A results; the green and purple data points in the upper right quadrant are false fails compared with the R2A results.

The frequency distribution of R2A CFU/mL results (Figure 6) indicates that there is a bimodal pattern, with most test results showing colony counts less than 100 CFU/mL or greater than 1,000 CFU/mL and few (110 of 1,700 [6.5%]) from 100 through 1,000 CFU/mL. Total FCM viable cell counts (HNA and LNA) show a similar bimodal distribution pattern in which the greatest frequencies were for the samples that had very low or very high total viable cell counts (Figure 7).

Figure 6.

Figure 6

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Frequency distribution of R2A colony-forming units (CFU)/mL.

Figure 7.

Figure 7

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Frequency distribution of flow cytometry (FCM) viable cell counts. The increased percentage of samples between the 100-999 viable cell count ranges shows improved sensitivity and nuance in the FCM results.

Discussion

The World Health Organization credits Koch as the first to describe the use of HPC methods for surveillance of bacterial water hygiene.23 With variations in the types of media used and other advancements in technique, HPC has remained a standard for the examination of water quality until the present day. AWWA Standard Methods have proven their value in the management of drinking water systems over many decades, but questions about the inherent limitations of HPC methods emphasize the need to consider new technologies, such as FCM, for both drinking water11 and DUWLs.43 As the number of DUWL tests performed each year in the United States continues to increase, limitations inherent to HPC methods have emerged. These include

  • •

    Poor sensitivity because of limited cultivability of bacterial species using media-based standard methods22,23,32,44

  • •

    Undercounting because of the presence of unneutralized residual germicide16,26

  • •

    Extended incubation times necessary to amplify and count slow-growing heterotrophic water bacteria, which delays reporting of results11,14,45,46

  • •

    The inability to measure the presence of damaged bacterial cells in effluent water47 that may indicate the persistence of biofilm despite acceptable plate counts

  • •

    The inability of AWWA standard methods to recover faster-growing bacterial species or aquatic fungi23

FCM technology avoids these challenges by directly measuring the number of cells present in water samples, offering potential advantages of

  • •

    More precise and replicable measurement of cell counts, including noncultivable bacteria, fungi, and damaged cells47,48

  • •

    No requirement for incubation or bacterial growth

  • •

    Faster turnaround for results and more timely remediation of failed units and improved patient outcomes14

The weak correlation between FCM and R2A methods when total viable cell counts/mL (HNA and LNA) and CFU/mL were measured was not surprising because R2A methods recover only viable, noninhibited bacterial cells or cell aggregates that will grow on selected growth media at a specified time and temperature.26 R2A plating methods are unable to recover up to 98% of the possible bacterial species,16,17,19,20 and different water samples will have differing proportions of these microbes compared with the total population measured via FCM.18,49 The poor linear correlation between R2A and FCM has been shown previously in water research.50 In contrast, FCM provides a direct, real-time measurement of cells present in the water sample, including viable, noncultivable, and damaged microbes. Therefore, FCM methods are advantageous because FCM counts are not affected by variables that influence the growth of organisms on solid media and provide higher and more repeatable numerical counts despite the lack of statistical correlation between the 2 measures.

Both R2A and FCM are capable of measuring the way bacteria grow in DUWLs. CFUs are a measure of total viable count and are expected to be low when DUWL biofilm is well controlled. As bacterial cells and cell aggregates attach to DUWL surfaces, they initially enter a phase in which they adapt to their new environment and begin quorum sensing and upregulation of genes for extrusion of exopolymer to form the biofilm matrix.51,52 Once established, biofilm bacteria divide and grow in response to oxygen, nutrient levels, shear force, pH, etc.52 Therefore, the number of viable cells that are recovered or sensed during the early phase of biofilm development should be small and will be reflected in both R2A and FCM counts. Once a biofilm has reached a critical mass of growth or has exhausted the components it needs to grow, pieces of the mature biofilm may disperse and seed to new locations.52 In these circumstances, higher microbial abundance can be observed both by growth-based assays and FCM.

Both FCM and R2A methods must be able to provide users with actionable information to manage better both planktonic and biofilm growth in dental water delivery systems. R2A provides a single numerical result (CFU/mL), which indirectly measures the extent of biofilm present in the water delivery system. FCM also indirectly measures biofilm by counting cells sorted by the gating process into HNA and LNA counts; HNA and LNA represent intact cells, and PI staining allows the quantification of damaged, nonviable cells. Both R2A CFU/mL and the FCM HNA and LNA counts measure intact viable cells, albeit with dramatically different levels of sensitivity. A fundamental difference between these 2 methods is that FCM provides data about all viable cells, whether cultivable or not, whereas R2A methods can only offer information about cultivable counts. In addition, R2A cannot provide a measure comparable with the damaged cell count produced by FCM with PI.4,47

The potential for rapid remediation of DUWLs that fail to meet CDC guidelines is an advantage of FCM testing that cannot be overstated. The lengthy incubation times inherent to R2A mean that contaminated water may continue to be used in patient procedures throughout the testing process, increasing the risk of negative health outcomes. Under ideal R2A circumstances, 7 days are required from commencement of testing to release of results, during which time dental care professionals have no information about the compliance of their DUWLs. In contrast, FCM eliminates this unnecessary risk by providing results in minutes to hours, allowing prompt corrective action to be taken to protect dental patients.

The goal of any DUWL testing is to ensure that lines are well-maintained, safe, and compliant with water quality standards. Because FCM and R2A plating are fundamentally different technologies that measure these outcomes in different ways, it is not surprising a linear relationship could not be established with high statistical validity. However, because R2A testing is an accepted standard for measuring the CDC guidelines of 500 CFU/mL HPC, we successfully attempted to correlate these paradigms using the algorithm in which R2A results were dichotomized into passing (< 500 CFU/mL) and failing (≥ 500 CFU/mL) and compared with HNA and LNA thresholds. A pattern emerged that allowed the proposal of a pass-fail FCM maximum contamination limit that corresponds to the pass-fail criteria of R2A testing; the HNA and LNA thresholds were set at 250 and 750 cell counts, respectively, to minimize false negatives and false positives. In showing that results from the 2 technologies can be harmonized despite their different mechanisms, we have provided evidence for the practical use of FCM in DUWL testing in circumstances that previously favored R2A, as well as addressed the weak correlation between R2A-measured CFUs and FCM-measured total cell counts.

The CDC 500 CFU/mL maximum contamination limit is an empirically derived engineering standard that dates from the late 19th century, before modern understanding of biofilm.52 Furthermore, this recommendation has no direct evidence-based association with risk of human infection or any other health care outcome.23 This will also be true for any FCM-derived maximum contamination limits that correspond to results obtained from culture-based methods.

The results of our study suggest that FCM has the potential to become an essential technology for monitoring dental water quality. Until standards developers, policy makers, and regulators achieve consensus and new guidance and rules are published, R2A will continue to be used in the management of dental water quality. However, FCM can now be used as an adjunctive tool to provide more accurate and timely information for practitioners that, with support from scientific evidence, may render culture-based methods obsolete.

Conclusions

On the basis of the results of this study, the improved accuracy, efficiency, and more rapid turn-around of test results suggest a bright future for this technology to monitor DUWL safety. Further research to develop a consensus on acceptable levels of bacteria in water used for dental treatment dental unit water based on FCM should be pursued.

Disclosures

Drs Carey and Mills received funding from ProEdge Dental Water Labs for the project. Mr Vigil and Dr Favazzo are employees of ProEdge Dental Water Labs. Dr Aungst did not report any disclosures.

Footnotes

Clifton M. Carey serves as an Editorial Board member for JADA FS. Dr Carey was not involved in decisions about the article he wrote, and peer review was handled independently.

This study was funded by ProEdge Dental Water Labs.

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