DOI 10.1007/s10517-015-2902-0 Bulletin of Experimental Biology and Medicine, Vol. 159, No. 1, May, 2015
107
METHODS Coherent Fluctuation Nephelometry: A Rapid Method for Urine Screening for Bacterial Contamination A. S. Gur’ev*,***, A. Yu. Volkov*,***, I. I. Dolgushin**, A. V. Pospelova**, S. F. Rastopov*, A. Yu. Savochkina**, and V. I. Sergienko* Translated from Byulleten’ Eksperimental’noi Biologii i Meditsiny, Vol. 159, No. 1, pp. 120-123, January, 2015 Original article submitted April 16, 2014 Express-test by the method of coherent fluctuation nephelometry for urine contamination was carried out on two prototype instruments with standard polystyrene photometric cuvettes. We analyzed 209 and 119 urine samples. Due to high sensitivity of the method, up to 50% negative samples were detected within 10 min by initial opacity and 90% negative samples were detected during 3.5 h by registration of the bacterial growth curves. Key Words: coherent fluctuation nephelometry; urine contamination; rapid screening of urine samples
Microbiological analysis of human biological fluids occupies a special place among clinical diagnostic methods used in screening studies and urgent medicine. Urine testing for contamination is one of the most frequent microbiological studies, as urinary infections are associated with a wide spectrum of diseases [9]. As 70-80% urine samples are negative according to results of inoculation, a rapid screening method detecting negative samples can significantly reduce the time and material expenditures for microbiological inoculations [3,5,10]. Manual microbiological inoculations, a difficult and time-consuming method, are used in the majority of studies of the microflora. Automated microbiological analyzers are still rare. The majority of analyzers based on analysis of the microorganism growth curve employ optical method, photometry (e.g., Vitek 2 [8]). Recently, optical nephelometry was introduced in clinical microbiology; this method is more sensitive than *Research Institute of Physicochemical Medicine, Federal Biomedical Agency of the Russian Federation, Moscow; **South Ural State Medical University, Ministry of Health of the Russian Federation, Chelyabinsk; ***Medtechnopark Company, Moscow, Russia. Address for correspondence: [email protected]. A. S. Gur’ev
photometry [6,12]. Alifax automated nephelometric microbiological analyzers carry out a rapid microbiological analysis within just several hours, including rapid screening of urine for contamination [4,7]. Standard nephelometry used for registration of low concentrations of bacteria has stringent limitations for the number of cuvettes and optical path of the instrument, as parasitic scatter on optical elements of the nephelometer cannot be separated from useful scatter on the target particles, which prevented the use of nephelometry in laboratory diagnosis until recent time. Coherent fluctuation nephelometry (CFN), developed at A. M. Prokhorov Institute of Physics and Research Institute of Physicochemical Medicine, is free from these flaws. The work of CFN instrument is based on multidetector registration of fluctuations in intensity of interference speckle picture of scattered coherent light, in contrast to common nephelometry that records the mean intensity of scattered light. Due to this approach, the instrument detects exclusively the signal created by the particles moving in the cuvette, while the contribution of the parasitic scatter to the signal does not exceed the electronic noise [11]. The CFN method detects bacterial growth from a concentration
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Bulletin of Experimental Biology and Medicine, Vol. 159, No. 1, May, 2015 METHODS
of 103-104 CFU/ml [2] and measures low opacities even in optical cuvettes of poor quality [1]. We carried out a rapid screening of urine samples by analyzing microflora growth curves and its initial opacity by the CFN method. The aim of screening was to detect and reject negative samples in order to reduce the number of inoculations at microbiological laboratories.
MATERIALS AND METHODS The study was carried out on two CFN prototype instruments (CFN-4 and CFN-48) created at Medtechnopark Company. The cuvettes in both prototypes were warmed to 37oC so that effective convective mixing of the fluid essential for CFN realization was maintained. The CFN-4 was intended for simultaneous opacity measurement in 4 cuvettes. The fluid was mixed in cuvettes due to convection induced by asymmetrical warming [11]. Standard disposable photometric semimicrocuvettes (1 ml; LP ITALIANA SPA) were used on CFN-4 intended for consecutive opacity measurements in six 8-well strips subjected to cyclic rotation relative to the measuring channels (the wells were transilluminated from the side through the round wall). The fluid was mixed due to convection and shaking caused by strip jerk-wise moving. Eight-well strips of standard laboratory 96-well plates (0.2 ml; Biomedical) were used in CFN-48. All cuvettes were covered with Parafilm M (Bemis Company Inc.). Two independent studies were carried out. The aim of the first study was rapid evaluation of urine contamination by the level of its initial opacity. A total of 208 urine samples were screened on a CFN-4 instrument at Microbiological Laboratory of Southampton hospital (Hampshire, UK). It was expected that positive (by the results of inoculations) urine samples would exhibit high opacity, while negative samples would exhibit low opacity, due to which the opacity threshold would be determined for detection of negative samples of the urine without misidentification of the positive samples. The aim of our second study was rapid analysis of urine contamination by bacterial growth. A total of 119 urine samples were analyzed on CFN-48 at Bacteriological Laboratory of Hospital of South Ural State Medical University (Chelyabinsk). Earlier growth was expected in positive (by the results of inoculation) urine samples and delayed growth in negative samples. The time threshold was determined for detection of negative samples without misidentification of positive samples. The results obtained on CFN were compared with the results of inoculations of the same urine samples
in both studies. The prognostic significance of screening methods was evaluated by sensitivity (percentage of positive samples to be used in subsequent microbiological studies); specificity (percentage of negative samples detected on CFN); positive prognostic significance (PPS; probability that the sample with high opacity or bacterial growth would prove to be positive by the result of inoculation); negative prognostic significance (NPS; probability that the sample with low opacity or without bacterial growth would prove to be negative by the result of inoculation). The urine for analysis was collected at least 2 h before analysis for both studies. A sample (1.5 ml) was put into a sterile tube and centrifuged (1700g, 60 sec) in order to precipitate large admixtures (blood cells, mucus, salts) contributing to parasitic opacity. Large scattering particles precipitated to the tube bottom under these conditions, while bacteria remained in the fluid. The supernatant (1 ml) pipetted from the tubes was used in subsequent analysis on CFN. In study 1, the centrifuged urine supernatant was put into a cuvette that was then placed into the instrument, and urine opacity was recorded over 10 min. In study 2, the centrifuged urine supernatant was mixed with sugar meat-peptone broth in a cuvette (1:1), the cuvette was placed into the instrument, and the microorganism growth curve was recorded over 5 h.
RESULTS In study 1, 66 (31.7%) of 208 urine samples were positive (>105 CFU/ml in 49 samples, 104-105 CFU/ ml in 17) and 142 (68.3%) were negative. The results of culture typing of positive samples were as follows: colimorphic (40), Streptococcus spp. (18), Pseudomonas spp. (5), Proteus spp. (4), Candida spp. (2), and Staphylococcus spp. (1). Three opacity threshold were selected by the results of opacity measurements in centrifuged urine supernatants on CFN-4: 5, 6, and 10 arb. units (opacity arb. units was the intensity of CFN output signal, mV). In accordance with each threshold, each sample was labeled after opacity (S) measurement as a sample with “low opacity”(S≤threshold) or “high opacity” (S>threshold). The results of microbiological inoculation (negative–positive) were compared with the results of opacity measurements on CFN (slight opacity–high opacity; Table 1). In study 2, 23 (19.3%) of 119 samples were positive (>105 CFU/ml in 20 and 104-105 CFU/ml in 3 samples) and 96 (80.7%) were negative. The results of culture typing of positive samples were as follows: colimorphic (15), Enterococcus spp. (3), Proteus spp. (2), Candida spp. (1), Chryseobacterium spp. (1), and Acinetobacter spp. (1).
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TABLE 1. Distribution of Samples by Results of Inoculation, by Opacity, and Prognostic Values of CFN Screening of Urine Inoculation
Opacity on CFN, arb. units
Positive: 66 (31.7%)
S>5
S>6
S>10
63 (95.5%)
60 (90.9%)
54 (81.8%)
Negative 142 (68.3%)
S≤5
S≤6
S≤10
40 (28.2%)
65 (45.8%)
122 (86%)
Prognostic values of CFN screening of urine Opacity threshold, arb. units
5
6
10
Sensitivity, %
95.5
90.9
81.8
Specificity, %
28.8
45.8
86
PPS, %
38.2
43.8
73.0
NPS, %
93.0
91.6
91.0
TABLE 2. Distribution of Samples by Results of Inoculation, by Bacterial Growth Inhibition, and Prognostic Values of CFN Screening of Urine Inoculation Positive: 23 (19.3%)
Negative: 96 (80.7%)
Bacterial growth inhibition on CFN, h T≤1.2
T≤2.4
T≤3.5
12 (52.2%)
15 (65.2%)
21 (91.3%)
T>1.2
T>2.4
T>3.5
94 (98.0%)
93 (96.9%)
86 (89.6%)
Prognostic values of CFN screening of urine Time threshold, h
1.2
2.4
3.5
Sensitivity, %
52.2
65.2
91.3
Specificity, %
98.0
96.9
89.6
PPS, %
85.7
83.8
67.7
NPS, %
93.0
91.6
91.0
Bacterium growth registration on CFN-48 consisted in analysis of growth inhibition since the moment of broth and urine supernatant mixing. Three time thresholds were selected: 1.2, 2.4, and 3.5 h. In accordance with these thresholds, each sample was labeled as a sample with “growth” (T≤threshold) or no growth (T>threshold) by the results of growth inhibition (T) measurements. The results of microbiological inoculation (negative–positive) were compared with the results of bacterial growth inhibition measurements on CFN (growth/no growth; Table 2). Study 1 demonstrated that up to 50% negative samples of urine could be rejected within 10 min by CFN analysis, while the majority of positive samples were taken for subsequent analysis by inoculation. Study 2 demonstrated that 90% negative samples were detected and rejected by CFN within 3.5 h, while 91% positive samples were subjected to further analysis.
Moreover, 52% positive samples of urine could be detected within 1.2 h. Hence, CFN is a prospective optical platform for creation of a simple, convenient, reliable, open analytical microbiological system for clinical microbiology. The use of CFN is expected to reduce significantly the number of obligatory microbiological inoculations and obtain a negative result on the day of the urine admission for analysis. Important, the initial opacity of urine samples can be used for screening. Further studies will show the advantages of combined approach to CFN evaluation of the urine contamination by its initial opacity and registration of bacterial growth curves.
REFERENCES 1. A. Yu. Volkov, A. S. Gur’ev, A. D. Levin, et al., Patent RF No. 2516193. Optical Method for Registration of the Kinetics
110
2. 3. 4. 5. 6.
Bulletin of Experimental Biology and Medicine, Vol. 159, No. 1, May, 2015 METHODS
of Particles Aggregation in Opaque Suspensions, Bull. No. 14, May 20, 2014. S. F. Rastopov, Pribory Tekhnika Eksperimenta, No. 6, 95-99 (2011). R. Falbo, M. R. Sala, S. Signorelli, et al., J. Clin. Microbiol., 50, No. 4, 1427-1429 (2012). A. Ilki, P. Bekdemir, N. Ulger, and G. Soyletir, New Microbiol., 33, No. 2, 147-153 (2010). S. Jolkkonen, E. L. Paattiniemi, P. Kärpänoja, and H. Sarkkinen, J. Clin. Microbiol., 48, No. 9, 3117-3121 (2010). A. Joubert, B. Calmes, R. Berruyer, et al., Biotechniques, 48, No. 5, 399-404 (2010).
7. S. Lahanas, G. Stathopoulos, R. C. Chan, and S. J. van Hal, J. Clin. Microbiol., 51, No. 10, 3406-3408 (2013). 8. M. Ligozzi, C. Bernini, M. G. Bonora, et al., J. Clin. Microbiol., 40, No. 5, 1681-1686 (2002). 9. P. Masson, S. Matheson, A. C. Webster, and J. C. Craig, Infect. Dis. Clin. North. Am., 23, No. 2, 355-385 (2009). 10. H. D. Patel, S. A. Livsey, R. A. Swann, and S. S. Bukhari, J. Clin. Pathol., 58, No. 9, 951-954 (2005). 11. S. Rastopov, Optical Detection of Particles in a Liquid Medium, Patent US 7209231, April 24, 2007. 12. C. Wiegand, M. Abel, P. Ruth, and U. C. Hipler, Skin. Pharmacol. Physiol., 25, No. 6, 288-297 (2012).
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METHODS Coherent Fluctuation Nephelometry: A Rapid Method for Urine Screening for Bacterial Contamination A. S. Gur’ev*,***, A. Yu. Volkov*,***, I. I. Dolgushin**, A. V. Pospelova**, S. F. Rastopov*, A. Yu. Savochkina**, and V. I. Sergienko* Translated from Byulleten’ Eksperimental’noi Biologii i Meditsiny, Vol. 159, No. 1, pp. 120-123, January, 2015 Original article submitted April 16, 2014 Express-test by the method of coherent fluctuation nephelometry for urine contamination was carried out on two prototype instruments with standard polystyrene photometric cuvettes. We analyzed 209 and 119 urine samples. Due to high sensitivity of the method, up to 50% negative samples were detected within 10 min by initial opacity and 90% negative samples were detected during 3.5 h by registration of the bacterial growth curves. Key Words: coherent fluctuation nephelometry; urine contamination; rapid screening of urine samples
Microbiological analysis of human biological fluids occupies a special place among clinical diagnostic methods used in screening studies and urgent medicine. Urine testing for contamination is one of the most frequent microbiological studies, as urinary infections are associated with a wide spectrum of diseases [9]. As 70-80% urine samples are negative according to results of inoculation, a rapid screening method detecting negative samples can significantly reduce the time and material expenditures for microbiological inoculations [3,5,10]. Manual microbiological inoculations, a difficult and time-consuming method, are used in the majority of studies of the microflora. Automated microbiological analyzers are still rare. The majority of analyzers based on analysis of the microorganism growth curve employ optical method, photometry (e.g., Vitek 2 [8]). Recently, optical nephelometry was introduced in clinical microbiology; this method is more sensitive than *Research Institute of Physicochemical Medicine, Federal Biomedical Agency of the Russian Federation, Moscow; **South Ural State Medical University, Ministry of Health of the Russian Federation, Chelyabinsk; ***Medtechnopark Company, Moscow, Russia. Address for correspondence: [email protected]. A. S. Gur’ev
photometry [6,12]. Alifax automated nephelometric microbiological analyzers carry out a rapid microbiological analysis within just several hours, including rapid screening of urine for contamination [4,7]. Standard nephelometry used for registration of low concentrations of bacteria has stringent limitations for the number of cuvettes and optical path of the instrument, as parasitic scatter on optical elements of the nephelometer cannot be separated from useful scatter on the target particles, which prevented the use of nephelometry in laboratory diagnosis until recent time. Coherent fluctuation nephelometry (CFN), developed at A. M. Prokhorov Institute of Physics and Research Institute of Physicochemical Medicine, is free from these flaws. The work of CFN instrument is based on multidetector registration of fluctuations in intensity of interference speckle picture of scattered coherent light, in contrast to common nephelometry that records the mean intensity of scattered light. Due to this approach, the instrument detects exclusively the signal created by the particles moving in the cuvette, while the contribution of the parasitic scatter to the signal does not exceed the electronic noise [11]. The CFN method detects bacterial growth from a concentration
0007-4888/15/15910107 © 2015 Springer Science+Business Media New York
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Bulletin of Experimental Biology and Medicine, Vol. 159, No. 1, May, 2015 METHODS
of 103-104 CFU/ml [2] and measures low opacities even in optical cuvettes of poor quality [1]. We carried out a rapid screening of urine samples by analyzing microflora growth curves and its initial opacity by the CFN method. The aim of screening was to detect and reject negative samples in order to reduce the number of inoculations at microbiological laboratories.
MATERIALS AND METHODS The study was carried out on two CFN prototype instruments (CFN-4 and CFN-48) created at Medtechnopark Company. The cuvettes in both prototypes were warmed to 37oC so that effective convective mixing of the fluid essential for CFN realization was maintained. The CFN-4 was intended for simultaneous opacity measurement in 4 cuvettes. The fluid was mixed in cuvettes due to convection induced by asymmetrical warming [11]. Standard disposable photometric semimicrocuvettes (1 ml; LP ITALIANA SPA) were used on CFN-4 intended for consecutive opacity measurements in six 8-well strips subjected to cyclic rotation relative to the measuring channels (the wells were transilluminated from the side through the round wall). The fluid was mixed due to convection and shaking caused by strip jerk-wise moving. Eight-well strips of standard laboratory 96-well plates (0.2 ml; Biomedical) were used in CFN-48. All cuvettes were covered with Parafilm M (Bemis Company Inc.). Two independent studies were carried out. The aim of the first study was rapid evaluation of urine contamination by the level of its initial opacity. A total of 208 urine samples were screened on a CFN-4 instrument at Microbiological Laboratory of Southampton hospital (Hampshire, UK). It was expected that positive (by the results of inoculations) urine samples would exhibit high opacity, while negative samples would exhibit low opacity, due to which the opacity threshold would be determined for detection of negative samples of the urine without misidentification of the positive samples. The aim of our second study was rapid analysis of urine contamination by bacterial growth. A total of 119 urine samples were analyzed on CFN-48 at Bacteriological Laboratory of Hospital of South Ural State Medical University (Chelyabinsk). Earlier growth was expected in positive (by the results of inoculation) urine samples and delayed growth in negative samples. The time threshold was determined for detection of negative samples without misidentification of positive samples. The results obtained on CFN were compared with the results of inoculations of the same urine samples
in both studies. The prognostic significance of screening methods was evaluated by sensitivity (percentage of positive samples to be used in subsequent microbiological studies); specificity (percentage of negative samples detected on CFN); positive prognostic significance (PPS; probability that the sample with high opacity or bacterial growth would prove to be positive by the result of inoculation); negative prognostic significance (NPS; probability that the sample with low opacity or without bacterial growth would prove to be negative by the result of inoculation). The urine for analysis was collected at least 2 h before analysis for both studies. A sample (1.5 ml) was put into a sterile tube and centrifuged (1700g, 60 sec) in order to precipitate large admixtures (blood cells, mucus, salts) contributing to parasitic opacity. Large scattering particles precipitated to the tube bottom under these conditions, while bacteria remained in the fluid. The supernatant (1 ml) pipetted from the tubes was used in subsequent analysis on CFN. In study 1, the centrifuged urine supernatant was put into a cuvette that was then placed into the instrument, and urine opacity was recorded over 10 min. In study 2, the centrifuged urine supernatant was mixed with sugar meat-peptone broth in a cuvette (1:1), the cuvette was placed into the instrument, and the microorganism growth curve was recorded over 5 h.
RESULTS In study 1, 66 (31.7%) of 208 urine samples were positive (>105 CFU/ml in 49 samples, 104-105 CFU/ ml in 17) and 142 (68.3%) were negative. The results of culture typing of positive samples were as follows: colimorphic (40), Streptococcus spp. (18), Pseudomonas spp. (5), Proteus spp. (4), Candida spp. (2), and Staphylococcus spp. (1). Three opacity threshold were selected by the results of opacity measurements in centrifuged urine supernatants on CFN-4: 5, 6, and 10 arb. units (opacity arb. units was the intensity of CFN output signal, mV). In accordance with each threshold, each sample was labeled after opacity (S) measurement as a sample with “low opacity”(S≤threshold) or “high opacity” (S>threshold). The results of microbiological inoculation (negative–positive) were compared with the results of opacity measurements on CFN (slight opacity–high opacity; Table 1). In study 2, 23 (19.3%) of 119 samples were positive (>105 CFU/ml in 20 and 104-105 CFU/ml in 3 samples) and 96 (80.7%) were negative. The results of culture typing of positive samples were as follows: colimorphic (15), Enterococcus spp. (3), Proteus spp. (2), Candida spp. (1), Chryseobacterium spp. (1), and Acinetobacter spp. (1).
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TABLE 1. Distribution of Samples by Results of Inoculation, by Opacity, and Prognostic Values of CFN Screening of Urine Inoculation
Opacity on CFN, arb. units
Positive: 66 (31.7%)
S>5
S>6
S>10
63 (95.5%)
60 (90.9%)
54 (81.8%)
Negative 142 (68.3%)
S≤5
S≤6
S≤10
40 (28.2%)
65 (45.8%)
122 (86%)
Prognostic values of CFN screening of urine Opacity threshold, arb. units
5
6
10
Sensitivity, %
95.5
90.9
81.8
Specificity, %
28.8
45.8
86
PPS, %
38.2
43.8
73.0
NPS, %
93.0
91.6
91.0
TABLE 2. Distribution of Samples by Results of Inoculation, by Bacterial Growth Inhibition, and Prognostic Values of CFN Screening of Urine Inoculation Positive: 23 (19.3%)
Negative: 96 (80.7%)
Bacterial growth inhibition on CFN, h T≤1.2
T≤2.4
T≤3.5
12 (52.2%)
15 (65.2%)
21 (91.3%)
T>1.2
T>2.4
T>3.5
94 (98.0%)
93 (96.9%)
86 (89.6%)
Prognostic values of CFN screening of urine Time threshold, h
1.2
2.4
3.5
Sensitivity, %
52.2
65.2
91.3
Specificity, %
98.0
96.9
89.6
PPS, %
85.7
83.8
67.7
NPS, %
93.0
91.6
91.0
Bacterium growth registration on CFN-48 consisted in analysis of growth inhibition since the moment of broth and urine supernatant mixing. Three time thresholds were selected: 1.2, 2.4, and 3.5 h. In accordance with these thresholds, each sample was labeled as a sample with “growth” (T≤threshold) or no growth (T>threshold) by the results of growth inhibition (T) measurements. The results of microbiological inoculation (negative–positive) were compared with the results of bacterial growth inhibition measurements on CFN (growth/no growth; Table 2). Study 1 demonstrated that up to 50% negative samples of urine could be rejected within 10 min by CFN analysis, while the majority of positive samples were taken for subsequent analysis by inoculation. Study 2 demonstrated that 90% negative samples were detected and rejected by CFN within 3.5 h, while 91% positive samples were subjected to further analysis.
Moreover, 52% positive samples of urine could be detected within 1.2 h. Hence, CFN is a prospective optical platform for creation of a simple, convenient, reliable, open analytical microbiological system for clinical microbiology. The use of CFN is expected to reduce significantly the number of obligatory microbiological inoculations and obtain a negative result on the day of the urine admission for analysis. Important, the initial opacity of urine samples can be used for screening. Further studies will show the advantages of combined approach to CFN evaluation of the urine contamination by its initial opacity and registration of bacterial growth curves.
REFERENCES 1. A. Yu. Volkov, A. S. Gur’ev, A. D. Levin, et al., Patent RF No. 2516193. Optical Method for Registration of the Kinetics
110
2. 3. 4. 5. 6.
Bulletin of Experimental Biology and Medicine, Vol. 159, No. 1, May, 2015 METHODS
of Particles Aggregation in Opaque Suspensions, Bull. No. 14, May 20, 2014. S. F. Rastopov, Pribory Tekhnika Eksperimenta, No. 6, 95-99 (2011). R. Falbo, M. R. Sala, S. Signorelli, et al., J. Clin. Microbiol., 50, No. 4, 1427-1429 (2012). A. Ilki, P. Bekdemir, N. Ulger, and G. Soyletir, New Microbiol., 33, No. 2, 147-153 (2010). S. Jolkkonen, E. L. Paattiniemi, P. Kärpänoja, and H. Sarkkinen, J. Clin. Microbiol., 48, No. 9, 3117-3121 (2010). A. Joubert, B. Calmes, R. Berruyer, et al., Biotechniques, 48, No. 5, 399-404 (2010).
7. S. Lahanas, G. Stathopoulos, R. C. Chan, and S. J. van Hal, J. Clin. Microbiol., 51, No. 10, 3406-3408 (2013). 8. M. Ligozzi, C. Bernini, M. G. Bonora, et al., J. Clin. Microbiol., 40, No. 5, 1681-1686 (2002). 9. P. Masson, S. Matheson, A. C. Webster, and J. C. Craig, Infect. Dis. Clin. North. Am., 23, No. 2, 355-385 (2009). 10. H. D. Patel, S. A. Livsey, R. A. Swann, and S. S. Bukhari, J. Clin. Pathol., 58, No. 9, 951-954 (2005). 11. S. Rastopov, Optical Detection of Particles in a Liquid Medium, Patent US 7209231, April 24, 2007. 12. C. Wiegand, M. Abel, P. Ruth, and U. C. Hipler, Skin. Pharmacol. Physiol., 25, No. 6, 288-297 (2012).
