a sample collection medium. Cell lysis will release the multiple targets concentrated within a host cell and may eliminate some unspecific aggregation. In summary, we showed that even when pathogens are homogeneously distributed in a sample, it is not theoret- ically possible to collect a single pathogen consistently if the concentration of pathogen is below a certain limit. The probability model presented in this study can be a useful tool to predict the impact of aliquot volume on the limit of detection and reproducibility of clinical assays. Grant/funding support: None declared. Financial disclosures: None declared. Acknowledgments: We thank Dr. Attila Lorincz for the support of this project and Katherine Mack for her help in preparing this manuscript. References 1. Ward ME. The chlamydial developmental cycle. In: Barron AL, ed. Microbiology of Chlamydia. Boca Raton: CRC Press, 1988:71–95. 2. Carabeo RA, Grieshaber SS, Fischer E, Hackstadt T. Chlamydia trachomatis induces remodeling of the actin cytoskeleton during attachment and entry into HeLa cells. Infect Immun 2002;70:3793– 803. 3. Ieven M, Goossens H. Relevance of nucleic acid amplification techniques for diagnosis of respiratory tract infections in the clinical laboratory. Clin Micro- biol Rev 1997;10:242–56. 4. Chong S, Jang D, Song X, Mahony J, Petrich A, Barriga P, et al. Specimen processing and concentration of Chlamydia trachomatis added can influence false-negative rates in the LCx assay but not in the APTIMA combo 2 assay when testing for inhibitors. J Clin Microbiol 2003;41:778 – 82. 5. Stormer M, Kleesiek K, Dreier J. High-volume extraction of nucleic acids by magnetic bead technology for ultrasensitive detection of bacteria in blood components. Clin Chem 2007;53:104 –10. 6. Shapiro DS. Quality control in nucleic acid amplification methods: use of elementary probability theory. J Clin Microbiol 1999;37:848 –51. 7. Kurahashi H, Emanuel BS. Unexpectedly high rate of de novo constitutional t(11;22) translocations in sperm from normal males. Nat Genet 2001;29: 139 – 40. 8. Remund KM, Dixon DA, Wright DL, Holden LR. Statistical considerations in seed purity testing for transgenic traits. Seed Science Research 2001;11: 101–19. Previously published online at DOI: 10.1373/clinchem.2007.089854 A Novel Equation to Estimate Glomerular Filtration Rate Using Beta-Trace Protein, Christine A. White, 1 Ayub Akbari, 2,3 Steve Doucette, 4 Dean Fergusson, 4 Naser Hussain, 2 Laurent Dinh, 5 Guido Filler, 6 Nathalie Lepage, 7 and Greg A. Knoll 2,3,4* ( 1 Division of Nephrology, Department of Med- icine, Queen’s University, Kingston, Canada; 2 Division of Nephrology, Department of Medicine, University of Ot- tawa, Ottawa, Ontario, Canada; 3 Kidney Research Cen- tre, The Ottawa Health Research Institute, Ottawa, Can- ada; 4 Clinical Epidemiology Program, The Ottawa Health Research Institute, Ottawa, Canada; 5 Division of Nuclear Medicine, Department of Medicine, University of Ottawa, Ottawa, Canada; 6 Division of Nephrology, Department of Pediatrics, University of Western Ontario, London, Canada; 7 Department of Laboratory Medicine, Children’s Hospital of Eastern Ontario and the University of Ottawa, Ottawa, Canada; * address correspondence to this author at: Division of Nephrology, The Ottawa Hospital, Riverside Campus, 1967 Riverside Dr., Ottawa, Ontario, Canada K1H 7W9; fax 613-738-8337, e-mail gknoll@ottawahospital.on.ca) Background: Beta-trace protein (BTP) is a low molecular weight glycoprotein that is a more sensitive marker of glomerular filtration rate (GFR) than serum creatinine. The utility of BTP has been limited by the lack of an equation to translate BTP into an estimate of GFR. The objectives of this study were to develop a BTP-based GFR estimation equation. Methods: We measured BTP and GFR by 99m techne- tium-diethylenetriaminepentaacetic acid in 163 stable Table 1. Comparison of predicted a and experimental b FNR. Parameters Target copies/mL Data source Aliquot size 1 L 2 L 4 L 10 L 20 L (A) False negatives/replicates 2 PCR data 44/44 40/40 36/36 31/32 32/32 200 PCR data 36/44 28/40 17/36 11/32 0/32 1000 PCR data 9/20 2/8 1/8 0/8 0/8 2000 PCR data 3/8 1/8 0/8 0/8 0/8 (B) FNR(%) 2 PCR data 100.00 100.00 100.00 96.88 100.00 Predicted value 99.80 99.60 99.20 98.01 96.05 200 PCR data 81.82 70.00 47.22 34.38 0.00 Predicted value 81.87 67.01 44.88 13.44 1.78 1000 PCR data 45.00 25.00 12.50 0.00 0.00 Predicted value 36.75 13.48 1.80 0.01 0.01 2000 PCR data 37.50 12.50 0.00 0.00 0.00 Predicted value 13.51 1.82 0.03 0.01 0.01 a Equation (3) was used to predict FNR(%). b PCR was performed as follows: primers, 5'-GCACCAAAAGAGAACTGCAATGT, 5'-CATATACCTCACGTCGCAGTAACT, and a TaqMan probe 5'-FAMCAGGACCCACAG GAGCGACCCAGA-3' IABlkFQ were ordered from Integrated DNA Technologies. Platinum® Quantitative PCR Supermix-UDG was purchased from Invitrogen. Amplification was performed on the Stratagene MX3005p® instrument (Stratagene): 95 °C 7 min, followed by 45 cycles of: 95 °C/30 s, 55 °C/30 s, 72 °C/30 s. Clinical Chemistry 53, No. 11, 2007 1965 Downloaded from https://academic.oup.com/clinchem/article/53/11/1965/5627346 by guest on 20 June 2022