Virology Journal 2009, 6:41.PubMedCrossRef 74. Lehman SM, Kropinski AM, Castle

AJ, Svircev AM: Complete genome of the broad-host-range Erwinia amylovora EGFR inhibitor phage fEa21–4 and its relationship to Salmonella phage felix O1. Applied & Environmental Microbiology 2009, 75:2139–2147.CrossRef 75. Mobberley JM, Authement RN, Segall AM, Paul JH: The temperate marine phage fHAP-1 of Halomonas aquamarina possesses a linear plasmid-like prophage genome. J Virol 2008, 82:6618–6630.PubMedCrossRef 76. Oakey HJ, Cullen BR, Owens L, Oakey HJ, Cullen BR, Owens L: The complete nucleotide sequence of the Vibrio harveyi bacteriophage VHML. Journal of Applied Microbiology 2002, 93:1089–1098.PubMedCrossRef 77. Oakey HJ, Owens L, Oakey HJ, Owens L: A new bacteriophage, VHML, isolated from a toxin-producing strain of Vibrio harveyi in tropical Australia. Journal of Applied Microbiology 2000, 89:702–709.PubMedCrossRef 78. Mobberley JM, Authement RN, Segall

AM, Paul JH: see more The temperate marine phage FHAP-1 of Halomonas aquamarina possesses a linear plasmid-like prophage genome. Journal of Virology 2008, 82:6618–6630.PubMedCrossRef 79. Ackermann H-W: 5500 Phages examined in the electron microscope. Archives of Virology 2007, 152:227–243.PubMedCrossRef 80. Hatfull GF, Pedulla ML, Jacobs-Sera D, Cichon PM, Foley A, Ford ME, Gonda RM, Houtz JM, Hryckowian AJ, Kelchner VA, Namburi S, Pajcini KV, Popovich MG, Schleicher DT, Simanek

BZ, Smith AL, Zdanowicz GM, Kumar V, Peebles CL, Jacobs WR Jr, Lawrence JG, Hendrix RW: Exploring the mycobacteriophage metaproteome: phage genomics as an educational platform. Nutlin-3 research buy PLoS Genetics 2006, 2:e92.PubMedCrossRef 81. Pedulla ML, Ford ME, Houtz JM, Karthikeyan T, Wadsworth C, Lewis JA, Jacobs-Sera D, Falbo J, Gross J, Pannunzio NR, Brucker W, Kumar V, Kandasamy J, Keenan L, Bardarov S, Kriakov J, Lawrence JG, Jacobs WR Jr, Hendrix RW, Hatfull GF: Origins of highly mosaic mycobacteriophage genomes. Cell 2003, 113:171–182.PubMedCrossRef 82. Mayer MJ, Narbad A, Gasson MJ: Molecular characterization of a Selleck Everolimus Clostridium difficile bacteriophage and its cloned biologically active endolysin. Journal of Bacteriology 2008, 190:6734–6740.PubMedCrossRef 83. Goh S, Ong PF, Song KP, Riley TV, Chang BJ: The complete genome sequence of Clostridium difficile phage fC2 and comparisons to fCD119 and inducible prophages of CD630. Microbiology 2007, 153:676–685.PubMedCrossRef 84. Govind R, Fralick JA, Rolfe RD: Genomic organization and molecular characterization of Clostridium difficile bacteriophage FCD119. Journal of Bacteriology 2006, 188:2568–2577.PubMedCrossRef 85. Goh S, Riley TV, Chang BJ: Isolation and characterization of temperate bacteriophages of Clostridium difficile. Appl Environ Microbiol 2005, 71:1079–1083.PubMedCrossRef 86.

14 %; p < 0.01). Of the 688 biological mothers, who completed the fracture questionnaire, 60 (9 %) indicated that they had sustained a fracture before the age of 18 years (white mothers 31 %, mixed ancestry 16 %, black mothers 6 %; W > B, p < 0.001; MA > B, p = 0.01). Unlike the pattern of fracture incidence among the adolescents and their siblings, there was no difference in the prevalence of fractures among the adolescents of mothers who had or did not have a history of fractures. Bivariate logistic regression analyses were initially performed for the whole group to assess if any confounding variables, such as weight, height, ethnicity, gender,

pubertal stage, adolescents’ and mothers’ S63845 datasheet BA and BMC (TB and LS), and sibling history of fracture or maternal history of fracture, were individually associated with adolescent fracture risk. In these analyses, the adolescent’s risk of fracture was higher if a sibling had a history of fracture (OR = 1.6, 95 % CI 1.12–2.32, p = 0.01), but was not associated with maternal history of fracture (OR = 1.09, 95 % CI 0.63–1.86, p = 0.762). Neither adolescent weight nor pubertal stage was associated with fracture risk of the entire www.selleckchem.com/products/Nilotinib.html cohort; however, height was positively associated with

the risk of fracture (OR = 9.85, 95 % CI 2.31–41.83, p < 0.01), and males were at greater risk of fracture compared to females (OR = 1.73, 95 % CI 1.33–2.24, p < 0.001). Adolescent TB BA (OR = 1.0008, 95 % CI 1.0002–1.001; p < 0.05) and TB BMC (OR = 1.0004, 95%CI 1.000002–1.0007, p < 0.05) were both marginally associated with increased fracture risk. Maternal LS BMC was inversely also associated

with fracture risk in their adolescent selleck products offspring (OR = 0.80, 95 % CI 0.7–0.93; p < 0.01). White adolescents had a greater risk of fracture than other ethnic groups (OR = 2.82, 95 % CI 1.82–4.37, p < 0.001). Multivariate logistic regression analyses were performed on the entire group (n = 1099) to determine the risk factors for fractures in the adolescents. The factors which had been found to be significantly associated in simple logistic regression and multiple regression analyses were included in the model, namely gender, ethnicity, sibling history of fracture, adolescent and maternal heights, adolescent TB BA and BMC, and maternal LS BMC. Only the significant risk factors for adolescent fracture risk are shown in Table 4. White ethnicity and male gender remained significant, with a greater risk of adolescent fracture. The adolescent’s risk of fracture was 50 % greater if a sibling had a history of fracture (OR = 1.5, 95 % CI 1.02–2.21, p < 0.05). Maternal LS BMC was protective against the risk of fracture in the adolescent (24 % reduction in fracture risk for every 1 unit increase in maternal BMC Z-score). Table 4 Odds ratios for fractures in 17/18-year-old adolescents Fractures (n = 1,099) Adjusted odds ratio 95 % Confidence interval Whites 3.16* 1.89–5.32 Males 1.94** 1.25–2.99 Sibling history of fracture 1.50*** 1.02–2.

Coord Chem Rev doi:10.​1016/​j.​ccr.​2008.​05.​014 Selleckchem LY3023414 Neese F (2008b) Spin Hamiltonian parameters from first principle calculations: theory and application. In: Hanson G, Berliner L (eds) High resolution EPR: applications to metalloenzymes and metals in medicine. Biological magnetic resonance, vol 28. Springer, Berlin, pp 175–232 Neese F, Schwabe T, Grimme

S (2007a) Analytic derivatives for perturbatively corrected “double hybrid” density functionals: theory, implementation, and applications. J Chem Phys 126:124115. doi:10.​1063/​1.​2712433 CrossRefPubMed Neese F, Petrenko T, Ganyushin D, Olbrich G (2007b) Advanced aspects of ab initio theoretical optical spectroscopy of transition metal complexes: multiplets, selleck spin-orbit coupling and resonance Raman intensities. Coord Chem Rev 251:288–327. doi:10.​1016/​j.​ccr.​2006.​05.​019 CrossRef Neese F, Wennmohs F, Hansen A, Becker U (2008) Efficient, approximate and parallel Hartree–Fock and hybrid DFT calculations. A ‘Chain-of-Spheres’

algorithm for the Hartree-Fock exchange. Chem Phys doi:10.​1016/​j.​chemphys.​2008.​10.​036 Neugebauer J, Hess BA (2003) Fundamental vibrational frequencies of small polyatomic molecules from density-functional calculations and vibrational perturbation theory. J Chem Phys 118:7215–7225. doi:10.​1063/​1.​1561045 CrossRef Noodleman L (1981) Valence bond description of antiferromagnetic coupling in transition metal dimers. J Chem Phys 74:5737–5743. doi:10.​1063/​1.​440939 CrossRef Noodleman L, Han WG (2006) Structure, redox, pK(a), spin. A golden tetrad

for understanding metalloenzyme energetics and reaction pathways. J Biol Inorg Chem 11:674–694. doi:10.​1007/​s00775-006-0136-3 CrossRefPubMed Noodleman L, Lovell T, Han WG, Li J, Himo F (2004) Quantum chemical studies of intermediates and reaction pathways in selected enzymes and catalytic synthetic systems. Chem Rev 104:459–508. doi:10.​1021/​Chk inhibitor cr020625a CrossRefPubMed Pantazis DA, Orio M, Petrenko T, Zein S, Bill E, Lubitz W, Messinger J, Neese F (2009) A new quantum chemical approach to the magnetic properties of oligonuclear transition metal clusters: application to a model for the tetranuclear manganese cluster of photosystem II. Chem Eur J. doi:10.​1002/​chem.​200802456 Flucloronide Parr RG, Yang W (1989) Density functional theory of atoms and molecules. Oxford University Press, Oxford Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77:3865–3868. doi:10.​1103/​PhysRevLett.​77.​3865 CrossRefPubMed Ray K, DeBeer George S, Solomon E, Wieghardt K, Neese F (2007) Description of the ground-state covalencies of the bis(dithiolato) transition-metal complexes from X-ray absorption spectroscopy and time-dependent density-functional calculations. Chem Eur J 13:2783–2797. doi:10.​1002/​chem.

Micrographs

learn more are overlays of sequential scans. Scale bar equals 10 μm. In contrast, the growth curve from C. thermocellum showed a long lag phase of approximately 20 h followed by a weak exponential growth phase (Figure 7). Due to the limitation on 36 h, the end of the exponential growth phase and the beginning of the stationary growth phase could not be determined during this experiment. Furthermore, CTC-formazan fluorescence signals could only be determined after 22 h growth time. However, fluorescence signals before a growth time of 22 h were quite low (microscopic data not shown). Thus, the low hybridization rate of C. thermocellum detected by Flow-FISH could have been caused by a low metabolic cell activity and, consequently, by a low 16S rRNA concentration in the cells. The results of both experiments are SU5416 concentration in accordance to further studies [6–8, 37]. Conclusions In this study, a protocol for Talazoparib purification of high heterogenic liquid samples from biogas reactors for the analysis of microbial community by flow cytometry was successfully developed. Furthermore, a Flow-FISH protocol was established to detect process-relevant active microorganisms in biogas reactor samples. The developed

purification procedure (1-C2-S2-H1-F2) is based on the treatment with sodium hexametaphosphate and ultrasound treatment with a final filtration step. We demonstrated that cell aggregates could successfully be suspended and cells were successfully removed from organic or inorganic particles and that these particles were eliminated from the samples using this purification procedure. Moreover, the cell loss due to purification

was negligible. Furthermore, a modified Flow-FISH protocol for analysis of microbial community biogas reactors was successfully adapted in this study. The waiver of dehydration steps decreased the cell loss during procedure but this may also decrease the hybridization rate of some bacteria species. Therefore, the benefit on cell counts by omission of dehydration should be decided from case to case. However, we have selleck chemicals shown that the applied Flow-FISH protocol did not allow cross hybridization determined by use of the nonsense probe NonEUB338. In addition, false positive fluorescence signals caused by background fluorescence or autofluorescence of microorganisms were also excluded by using control hybridizations without any FISH probes. The new developed purification technique in combination with a modified Flow-FISH protocol described in this paper enables for the first time a high throughput analysis of microbial communities in heterogenic samples from biogas reactors focused on the detection of process-relevant, metabolically active microorganisms.

It was assumed that the eccentric load of running led to rhabdomyolysis and therefore to an impaired renal function thus leading to a reduced water TSA HDAC concentration excretion as the reason for the accumulation of total body water. In a recent field study, the changes in body mass and fluid metabolism in Triple Iron ultra-triathletes covering

11.4 km swimming, 540 km cycling and 126.6 km running were investigated [7]. Unlike in a marathon, there is a change in sport disciplines in a Triple Iron ultra-triathlon and there is also a high eccentric stress situation due to the 126.6 km of running at the end of the race. The authors reported a decrease in body mass due to both a reduced fat mass and a reduced skeletal muscle mass but not due to dehydration. Furthermore, the development of oedemata after an ultra-endurance

performance, such as a Triple Iron ultra-triathlon, has recently been described in a case report [8]. These authors described a persistent increase in the total body water within 42 hours after finishing the race. They concluded, that the remarkably higher fluid intake during the race combined with an impairment of renal function buy Navitoclax due to muscle damage led to clinically visible oedemata of the feet, persisting for four days post-race. We may assume that comparable to the study from Milledge et al.[2] describing oedemata at the lower leg during the prolonged exercise of hill-walking, a Triple Iron ultra-triathlon also leads to oedemata at the lower leg. There are several different Phospholipase D1 mechanisms, which might lead to a retention of total body water. Maughan et al.[9] described an increased plasma volume following an increased protein synthesis. Mischler et al.[10] confirmed it in their study measuring the albumin synthetic rates as well as plasma volume and total body water before and after an ultra-endurance trial in six young men. They explained that due to its colloid osmotic properties, albumin mass expansion

is the major driving force for plasma volume expansion. On the Forskolin molecular weight contrary, Lehmann et al.[11] showed that protein catabolism could lead to hypoproteinemic oedemata. A further mechanism was reported by Uberoi et al.[12] describing that skeletal muscle damage with severe rhabdomyolysis could lead to an impaired renal function. Furthermore, due to an increased activity of aldosterone the Na+ retention increases [3] which therefore results in an increase in plasma volume [2, 13]. The quantification of changes in volume of body parts and the development of oedemata is a technical problem. There are different methods described in the literature for quantifying a change in limb volume. Lund-Johansen et al.[14] measured the displaced water by weighing whereas Bracher et al.[15] used plethysmography, which is quite similar to Lund-Johansen et al.[14] method with the difference that using plethysmography the displaced water is quantified as a volume.

Can J Microbiol 1998, Idasanutlin 44:162–167.CrossRef 13. Yashiro E, Spear RN, McManus PS: Culture-dependent and culture-independent assessment of bacteria in the apple phyllosphere. J Appl Microbiol 2011,110(5):1284–1296.PubMedCrossRef 14. Chelius MK, Triplett EW: The diversity of archaea and bacteria in association with the roots of Zea mays L. Microbial Ecol 2001, 41:252–263. 15. Sun L, Qiu F, Zhang X, Dai X, Dong X, Song W: Endophytic bacterial diversity in rice (Oryza sativa L.) roots estimated by 16S rDNA sequence analysis. Microbial Ecol 2007,55(3):415–424.CrossRef 16. Liu W-T, Marsh TL, Cheng H, Forney LJ: Characterization of microbial diversity by determining terminal restriction

fragment length polymorphisms of genes encoding 16 s rRNA. Appl Environ Microbiol 1997,63(11):4516–4522.PubMed 17. Wren JD, Roossinck MJ, Nelson RS, Scheets K, Palmer MW, Melcher U: Plant virus biodiversity

and ecology. PLoS Biol 2006,4(3):e80.PubMedCrossRef 18. Melcher U, Muthukumar V, Wiley GB, Min BE, Palmer MW, Verchot-Lubicz J, Ali A, Nelson RS, Roe BA, Thapa V, Pierce ML: Evidence for novel viruses by analysis of nucleic acids in virus-like particle fractions from Ambrosia psilostachya. J Virol Methods 2008,152(1–2):49–55.PubMedCrossRef 19. Muthukumar V, Melcher U, Pierce M, Wiley GB, Roe BA, Palmer MW, Thapa V, Ali A, Ding T: Non-cultivated plants of the Tallgrass Prairie Preserve of northeastern Oklahoma frequently LY2228820 manufacturer Chlormezanone contain virus-like sequences in particulate fractions. Virus Res 2009,141(2):169–173.PubMedCrossRef 20. Rastogi G, Tech JJ, Coaker GL, Leveau JHJ: A PCR-based toolbox for the culture-independent quantification of

total bacterial abundances in plant environments. J Microbiol Methods 2010,83(2):127–132.PubMedCrossRef 21. Engebretson JJ, Moyer CL: Fidelity of select restriction endonucleases in determining microbial diversity by terminal-restriction fragment length polymorphism. Appl Environ Microbiol 2003,69(8):4823–4829.PubMedCrossRef 22. Culman SW, Gauch HG, Blackwood CB, Thies JE: Analysis of T-RFLP data using analysis of variance and ordination methods: a comparative study. J Microbiol Methods 2008,75(1):55–63.PubMedCrossRef 23. Culman S, Bukowski R, Gauch H, Cadillo-Quiroz H, Buckley D: T-REX: software for the processing and analysis of T-RFLP data. BMC Bioinformatics 2009,10(1):171. supplementary materialPubMedCrossRef 24. Osborn AM, Moore ERB, Timmis KN: An evaluation of terminal-restriction fragment length polymorphism (T-RFLP) analysis for the study of microbial community structure and dynamics. Environ Microbiol 2000,2(1):39–50.PubMedCrossRef 25. Min BE, Feldman TS, Ali A, Wiley G, Muthukumar V, Roe BA, Roossinck M, Melcher U, Palmer MW, Nelson RS: Molecular characterization, click here ecology, and epidemiology of a novel Tymovirus in Asclepias viridis from Oklahoma. Phytopathology 2012,102(2):166–176.

Figure

3 Analysis of antioxidants. A) Activity of SOD; B) GSH-GPx and C) CAT. The results are expressed as the mean + S.E. of 10 animals per group. TCr Anlotinib research buy = Trained Creatine; T = Trained; CCr = Control Creatine; C = Control not trained. * different C; † different CCr; ‡ different T/C. Concentration of reduced glutathione (GSH), oxidized glutathione (GSSG) and ratio between reduced glutathione and oxidized glutathione (GSH/GSSG) in liver Rat liver values for GSH, GSSG and GSH/GSSG ratio at the end of the experiment showed no differences between groups (Figure 4). Figure 4 Concentration of reduced glutathione, oxidized glutathione and ratio reduced glutathione/oxidized glutathione in the liver the animals at the end of the experiment. The results are expressed as the mean + S.E. of 10 animals per group. TCr = Trained Creatine; T = Trained; CCr = Control Creatine; C = Control not trained. Discussion In recent years the use of creatine supplementation (CrS) whith Epoxomicin research buy antioxidant function has increased. Several studies have confirmed these effects and pointed to creatine as a new alternative in the prevention of oxidative stress in which creatine appears to play a crucial role in reducing the toxic effects of endogenous production of reactive oxygen species (ROS) [5, 26–28]. The literature indicates that 2% CrS in animal feed Caspase Inhibitor VI ic50 is able to

trigger a significant increase in phosphocreatine (PCr) and creatine levels in rat tissues [29, 30]. Using this amount of creatine, McMillen et al. [30] observed a significant increase in the total creatine content of rat gastrocnemius muscle in two weeks of supplementation. In the present study, significant increase in the hepatic creatine concentrations were demonstrated in CCr and TCr rats compared to the non-supplemented control groups, which supports prior findings in the literature [30, 31]. After confirming that dietary supplementation increased creatine concentration in rat liver, this study aimed to evaluate the possible

antioxidant effects of CrS in vivo. The results demonstrate that Exoribonuclease creatine exerts indirect antioxidant activity in rat liver, i.e., creatine increased the activity of antioxidant enzymes GSH-GPx and CAT. However, CrS was not effective in normalizing the increased concentrations of H2O2 triggered by exercise. In addition, no significant differences were observed in the concentration of TBARS between groups. H2O2 plays an important role in homeostasis. It participates in cellular induction of gene expression, among which are those genes responsible for antioxidant enzyme synthesis [32–34]. In the present study, we demonstrated that exercise-trained rats (T and TCr) had higher concentrations of H2O2 than sedentary rats (C and CCr). These data reinforce the observations of several authors that indicate that creatine appears to exert selective antioxidant effects [26, 27]. Lawler et al.

Cytoimmunochemistry and Immunohistochemistry 2×105 MHCC97-H and MHCC97-L cell were plated and cultured in six-well plate respectively, when reached to 60% confluent, the cells were fixed with 100% methanol, permeabilized with 0.5% Triton X-100, and sequentially incubated with the primary anti- TGF β1 monoclonal antibodies and anti-mouse

immunoglobulin (Ig) coupled to Horseradish peroxidase (HRP), then, the cells were stained with DAB (3, 3′-diaminobenzidine) and counterstained with hematoxylin. Paraffin-embedded tumor tissues were sliced as 5μm sections in thickness and mounted on glass. Slides were deparaffinated and rehydrated over 10 min through a graded alcohol series to deionized water; 1% Antigen Unmasking Solution (Vector Laboratories) and microwaved were used to enhance antigen retrieval; the slide were incubated with anti-TGF β1 monoclonal antibodies and HRP-conjugated secondary antibody, and then, stained with DAB. ELASA Total protein of all tumor tissues selleck inhibitor were extracted as described above. TGF β1 protein levels in tumors were determined using the Quantikine TGF β1 Immunoassay (R&D, Minneapolis, MN,USA). The operational approach was performed according to manufacture specification. Statistical Selleckchem Adavosertib analysis Statistical analysis was performed using SPSS 11.5 software (SPSS Inc, USA). The data were analyzed by Students’ t test, one-way analysis of variance and covariance analysis. All statistical

tests were two-sided; a P value of less than INCB024360 concentration Non-specific serine/threonine protein kinase 0.05 was considered statistically

significant. Results The tumor weight and pulmonary metastatic rate The tumors of MHCC9-H model grew fast than that of MHCC97-L, and especially in early stage of tumor formation, MHCC9-H spent shorter time (days) than MHCC97-L getting to the size of 500mm3 (21.93±3.67 vs. 30.83±1.94, P<0.001) (Figure 1A), however, the growth speed became similar from the size of 500mm3 to 1500 mm3 (9.00±2.69 vs.10.83±1.47, P=0.14 ) (Figure 1B). MHCC9-H model had bigger pulmonary metastatic loci than MHCC97-L model (Figure 1C,D). The mean tumor weight (g) in MHCC9-H and MHCC97-L were 1.75±0.75 and 1.26±0.51, and the pulmonary metastatic rate were 55% and 36.36%; and the average number of metastatic cell in lung were 119.25±177.39 and 43.36±47.80 respectively (Table 1). Figure 1 Comparison of Growth and pulmonary metastsis in mice models. A) Growth curve of MHCC97-H and MHCC97-L models; B) Average days which were spent for getting to tumor size. * denoted P<0.05, Error bar represent the standard errors of the mean. C,D) MHCC97-L models (C) had smaller pulmonary metastatic loci than MHCC97-H models (D). Arrows denote metastatic loci. Table 1 The tumor weight and pulmonary metastasis rate in different nude mice models of HCC Models No. of cases Tumor weight(g) (Mean±SD) Metastatic rate No. of Metastatic cells (Mean±SD) MHCC97-L 11 1.26±0.51 36.36% (4/11) 46.36±47.80 MHCC97-H 20 1.75±0.75 55.00% (11/20) 119.25±177.39 SD=standard deviation.

To assess the sensitivity of the RCA-based assay, RCA was initially performed on 10-fold serial dilutions of the target template (PCR product; see Methods) ranging from 1011 to 100 copies of template. For all isolates studied, a clear RCA fluorescence signal was observed with a sensitivity of detection of 109 copies; below this copy number, the signal was not easily distinguishable from the background signal (as defined when amplifying target template that did not have the mutation of interest) (Figure 2). Only signals that were clearly measurable above background were considered

to be indicative of the presence of the mutation. Figure 2 Sensitivity of the RCA assay. RCA was performed on 10-fold ISRIB research buy serial dilutions of the target template ranging from 1011 to 100 copies of target template (PCR product). The figure

illustrates the RCA reaction using the TPX-0005 order Ca-Y132H-specific probe to OSI744 detect 1011, 1010 and 109 copies of the template containing the Y132H mutation (obtained from amplifying DNA from isolate C594). RCA signals are shown as exponential increases in florescence signal above baseline (indicated by the “”negative signal”" label and defined as the signal obtained when amplifying target template that did not have the mutation of interest). The intensity of the signal weakened with decreasing copy numbers starting at 1011copies and the sensitivity of the assay corresponded to a concentration of 109copies of target

template. The capability of the RCA assay to detect heterozygous, as well as homozygous ERG11 nucleotide changes was assessed RANTES indirectly by testing its ability to detect a specific mutation in the presence of wild-type template (ie. template without the mutation of interest) using the eight “”reference”" isolates. For each of the known ERG11 mutations (Table 1), target template (1011 copies) containing the mutation at 100%, 50%, 20%, 10%, 5%, 2% and 0% concentration in a backdrop of wild-type template were prepared by mixing both templates at the above-mentioned ratios. In all cases, a clear RCA signal above background was observed down to a dilution containing 5% target template (Figure 3); results were reproducible with minimal or no variation in repeat (n = 3) experiments. The results demonstrate that the RCA assay was able to detect ERG11 mutations with high sensitivity in the presence of mixtures of DNA and that the sensitivity was well above that required to detect heterozygous nucleotide changes (expected ratio of target template (with mutation) to template without mutation of 1:1)). Figure 3 Sensitivity of the RCA assay in the presence of DNA mixtures. The accumulation of double-stranded DNA was detected by staining with Sybr green I.