aureus in the rats with only the Selleck Epoxomicin established strain (- pulse). For MK-2206 manufacturer S. aureus the bacterial density does not exceed that observed in rats without a pulse and the resident strain has a competitive advantage. Figure 3 Pulse on established populations of same species. Established populations were inoculated into 3-day-old neonatal rats 48 hours prior to pulsing 104 cfu of a marked strain of the same species or PBS. The total bacterial density in nasal epithelium of 6-8 rats with the established

and pulsed population (dark grey) and just the established population (light grey) were tracked over 96 hours after the pulse and expressed as the geometric mean with error bars indicating SE. In addition, the percent of the bacterial density that is pulsed is marked with points with dotted error bars indicating SE. Antibiotic marked strains were switched to be either pulsed or established for H. influenzae (in A and B), S. aureus (in C and D) and S. pneumoniae (in E and F). For both S. pneumoniae and H. influenzae there is an increase in the total density in the rats with the pulse (+ pulse) compared to rats with only the established strain (as shown in representative experiments in Figure 3C-F). We saw the bacterial load increase to varying degrees, more so for H. influenzae than for S. pneumoniae, in each of four replicate experiments (data available upon request). In both of these species, we observe that the pulsed

and resident strains co-exist with the pulse strain becoming 25-90% of the population. For all the species, similar pulse results Pritelivir supplier were obtained in reciprocal experiments (switching pulse and resident strains)

confirming that the results were not due to fitness differences in the antibiotic marked strains. Invasion of Different Species in a Colonized Host Competition between different strains or species can be defined simply as a reduction in the density of one or both strains when both are present. Competition within the same species and particularly in the case of the same strain (as in the above pulse experiment) is usually mediated through a limiting shared resource. Competition between species, in addition to partitioning of a shared resource, can be mediated through inhibitory agents/toxins Rebamipide (allelopathy) or predators (in this case components of the immune system [23]). Previous studies suggest that production of hydrogen peroxide by S. pneumoniae may affect the densities of other species [24, 25] and that immune-mediated competition reduces S. pneumoniae density in the presence of H. influenzae [26]. To evaluate the contributions of these different competitive mechanisms we performed invasion experiments (with one strain of each species: Eagan, TIGR4 and PS80) in which one species was resident and a second was introduced (an invader). Evidence for synergistic interactions between H. influenzae and S. pneumoniae or S.

1995), where a short-lived charge-transfer state is created before the subsequent electron-transfer processes take place. This picture is consistent with the so-called multimer models (Durrant et al. 1995; AZD2014 cost Jankowiak et al. 2002; Prokhorenko and Holzwarth 2000). Other models for energy transfer and charge separation in PSII, based on decoupled pigments with monomeric absorption, have also been reported (Diner and Rappaport 2002). A discussion on the nature of P680

and the relation to a far red-absorbing (700–730 nm) complex that induces charge separation in intact O2-evolving PSII RCs, can be found in Hughes et al. (2005, 2006b), Krausz et al. (2008, and references therein) and Peterson-Årsköld et al. (2004). Foretinib supplier Time-resolved HB experiments were performed, in PF-6463922 price our laboratory, in red-absorbing pigments of the isolated PSII sub-core complexes that act as ‘traps’ for energy transfer, i.e. in pigments characterized by a fluorescence decay time of a few

nanoseconds and therefore yielding narrow holes. In the presence of SD, the holes broaden with delay time t d, the time between burning and detecting the hole. From such holes, the ‘effective’ homogeneous linewidth \( \Upgamma_\hom ^’ (t_\textd ) \) is determined, which reflects the occurrence of time-dependent conformational changes Metformin solubility dmso in the protein or glassy host. \( \Upgamma_\hom ^’ (t_\textd ) \) can be expressed as: $$ \Upgamma_\hom ^’ \;(T,t_\textd )\; = \;\frac12\,\pi \,T_1 \; + \;\frac1\pi \,T_2^* \left( T,t_\textd \right) = \Upgamma_0 \; + \;\left( a_\textPD

\; + \;a_\textSD (t_\textd ) \right)\;T^1.3\, , $$ (3)where in the absence of energy transfer, Γ0 is determined by the fluorescence lifetime τ fl, Γ0 = (2πτ fl)−1 (see Creemers and Völker 2000; Den Hartog et al. 1999b; Koedijk et al. 1996; Silbey et al. 1996; Wannemacher et al. 1993). The last term in Eq. 3 consists of two contributions: a ‘pure’ dephasing contribution a PD T 1.3 (always present) that accounts for fast fluctuations of the optical transition within the lifetime of the excited state of a few ns, and a delay-time-dependent contribution determined by spectral diffusion a SD (t d) T 1.3 that increases with t d. Hence, following from Eq. 3: $$ a_\textSD (t_\textd )\; = \;\frac\Upgamma_\hom ^’ (t_\textd )\; – \;\Upgamma_0 T^1.3\, \; – \;a_\textPD , $$ (4)where the functional dependence of the coupling constant a SD on delay time t d yields the distribution P(R) of relaxation rates R in the protein (see below and Fig. 7). Fig. 7 Coupling constant a SD of spectral diffusion (SD) as a function of the logarithm of the delay time between burning and probing, t d.

Ofek et al.[19] proposed that resistance to novobiocin in Gram-negative enteric bacteria is probably due to the inability of the antibiotic to penetrate the outer membrane. Based on this, Vaara and Vaara [20] used the sensitization of S. Thypimurium to novobiocin as an indicator of outer membrane permeability changes in the presence of cationic agents. In a similar

manner, we studied if the S. Thypimurium resistance to novobiocin was circumvented CYT387 supplier by growing bacteria in acidic pH condition. To this end, we determined CFU mL-1 at different times after exposure to novobiocin (see Methods). As expected, we observed that 0.15 μM novobiocin did not affect S. Thypimurium growth at neutral pH whereas at pH 4.7, the antibiotic reduced 90% of colony counts after 24 h of incubation (Figure 5). Taken together, our results suggest that low pH incubation modifies the outer membrane permeability, allowing the entry of MccJ25 and novobiocin into the cell. Figure 5 Effect of low pH on the sensitivity of S. Typhimurium to novobiocin. 106 mL-1 cells of S. Typhimurium 14028s strain in M9 medium pH 7 (grey bars) or pH 4.7 (black bars) were treated with 0.15 μM novobiocin or sterile see more bidistilled

water as control. CFU mL-1 was determined after 0, 6 and 24 h of incubation at 37°C. Results are expressed as percentage of surviving bacteria to novobiocin relative to the control in the absence of the antibiotic. Error bars represent standard deviations from five different experiments. As a mean of simulating internal macrophage conditions, antibiotic sensitivity assays were carried out in M9 medium without nutrient supplementation. However, we considered interesting to evaluate the low pH effect on the sensitivity of S. Thypimurium to

MccJ25 and novobiocin when bacteria are cultured in a medium that allows bacterial growth. The S. Thypimurium viability upon antibiotic treatment was estimated by calculating CFU mL-1 after 24 pheromone h of incubation in M9 medium (pH 4.7) supplemented with 0.2% glucose, 0.2% casamino acids and 10 μM MgSO4. In fact, compared with the control (no antibiotic added), surviving bacteria were 0.0001 and 0.1% for cultures treated with MccJ25 and novobiocin, respectively (Data not shown). Since bacterial physiology is radically different in actively growing cultures compared with cultures in non-supplemented minimal medium, the observation of the low pH effect in both conditions strengthen the idea that low pH is a determinant feature in turning resistant bacteria to MccJ25 and novobiocin into sensitive ones. In summary, these results present evidence that the previously reported resistance of S. Thypimurium to MccJ25 and novobiocin, produced by the inability of the buy CB-839 antibiotics to penetrate the bacterial outer membrane [9, 19], could be overcome when cells are exposed to low pH. Conclusions In the present work we demonstrated that MccJ25 has an inhibitory effect on the intracellular replication of an in vitro MccJ25-resistant strain of S.

01) (Table  3). Dietary HC effect was not obtained in femoral length both among the 20% protein selleck chemicals llc groups and the 40% protein groups. Table 3 Femoral

weights and length   20% protein Two-way ANOVA (p value) 40% protein Two-way ANOVA (p value)     Exercise Collagen Interaction   Exercise Collagen Interaction Wet weight (g)                 Collagen(-) EX(-) 0.9860 ± 0.0010 0.189 0.116 0.888 1.0127 ± 0.0206 0.326 0.570 0.271 EX(+) 0.9633 ± 0.0290 0.9712 ± 0.0107 Collagen(+) EX(-) 1.0191 ± 0.0215 1.0020 ± 0.0159 EX(+) 0.9910 ± 0.0145 1.0044 ± 0.0319 Wet weight (g/100g Body Wt.)                 Collagen(-) EX(-) 0.2434 ± 0.0026 <0.001 0.006 0.633 0.2461 ± 0.0045 <0.001 0.001 0.191 EX(+) 0.2796 www.selleckchem.com/products/AZD8931.html ± 0.0077 0.2772 ± 0.0037 Collagen(+) EX(-)

0.2605 ± 0.0032 0.2560 ± 0.0035 EX(+) 0.2918 ± 0.0057 0.2988 ± 0.0066 Dry weight (g)                 Collagen(-) EX(-) 0.6363 ± 0.0088 0.013 0.152 0.540 0.6401 ± 0.0126 0.327 0.207 0.508 EX(+) 0.6031 ± 0.0110 0.6202 ± 0.0075 Collagen(+) EX(-) 0.6450 ± 0.0142 0.6475 ± 0.0082 EX(+) 0.6247 ± 0.0088 0.6436 ± 0.0199 Dry weight (g/100g Body Wt.)                 Collagen(-) EX(-) 0.1570 ± 0.0021 0.001 <0.001 0.851 0.1556 ± 0.0028 <0.001 <0.001 0.365 EX(+) 0.1751 ± 0.0027 0.1769 ± 0.0021 Collagen(+) EX(-) 0.1649 ± 0.0021 0.1654 ± 0.0016 EX(+) 0.1838 ± 0.0028 0.1915 ± 0.0040 Ash weight (g)                 Collagen(-) EX(-) 0.3981 ± 0.0109 0.193 0.572 0.686 0.4040 ± 0.0125 0.726 0.442 0.751 EX(+) 0.3793 ± 0.0117 0.3972 ± 0.0037 Collagen(+) EX(-) 0.3998 ± 0.0128 selleckchem 0.4086 ± 0.0071 EX(+) 0.3899 ± 0.0108 0.4083 ± 0.0175 Ash weight (g/100g Body Wt.)                 Collagen(-) EX(-) 0.0982 ± 0.0016

<0.001 0.095 0.896 0.0982 ± 0.0027 <0.001 0.005 0.688 EX(+) 0.1101 ± 0.0026 0.1134 ± 0.0024 Collagen(+) EX(-) 0.1022 ± 0.0016 0.1044 ± 0.0012 EX(+) 0.1147 ± 0.0034 0.1215 ± 0.0034 Ash weight (g/Dry weight)                 Collagen(-) EX(-) 0.6252 ± 0.0069 0.553 0.396 0.985 0.6310 ± 0.0033 0.223 0.577 0.540 EX(+) 0.6287 ± 0.0042 0.6413 ± 0.0094 Collagen(+) EX(-) 0.6200 ± 0.0044 0.6313 ± 0.0038 EX(+) 0.6237 ± 0.0083 0.6347 ± 0.0037 Length (cm)                 Collagen(-) EX(-) 3.710 ± 0.014 0.004 PDK4 0.216 0.109 3.696 ± 0.015 0.084 0.851 0.082 EX(+) 3.623 ± 0.023 3.646 ± 0.009 Collagen(+) EX(-) 3.699 ± 0.017 3.668 ± 0.010 EX(+) 3.675 ± 0.018 3.669 ± 0.023 Long Width (cm)                 Collagen(-) EX(-) 0.440 ± 0.005 0.848 0.266 0.722 0.441 ± 0.005 1.000 0.035 0.339l EX(+) 0.438 ± 0.004 0.436 ± 0.003 Collagen(+) EX(-) 0.444 ± 0.006 0.446 ± 0.005 EX(+) 0.445 ± 0.005 0.451 ± 0.006 Short Width (cm)                 Collagen(-) EX(-) 0.352 ± 0.004 0.169 0.328 0.591 0.348 ± 0.005 0.121 0.385 0.746 EX(+) 0.345 ± 0.003 0.344 ± 0.002 Collagen(+) EX(-) 0.346 ± 0.004 0.353 ± 0.003   EX(+) 0.343 ± 0.003       0.346 ± 0.005       Values are expressed d as means ± SE.

This study examined INCB28060 molecular weight the efficacy of several factors impacting long-term renal survival, such as gender, age, therapeutic option, and dialysis induction risk according to the new domestic CGJ-IgAN. Multivariate analysis was used for this study. Materials and methods Patients Between December 1986 and July 2009, 303 patients were diagnosed with IgAN by renal biopsy at Fujita Health University and its affiliated hospitals. The diagnosis of IgAN was based on predominant mesangial IgA staining shown on immunofluorescence study. Patients with

systemic diseases such as diabetes mellitus, systemic lupus erythematosus, abnormal hypergammaglobulinemia, chronic liver diseases, and Henoch-Schönlein purpura were distinguished from IgAN by clinico-pathological features. Among IgAN patients, the following patients were excluded from this study: (1) age <15 years, (2) insufficient number of glomeruli (<7 glomeruli) in a biopsy specimen for light microscopic study, (3) follow-up period <18 months, (4) patients who showed a combination with other systemic diseases (antineutrophil cytoplasmic antibodies-associated vasculitis, systemic lupus erythematosus, malignancy) during an observation period, or (5) incomplete data in the medical records. As a result, 208 of the 303 patients were included in this study (Fig. 1). Fig. 1 Enrollment of study patients. Detailed list

of reasons for exclusion see more of patients This study complied with the Helsinki declaration and was approved by the Ethics Committee of Fujita Health University (approval number 11–130). Clinical, laboratory, and pathological analyses The baseline data at the time of renal biopsy were compiled from medical records. The time of renal biopsy was regarded as

the entry time into the follow-up. The clinical data evaluated included gender, age, and receiving ACEIs or ARBs. The laboratory data were also evaluated, and included serum creatinine, estimated glomerular filtration rate (eGFR), and degrees of proteinuria and hematuria at (a) the time of renal biopsy, (b) the end of steroid pulse therapy, (c) the end of administration of prednisolone, and (d) the final observation time. The qualitative findings of hematuria were converted into scores as Cobimetinib (−) to 0, (±) to 1, (1+) to 2, (2+) to 3, and (3+) to 4. The Screening Library histological findings were classified according to the new histological classification of IgAN in CGJ-IgAN. The classification details are shown in Tables 1, 2, 3. The names of the patients were blinded to all evaluations of baseline data from renal biopsies. Stratification of dialysis induction risk Predictive grading of dialysis induction risk in the CGJ-IgAN was defined by stratification of the two grades of clinical and histological severities. The clinical severities were graded by the levels of urinary protein (UP g/day) and eGFR (ml/min/1.73 m2) at the time of renal biopsy. Clinical grades (C-G I–III) were defined as C-G I, UP < 0.5; C-G II, UP ≥0.

Twenty six F. tularensis type A (20 A1 and 6 A2), thirteen F. tularensis type B and one F. novicida strain were used for phylogenetic SNP analysis and identification of high-quality SNPs for use as typing markers. Based on our global analysis of 40 genomes, we were able to identify

a www.selleckchem.com/products/qnz-evp4593.html series of SNPs at various levels of hierarchy. We used these SNPs to develop and validate a low-cost PCR-based assay for typing and discriminating F. tularensis isolates. Methods Francisella strains Francisella strains used for whole genome sequencing https://www.selleckchem.com/products/pri-724.html are listed in Table 1. Strains used for evaluation of diagnostic SNP markers are shown in Table 2. All strains were identified as either type A or type B by glycerol fermentation or PCR. Pulsed field gel electrophoresis using PmeI was performed for CDC strains to characterize type A strains as either A1, A2, A1a or A1b [14]. Ribotyping, using the Dupont Qualicon RiboPrinter and PvuII restriction enzyme, was used to characterize USAMRIID type A strains as A1 or A2 (USAMRIID,

unpublished method). Table 1 Francisella strains resequenced in the study S. No. Isolate Species/Subspecies Cladea Other strain name Geographic Source Year isolated Source 1 SCHUS4 mTOR target F. tularensis type A A1 (A1a)   Ohio 1941 CDC 2 MA00-2987 F. tularensis type A A1 (A1b)   Massachusetts 2000 CDC 3 AR01-1117 F. tularensis type A A1 (A1b)   Arkansas 2001 CDC 4 KS00-1817 F. tularensis type A A1 (A1a)   Kansas 2000 CDC 5 OK00-2732 F. tularensis type A A1 (A1b)   Oklahoma 2000 CDC 6 FRAN005 F. tularensis type A A1   Illinois 1990 USAMRIID 7 FRAN006 F. tularensis type A A1   Illinois 1988 USAMRIID MycoClean Mycoplasma Removal Kit 8 FRAN007 F. tularensis type A A1   Illinois 1988 USAMRIID 9 FRAN008 F. tularensis type A A1   Illinois 1988 USAMRIID 10 FRAN009 F. tularensis type A A1   Illinois 1988 USAMRIID 11 FRAN010 F. tularensis type A A1   Illinois 1987 USAMRIID 12 FRAN011b F. tularensis type A A1   Illinois 1984 USAMRIID 13 FRAN014 F. tularensis type A A1   Illinois 1989 USAMRIID 14 FRAN015 F. tularensis type A A1   Illinois 1988 USAMRIID 15 FRAN023

F. tularensis type A A1 FoxP1 Ohio 1940 USAMRIID 16 FRAN026 F. tularensis type A A1 Schu-SOO Unknown Unknown USAMRIID 17 FRAN030 F. tularensis type A A1 SOL Unknown Unknown USAMRIID 18 FRAN031 F. tularensis type A A1 SCHERM Ohio 1944 USAMRIID 19 FRAN032 F. tularensis type A A1 GREU Ohio Unknown USAMRIID 20 FRAN033 F. tularensis type A A1 HUGH Ohio 1940 USAMRIID 21 WY96-3418 F. tularensis type A A2   Wyoming 1996 CDC 22 CA02-0099 F. tularensis type A A2   California 2002 CDC 23 UT02-1927 F. tularensis type A A2   Utah 2002 CDC 24 FRAN001 F. tularensis type A A2 38 derivative (ATCC 6223) Utah 1920 (?) USAMRIID 25 FRAN027 F. tularensis type A A2 38A (38 derivative) Utah – USAMRIID 26 FRAN028 F. tularensis type A A2 Larsen NIH38 (38 derivative) Utah – USAMRIID 27 LVS F. tularensis type B     Russia 1958 (?) CDC 28 KY99-3387 F. tularensis type B     Kentucky 1999 CDC 29 OR96-0246 F.

Such an approach requires that goals and plans for evaluations are incorporated into the construction schedule. Step 5: Determine sampling scheme Several key questions Selleckchem Geneticin related to data collection should now be addressed: (1) How long should sites be monitored before and after road mitigation? (2) How often should sites be monitored? (3) How many Quisinostat cost replicates are needed? As these decisions are unlikely to be independent, we recommend conducting model-based power analyses to optimize the sampling design (see, e.g., van der Grift et al. 2009b). For example, Fig. 2 illustrates the relationship between mitigation

effectiveness (the expected effect size) on the degree of temporal replication needed for adequate statistical power. Similar graphs can be produced for other design variables such as sampling frequency and the number of replicate sites. Note that either pilot studies or pre-existing data on anticipated effect sizes are needed to conduct this type of analysis. Fig. 2 Hypothetical AG-881 in vivo relation between the probability of detecting an effect of road mitigation and the duration of monitoring after the mitigation measures are put in place. The three scenarios illustrate variations in the expected effectiveness of mitigation, e.g. road mitigation is expected to reduce the

road effect by 100, 75 or 50 %. The figure shows that if we want to achieve statistical power of 80 % we should measure the response variable for 3, 6 and 12 years in scenarios 1, 2, and 3, respectively. This figure assumes that the effect of the mitigation measure on the population is

immediate. However, response IKBKE times of the population to both the road and the mitigation measures also have to be considered The sampling scheme is related to the chosen measurement endpoint and the characteristics of the studied species. For example, for a highly mobile species with a long lifespan, monitoring over a longer period would be required to assess a change in population density than that required to detect a change in movement. Similarly, a shorter monitoring period would be required to assess a change in road-kill numbers for a species that crosses roads frequently than for a species that crosses roads infrequently. For some measurement endpoints, such as changes in population size/density, higher levels of replication will allow a quicker evaluation of effectiveness. A study with three replicates will need to be continued for longer than a study with ten replicates, because with more replication the uncertainty in effect size will be reduced, thus allowing a reliable decision to be reached sooner. The rate of use of wildlife crossing structures often increases over time (e.g., Clevenger and Waltho 2003; Ford et al. 2010) due to habituation or gradual improvement in the quality of the crossing structure (e.g., vegetation succession on wildlife overpasses).

pseudomallei 1026b. Despite these differences, our data constitute independent proof of the role of BpaC as an adhesin. These results are substantiated by showing that expression of BpaC on the surface of recombinant E. coli bacteria increases adherence to NHBE, A549

and HEp-2 cells (Figure  2). Given the phenotype of mutants in assays with NHBE cultures and that adherence is a key step in pathogenesis by most infectious agents, we expected the bpaC mutation to reduce the virulence of B. mallei and/or B. pseudomallei in a mouse model of aerosol infection. However, the results of our animal experiments indicate that the mutants are as virulent as wild-type strains (Table  2). Presumably, other adhesins expressed by the bpaC mutants provide sufficient adherence to the murine selleck chemicals airway mucosa to allow colonization at wild-type levels

and for the normal course of disease to ensue. It is unlikely that the lack of phenotype we observed in vivo is due to non-expression of BpaC. Though we discovered that B. pseudomallei DD503 and B. mallei ATCC 23344 do not produce detectable amounts of BpaC under routine laboratory growth conditions, ELISA with sera from mice that survived acute aerosol infection with the agents show that animals produce Abs against the protein (Figure  4A and B). Moreover, sera from horses with experimental glanders have been shown to contain high antibody titers against BpaC [70]. These Aurora Kinase inhibitor results are particularly significant as horses are the natural host and reservoir for B. mallei and arguably the most relevant surrogate to study glanders. Together, these data demonstrate that BpaC is expressed in vivo and elicits the production of Abs during infection. The infection model we used to examine the effect of the bpaC mutation might have impacted the outcome of virulence experiments. This hypothesis is supported by the

Campos et al. study in which they show that the bpaC mutation reduces the ability of B. pseudomallei strain 340 to disseminate and/or survive in the liver [51]. Chorioepithelioma In these experiments, BALB/c mice were infected intranasally with 500 CFU of the agent and bacterial loads in tissues were determined 48 hours post-infection. In contrast, we see more inoculated BALB/c mice intratracheally using a Microsprayer®, which nebulizes bacteria directly into the lungs, infected animals with doses ranging from 102 to 105 CFU, and determined bacterial burden in survivors 6–10 days post-infection (Table  2). It is also known that the choice of bacterial strains [71], inoculation route [72], and animal background [73] can significantly affect the course of disease by B. pseudomallei and B. mallei. For example, the LD50 value of the same B. pseudomallei isolate has been shown to differ by several orders of magnitude in C57BL/6 mice and BALB/c mice [74]. Therefore, a complete understanding of the role of BpaC in pathogenesis may require the use of multiple infection models.

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A, Citro G, Molinari A: Ultrastructural modifications of cell membranes induced by “”electroporation”" on melanoma xenografts. Micr Res Tech 2007, 70: 1041–1050.CrossRef 34. Spugnini EP, Dragonetti E, Vincenzi B, Onori N, Citro G, Baldi A: Pulse mediated chemotherapy enhances local control and survival in a spontaneous canine mucosal melanoma model. Melanoma Res 2006, 16: 23–27.CrossRefPubMed 35. Spugnini EP, Filipponi M, Romani L, Dotsinsky I, Mudrov N, Baroni A, Ruocco E, Laieta MT, Montesarchio V, Cassandro R, Citro G, Baldi A: Local control and distant metastases after electrochemotherapy of a canine anal melanoma. In Vivo 2007, 21: 897–900.PubMed 36. Spugnini EP, Dotsinsky I, Mudrov N, Cardosi G, Citro G, Baldi A: Biphasic pulses enhance bleomycin efficacy in a spontaneous canine perianal tumors model. J Exp Clin Cancer Res 2007, 26: 483–487.PubMed 37. Spugnini EP, Citro G, Mellone P, Dotsinsky I, Mudrov N, Baldi A: Electrochemotherapy for localized lymphoma: a preliminary study in companion animals. J Exp Clin Cancer Res 2007, 26: 343–346.PubMed 38.