From the sequence alignment of GadX binding sites on btuB, gadA,

From the sequence alignment of GadX binding sites on btuB, gadA, and gadBC regulatory regions[42], we found that sequence in the region I (the 31 nucleotides) has 62.5% identity (+52-AGCGGTAAGGAAAGGTGCGATGATTGCGTTAT-+82, underlined nucleotides indicate the protected region) with gadBC and sequence in the region III (the 26 nucleotides) has 60.7% identity (+106-AAGTCATCATCTCTTAGTATCTTAGATA-+133, underlined nucleotides indicate the protected region)

with gadA regulatory region. From the footprinting result, the GadX binding sites on 5′ untranslated region of btuB share only partial homology with the 42 nucleotides consensus sequence which was reported by Tramonti et. al.[42]. #Doramapimod in vivo randurls[1|1|,|CHEM1|]# The sequence analysis also revealed the btuB expression was regulated by the binding of GadX on its 5′ untranslated region. Binding of transcriptional regulator to the 5′ untranslated region to regulate gene expression is also seen in the glp regulon of E. coli, in which four repressor binding sites are located at -41 to -60, -9 to -28, +12 to -8, and +52 to +33 of the glpACB genes MK-8931 supplier [43]. In addition, two

IHF binding sites are present downstream from the glpT transcriptional start site at positions +15 to +51 and +193 to +227 [44]. In the btuB promoter assay experiment, different lengths of DNA fragments containing btuB promoter were fused to lacZ. The minimum length of DNA fragment with btuB promoter activity was 461 bp spanning -219 to + 242 nucleotides relative to the translation initiation site of btuB. No significant difference in promoter activity was observed when the 5′ end of these fragments was extended to -671. However, a 6 fold (37.5 vs. 6.4 β-galactosidase units, Table 2) increase in promoter activity was detected when the DNA fragment was extended to -1043 with a total length of 1,285 bp as compared to that of the 461-bp fragment. It is very likely that a certain transcription regulator binds to the region between -1043 and -671 and enhances the expression of btuB. The β-galactosidase activity in these assays

was not very high because the lacZ fusions were constructed ZD1839 cost using the single copy plasmid vector pCC1Bac™ (Epicentre). The purpose of using the single copy number plasmid in this experiment was to mimic the natural state of btuB expression in E. coli. In fact, the promoter activity of btuB is lower than other membrane protein, we have determined the ompC promoter activity, under the same test condition the Miller’s Units of lacZ driven by ompC promoter is 8 folds higher than that of btuB (data not shown). Although the results of footprinting and reporter assay revealed that the GadX binding sites on btuB 5′ untranslated region share only partial homology with the GadX binding consensus sequence[42] and showing 50% down regulation in the reporter assay, the expression of btuB was indeed controlled by GadX.

Source control is a broad term encompassing all measures undertak

Source control is a broad term encompassing all measures undertaken to eliminate the source of infection and control buy BIBW2992 ongoing

contamination [2]. The most common source of infection in community-acquired intra-abdominal infections is the appendix, followed by the colon, and then the stomach. Dehiscence complicates 5–10% of intra-abdominal bowel anastomoses and is associated with an increased mortality rate [3]. Antimicrobial therapy plays an integral role in the management of intra-abdominal infections; empiric antibiotic therapy should be initiated as early as possible. Bacterial antibiotic resistance has become a very prevalent problem in treating intra-abdominal infections, yet despite this elevated resistance, the pharmaceutical industry has surprisingly few new antimicrobial agents currently in development. In the last decade, the increased emergence of multidrug-resistant (MDR) bacteria, such as extended-spectrum beta-lactamase (ESBL)-ACY-1215 producing Enterobacteriaceae, Carbapenem-resistant AZD1390 Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Vancomycin-resistant Enterococcus, and Methicillin-resistant Staphylococcus aureus, has foreshadowed a troubling trend and become an issue of key concern in the medical community regarding the treatment of intra-abdominal

infections. In the specific context of intra-abdominal infections, ESBL-producing Enterobacteriaceae pose the greatest resistance-related problem. Today these pathological microorganisms are frequently found in both nosocomial and community-acquired IAIs. The recent and rapid spread of serine carbapenemases in Klebsiella pneumoniae (KPC) has become an important issue concerning antimicrobial therapy in hospitals worldwide and is of primary importance in properly optimizing the use of carbapenems based on a patient’s indication and exposure criteria [4]. Study design The purpose of the CIAO Study is to describe the epidemiological, clinical, microbiological, and treatment profiles Lumacaftor datasheet of community-acquired and healthcare-associated complicated intra-abdominal

infections (IAIs) based on the data collected over a six-month period (January 2012 to June 2012) from 66 medical institutions (see Figure 1) across Europe. This preliminary report overviews the findings of the first half of the study, which includes all data from the first three months of the six-month study period. Figure 1 Geographic distribution of the CIAO study. Patients with either community-acquired or healthcare-associated complicated intra-abdominal infections (IAIs) were included in the study. In each treatment center, the center coordinator collects and compiles the data in an online case report database. The collected data include the following: (i) patient and disease characteristics, i.e.

BMD measurements and cross-calibration

BMD measurements and cross-calibration Femoral neck, total hip, and total lumbar spine BMD (gram per square centimeter) were measured using Hologic QDR 4,500-W densitometer (Hologic Inc, Bedford, MA) in the MrOS Study, the MrOS Hong Kong Study, and the Tobago Bone Health

Study and using Lunar Prodigy (GE, Madison, WI) in the Namwon Study. All BMD scans were conducted using standardized procedures following the manufacturer’s recommended protocols. All DXA operators in each study were trained and certified. Longitudinal quality control was performed daily with a spine phantom and showed no shifts or drifts in each study site. From 2002 to 2005, by the Musculoskeletal and Quantitative Imaging Research Group at the University of California, San Francisco (UCSF), cross-calibration studies were carried out using the Hologic spine, femur, and block phantoms for the scanners used in the MrOS Study (US sites; 2000), the MrOS Hong Kong Study (2002), and the Tobago Bone Health Study (2004). For this analysis, UCSF also carried out a cross-calibration procedure in 2008 using the same phantoms for the scanner of the Namwon Study. Since the sites included Lunar and Hologic scanners, BMD parameters were standardized (converted

to sBMD) according to the formula published by Hui et al. [23]. Corrections for any statistically significant differences across scanners were GDC-0973 mw then applied to participant spine, total hip, and femoral neck BMD values. BMD values for participants at the six US sites and Hong Kong sites, but not in Tobago or Korea, were also corrected for longitudinal shifts, based on Hologic spine phantom scanned during the visit on each Nabilone densitometer. Details on the cross-calibration procedure were as follows. Phantom scans were scanned five times each on the same day and were analyzed centrally by the same research assistant (MrOS, MrOS Hong Kong, Tobago) or locally (Korea) for each DXA scanner. To avoid edge effects, subregional analyses were used by UCSF to

analyze all block phantom scans. One MrOS US site was considered the reference site. The phantom BMD results were first converted to sBMD [23]. In order to derive the linearity of each machine, linear selleck kinase inhibitor regression was used in analyzing the block phantom results. The ratio between the study site and the reference site (reference site/measurement site) for sBMD was then calculated. ANOVA with a Dunnet test was applied to determine the mean sBMD difference between the study site and the reference site. If the sBMD for a study site was significantly different from the reference site, the ratio was used as the cross-calibration factors for each specific scan type. Otherwise, the cross-calibration factor was set to 1.

This manifests as a negative correlation between the difference i

This manifests as a negative correlation between the difference in cell elongation rate and the difference in interdivision intervals between two sisters (inserts JNK-IN-8 mouse Figure 3c and 3d; see also Additional File 13 – Figure S5). This is consistent with the interpretation that, during YgjD depletion, the timing of cell division remained coupled to a given cell size – and that the target cell size declined. The transition to decreased cell size is reminiscent of morphological changes that occur during the ‘stringent response’ [24, 25],

a stress adaptation program that is elicited when cells encounter amino-acid or carbon-starvation [26]. The stringent response is induced by accumulation of the ‘alarmone’ guanosine tetra/penta phosphate ((p)ppGpp), e.g. in Angiogenesis inhibitor response to low concentrations of amino-acylated tRNAs [26]. We thus wanted to investigate this possible link to (p)ppGpp signaling more closely, and asked whether the changes in cell homeostastis upon YgjD depletion are mediated through (p)ppGpp. Changes in cell size homeostastis are mediated through ppGpp We constructed a strain, TB84, that is deficient in (p)ppGpp synthesis ((p)pGpp0), due to deletions of relA and spoT [26, 27], and in which expression of ygjD was again under control

of Para. We followed growing microcolonies of TB84 as described above and found that the consequences of YgjD depletion were profoundly different: cell elongation rate decreased during filipin the YgjD depletion process as for the relA + spoT + strain TB80 (Figure 4a). In contrast to what we observed with this (p)ppGpp+ strain, the decrease p38 MAPK inhibitor in elongation rate was compensated for by an increase in the time interval between two divisions (Additional file 14 – movie 9, and Figure 4a). As a consequence, cell size at division was not reduced, and the final cell length of depleted (p)ppGpp0 cells (TB84) was on average twice that of depleted (p)ppGpp+ cells (TB80)

(Figure 4b). This is reminiscent of the elongated cells found in populations of cells depleted for YgjD by Handford and colleagues [3]. Figure 4 The change in cell size homeostasis in response to YgjD depletion depends on (p)ppGpp. A) Changes in cell elongation rate and the interval between two divisions during YgjD depletion, for TB80 (ppGpp+) and TB84 (ppGpp0). For each strain, means and standard errors of three independent experiments are shown. In TB80, cell elongation rate starts to decrease after generation 3, and cells divide before they double in size. In TB84, cell division occurs close to the moment of cell size doubling (the means are close to the contour line of constant cell size). B) Change of mean cell size during YgjD depletion, for and TB80 (ppGpp+) and TB84 (ppGpp-). In TB80, cell size starts to decrease after generation 3, as a consequence of cell division that occurs before cells double in size (see panel A).

Bioinformatics 2008, 24:i7–13 PubMedCrossRef 33 Meyer F, Paarman

Bioinformatics 2008, 24:i7–13.PubMedCrossRef 33. Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, Paczian

T, Rodriguez A, Stevens R, Wilke A, Wilkening J, Edwards RA: The Metagenomics RAST server – A public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 2008, 9:386.PubMedCrossRef 34. Cole JR, Chai B, Farris RJ, Wang Q, Kulam-Syed-Mohidee AS, McGarrell DM, Bandela AM, Cardenas E, Garrity GM, Tiedje JM: The ribosomal database project (RDP-II): introducing myRDP space and quality controlled public data. Nucleic Acids Res 2007, 35:169–172.CrossRef 35. Pruess E, Mocetinostat manufacturer Quast C, Knittel K, Fuchs B, Ludwig W, Peplies J, Glöckner FO: SILVA: a comprehensive mTOR inhibitor online resource for quality checked and aligned ribosomal

RNA sequence data compatible with ARB. Nuc Acids Res 2007, 35:7188–7196.CrossRef 36. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL: Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 2006, 72:5069–5072.PubMedCrossRef 37. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol 1990, 215:403–410.PubMed 38. Kristiansson E, Hugenholtz P, Dalevi D: ShotgunFunctionalizeR: An R-package for functional analysis of metagenomic data. Bioinformatics 2009, 25:2737–2738.PubMedCrossRef 39. Parks DH, Beiko RG: Identifying biologically relevant differences between metagenomic Sclareol communities. Bioinformatics 2010, 26:715–721.PubMedCrossRef 40. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger

GG, Van Horn DJ, Weber CF: Introducing mothur: open source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 2009, 75:7537–41.PubMedCrossRef 41. Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, de Crécy-Lagard V, Diaz N, Disz T, Edward R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata-Reuyl D, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V, Pusch GD, Rodionov DA, Rückert C, Steiner J, Stevens R, Thiele I, Vassieva O, Ye Y, Zagnitko O, Vonstein V: The subsystems approach to Nutlin-3 nmr genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 2005, 33:5691–702.PubMedCrossRef 42. Clarke KR, Gorley RN: PRIMER-E. PRIMER-E Ltd, Plymout, UK; 2001. Authors’ contributions RL carried out sample collection, sample processing, bioinformatic analyses, and manuscript preparation. JSD conceived of the study, and participated in its design and coordination and helped to draft the manuscript. SG participated in bioinformatic and statistical analyses.

FEMS Microbiol Ecol 2013,83(3):672–684 PubMedCrossRef 43 Beringe

FEMS Microbiol Ecol 2013,83(3):672–684.PubMedCrossRef 43. Beringer JE: R factor transfer in Rhizobium leguminosarum . J Gen Microbiol 1974,84(1):188–198.PubMedCrossRef 44. Robertsen BK, Aman P, Darvill AG, McNeil M, Albersheim P: The structure PFT�� molecular weight of acidic extracellular polysaccharides secreted by Rhizobium leguminosarum and Rhizobium trifolii . Plant Physiol 1981,67(3):389–400.PubMedCentralPubMedCrossRef 45. Vargas C, McEwan AG, Downie JA: Detection of c-type cytochromes using enhanced chemiluminescence. Anal Biochem 1993,209(2):323–326.PubMedCrossRef

46. Nicholas DJD, Nason A: Determination of nitrate and nitrite. In Methods in Enzymology, VOlume III. Edited by: Colowick SP, Talazoparib chemical structure Kaplan NO. London: Academic Press; 1957:974–977. 47. Zhang X, Broderick M: Amperometric detection of nitric oxide. Mod Asp Immunobiol 2000,1(4):160–165. 48. Sambrook J, Fritsch EF, Maniatics T: Molecular cloning: a laboratory manual. New York: Cold Spring Harbor Laboratory Press; selleck screening library 1989. 49. Glenn SA, Gurich N, Feeney MA, Gonzalez JE: The ExpR/Sin quorum-sensing system controls succinoglycan production in Sinorhizobium

meliloti . J Bacteriol 2007,189(19):7077–7088.PubMedCentralPubMedCrossRef 50. Krol E, Becker A: Global transcriptional analysis of the phosphate starvation response in Sinorhizobium meliloti strains 1021 and 2011. Mol Genet Genomics 2004,272(1):1–17.PubMedCrossRef Competing interests The authors declare that they have no competing interests. Authors’ contributions MJT and MJD conceived of the study. MJT and MIR carried out the phenotypic analyses of the E. meliloti denitrification mutants. TC and JJP participated in the gene expression experiments. MJD and EJB supported the research. MJT and MJD wrote the manuscript. EJB coordinated and critically revised Y-27632 2HCl the manuscript. All of the authors read and approved the manuscript.”
“Background Campylobacter jejuni (C. jejuni), a microaerophilic, spiral-shaped, flagellated Gram-negative bacterium, is the most frequent cause of human gastroenteritis worldwide [1]. C. jejuni infections are often caused by consumption of undercooked poultry, unpasteurised milk or contaminated water

[2]. Adhesion of C. jejuni to host cells plays an important role in colonisation of chickens and in human infection [3]. Campylobacter binding to host cell receptors is not mediated by fimbria or pili, like in E. coli and Salmonella[4]. As noted in a recent review, other bacterial cell structures may contribute to interaction of Campylobacter with host cells [5]. In some cases, bacterial adhesion can be mediated by oligosaccharides present on the surface of host cells [6, 7]. In other cases, it is a pathogen oligosaccharide that is responsible for binding to specific, lectin-like, host cell structures. For example, a pathogenic Gram-positive bacterial species Nocardia rubra binds to a human lectin (intelectin) expressed by cells in different organs including intestine [8].

However, this did not result in interpretation discrepancies (Tab

However, this did not result in interpretation discrepancies (Table 2). Most important, on-screen adjusted automation of disk diffusion readings did not result in an increased frequency of susceptibility categorisation errors. The results of this study showed no major and very major discrepancies occurring with on-screen adjusted Sirscan readings

KPT-8602 cell line when compared to manual measurements serving as the gold standard. Other authors found low numbers of major and very major errors with the Sirscan system as well [12, 13]. Isolates with confirmed resistance mechanisms such as ESBL, AmpC, carbapenemases, VRE, or MRSA were reliably detected except for two isolates showing inhibition zone diameters close to the EUCAST breakpoint. However, both isolates would have been missed by manual reading, too. Reproducibility and precision check details of diameter measurements are critical for AST interpretation and antimicrobial therapy. Previous investigations have focused on the correlation of manual and automated measurements using Bcl-2 inhibitor systems like Sirscan, OSIRIS, BIOMIC, or Oxoid Aura [12–16,

20]. While correlation of manual and automated systems is well established, we here used a fully automated system to assess, if automated reading is principally able to decrease standard deviation of measurements and, thus, can increase precision. This is of particular importance given the changes in recent EUCAST and, in part, CLSI AST guidelines to decrease or even abandon the intermediate AST zone [19]. Investigator dependence of manual measurements with the disk diffusion method is partly due to non-standardised conditions such as ambient light, angle of vision, reading plates from top or bottom, or physical and mental condition of the investigator. The Sirscan analysis software reads under standardised light, positioning and background conditions. The lack or downsizing of the intermediate category by CLSI and/or EUCAST 2011/12 guidelines enhances

the probability of major and very major errors of repeat measurements since susceptible and resistant categories lie directly adjacent to each other [17–19]. Standardisation Glutathione peroxidase of measurements with concomitant lower standard deviations will facilitate consistent AST reports for repeatedly tested strains, or for ASTs of one strain isolated from multiple patient samples. The reproducibility of fully automated Sirscan readings without human interaction (on-screen adjustments) was significantly higher compared with manual calliper measurements. The average standard deviation for repeat measurements of E. coli ATCC 25922 and S. aureus ATCC 29213 inhibition zones was reduced by half using the fully automated reading mode. If, however, Sirscan readings were adjusted on-screen, standard deviations were not significantly lower (Table 3). For P.

Although different tissue types and excitation wavelengths were a

Although different tissue types and excitation wavelengths were analyzed before to determine the optimal dimensions of a nanoshell [10, 11], no optimization has ever been performed for a nanoshell ensemble with a real size distribution. In this Letter, we fill this gap by conducting the first theoretical study of the distribution parameters of the lognormally

dispersed HGNs exhibiting peak absorption or scattering efficiency. In particular, we comprehensively analyze the dependence of these parameters on the excitation wavelength and optical properties of the tissue, Selleck H 89 giving clear design guidelines. Methods Despite a significant progress in nanofabrication technology over the past decade, we are still unable to synthesize large ensembles of almost identical nanoparticles. The nanoparticle ensembles that are currently used for biomedical applications selleck chemicals llc exhibit broad size distributions, which

are typically lognormal in shape [12–15]. In an ensemble of single-core nanoshells, both the core radius R and the shell thickness H are distributed lognormally [15], with their occurrence probabilities given by the function [16] (1) where x=r or h is the radius or thickness of the nanoshell, μ X = ln(Med[X]) and σ X are the mean and standard deviation of lnX, respectively, and Med[X] is the geometric mean of the random variable x=r or H. The efficiencies of absorption and scattering by a nanoparticle ensemble are the key characteristics determining its performance in biomedical applications. In estimating

these characteristics, it is common to use a number of simplifying assumptions. First of all, owing to a relatively large interparticle distance Histone demethylase inside human tissue (typically constituting several micrometers [17]), one may safely neglect the nanoparticle interaction and the effects of multiple scattering at them [18, 19]. Since plasmonic nanoparticles can be excited resonantly with low-intensity optical small molecule library screening sources, it is also reasonable to ignore the nonlinear effects and dipole–dipole interaction between biomolecules [20]. The absorption of the excitation light inside human tissue occurs on a typical length scale of several centimeters, within the near-infrared transparency window of 650 to 1000 nm [21]. However, the attenuation of light does not affect the efficiencies of scattering and absorption by the ensemble, and is therefore neglected in the following analysis.

These observations allowed us to rule out the participation of σT

These observations allowed us to rule out the participation of σT and σE in the control of sigF expression. To further verify if the promoter region upstream of sigF is controlled by σF, we overexpressed sigF in the parental strain from an additional plasmid-encoded copy of the gene under the control of a constitutive

promoter (construct pCM30) and measured β-galactosidase activity in these cells harboring either pCKlac53-1 or pCKlac53-2. Overexpression of sigF in cells with the construct containing the complete sigF promoter (pCK53-1) led to an increase in β-galactosidase activity, whereas no difference was observed in cells harboring the promoterless construct pCKlac53-2 (Figure 3B). Similarly, higher β-galactosidase activity was observed in sigF overexpressing cells bearing the construct containing the promoter sequence motifs upstream from CC3254 (pCKlac54-1) when compared to the parental strain carrying the same construct or sigF overexpressing cells harboring the construct containing only the −10 motif of the promoter sequence of PRIMA-1MET cell line CC3254-CC3255-CC3256-CC3257 (pCKlac54-2) (Figure 3B). Therefore, these results confirm

that specific and highly similar promoter sequence motifs found upstream from sigF-CC3252 and CC3254-CC3255-CC3256-CC3257 are required for the control of these transcriptional units by σF. CC3252 negatively regulates σF regulon expression The chromosomal organization of CC3252 and check details sigF in a putative operon suggests that CC3252 could be involved in the same regulatory pathway of σF. To test the assumption that CC3252 could control σF activity, we monitored the expression of σF-dependent genes in parental cells overexpressing CC3252 from a plasmid-encoded copy of the gene under the control of the constitutive lacZ promoter present in vector pJS14. For that, cells overexpressing

CC3252 were stressed or not with dichromate and compared in qRT-PCR experiments with cells harboring the empty vector pJS14 or cells without this vector under the same conditions. According to qRT-PCR experiments, expression of genes Etofibrate CC2906 and CC3255 was slightly reduced in cells overexpressing CC3252 under no stress conditions, when compared to cells with the empty vector pJS14 or cells without the vector (Figure 4). However, induction of CC2906 and CC3255 expression under dichromate stress was clearly absent in CC3252 overproducing cells, when compared to cells not overexpressing CC3252 (Figure 4). No difference could be found in the expression levels of two control genes (CC1039 and CC0566) when we compared cells overexpressing CC3252 or not (data not shown). This observation rules out a possible nonspecific effect due to overproduction of the protein. Taken together, these data indicate that CC3252, here denominated nrsF, acts as a negative regulator of σF function in C. crescentus.

CrossRefPubMed Competing interests The authors declare that they

CrossRefPubMed Competing interests The authors declare that they have no competing interests. Authors’ contributions RUK – conceived and coordinated the study, performed experiments, analyses, interpreted data and wrote the manuscript.

RK – acquisition of funding, general supervision of the research selleck inhibitor group. EP, AKB, JHP – acquisition of data, edition of the draft manuscript. PP – participated in analysis and interpretation of data, performed the statistical analysis, was involved in drafting the manuscript and revised it critically. All authors read and approved the final manuscript.”
“Background During the last years a wide consensus has been growing on the fact that α/β ratio for prostate cancer should be low [1–6], encouraging the use of hypo-fractionated treatment schemes. This would result in an increased therapeutic ratio besides a well known series of practical advantages, like diminishing the number of accesses to department, shorter treatment time and abatement of waiting lists. Due to the fact that a major concern on the use of hypofractionation is the late rectal toxicity, the necessity to predict the GANT61 research buy risk of toxicity for alternative treatment schemes is becoming insistent. Leborgne [7], in a study conducted on patients Blebbistatin supplier treated with brachytherapy

for cancer of the cervix, evaluated an α/β ratio for rectal late complications not significantly different from 3 Gy. In a more recent publication, Brenner

[8] underlined the importance of investigating the sensitivity of late rectal damage to changes in fractionation and encouraged the use of new data from hypofractionated schemes. His analysis resulted in an α/β ratio estimate of 5.4 Gy, suggesting a correlation with early-responding damage. Since 2003, a phase II randomized trial started at our second institute, to compare a conventional versus a hypofractionated treatment scheme for localized prostate cancer. It was assumed an α/β ratio for prostate of 1.5 Gy. The primary objective of the trial were acute and late toxicity, and survival and local control with controlled PSA (Prostate Specific Antigen). In this work, dose-volume data of rectal wall from patients treated exclusively at our institution were fitted to the Normal Tissue Complication Probability (NTCP) model proposed by Lyman-Kutcher-Burman [9–11]. The effect of dose fractionation was included in the model to quantify the α/β ratio for late rectal toxicity. Methods Patient population From March 2003 to June 2008, 162 patients with carcinoma of the prostate were randomised for the present study. Assuming that an incidence of ≥ Grade 2 (G2) toxicity in less than 55% of patients is acceptable, the sample size was calculated for a power of 80% and a level of significance of 5%. A total of 114 patients, having a follow-up longer than 6 months, were included in the present analysis: 57 patients in each arm.