PubMedCrossRef 2. Sill ML, Tsang RSW: Antibiotic susceptibility of invasive Haemophilus influenzae strains in Canada. Antimicrob Agents Chemother 2008, 52:1551–1552.see more PubMedCentralPubMedCrossRef 3. Shuel M, Hoang L, Law DKS, Tsang R: Invasive Haemophilus influenzae in British Columbia: non-Hib and non-typeable strains causing disease in children and adults. Int J Infect Dis 2011, 15:e167-e173.PubMedCrossRef 4. Resman F, Ristovski M, Forsgren A, Kaijser B, Kronvall

G, Medstrand P, Melander E, Odenholt I, Riesbeck K: Increase of beta-lactam-resistant invasive Haemophilus influenzae in Sweden, 1997 to 2010. Antimicrob Agents Chemother 2012, 56:4408–4415.PubMedCentralPubMedCrossRef 5. Murphy T: Current and future prospects for a vaccine for nontypeable TEW-7197 price Haemophilus influenzae . Curr Infect Dis Rep 2009, 11:177–182.PubMedCrossRef 6. Tristram S, Jacobs MR, Appelbaum PC: Antimicrobial resistance in Haemophilus influenzae . Clin Microbiol Rev 2007, 20:368–389.PubMedCentralPubMedCrossRef 7. Ubukata K, Shibasaki Y, Yamamoto K, Chiba N, Hasegawa K, Takeuchi Y, Sunakawa K, Inoue M, Konno M: Association of amino acid substitutions in penicillin-binding protein 3 with beta-lactam resistance in beta-lactamase-negative ampicillin-resistant Haemophilus influenzae . Antimicrob Agents Chemother 2001, 45:1693–1699.PubMedCentralPubMedCrossRef 8. Hasegawa K, Chiba N, Kobayashi R, Murayama

SY, Iwata S, Sunakawa K, Ubukata K: Rapidly increasing prevalence of beta-lactamase-nonproducing, ampicillin-resistant Haemophilus influenzae type b in patients with meningitis. Antimicrob AZD6094 Agents Chemother 2004, 48:1509–1514.PubMedCentralPubMedCrossRef 9. Garcia-Cobos S, Campos J, Lazaro E, Roman F, Cercenado E, Garcia-Rey C, Perez-Vazquez M, Oteo J, de AF: Ampicillin-resistant non-beta-lactamase-producing Haemophilus influenzae in Spain: recent emergence of clonal isolates with increased resistance to cefotaxime and cefixime. Antimicrob Agents Chemother 2007, 51:2564–2573.PubMedCentralPubMedCrossRef 10. Hotomi M,

Fujihara K, Billal DS, Suzuki K, Suplatast tosilate Nishimura T, Baba S, Yamanaka N: Genetic characteristics and clonal dissemination of beta-lactamase non-producing ampicillin-resistant (BLNAR) Haemophilus influenzae isolated from the upper respiratory tract in Japan. Antimicrob Agents Chemother 2007, 51:3969–3976.PubMedCentralPubMedCrossRef 11. Skaare D, Allum AG, Anthonisen IL, Jenkins A, Lia A, Strand L, Tveten Y, Kristiansen BE: Mutant ftsI genes in the emergence of penicillin-binding protein-mediated beta-lactam resistance in Haemophilus influenzae in Norway. Clin Microbiol Infect 2010, 16:1117–1124.PubMedCrossRef 12. Shuel ML, Tsang RSW: Canadian beta-lactamase negative Haemophilus influenzae isolates showing decreased susceptibility toward ampicillin have significant penicillin binding protein 3 mutations. Diagn Microbiol Infect Dis 2009, 63:379–383.PubMedCrossRef 13.

PubMedCrossRef 38. Madsen K, Cornish A, Soper P, McKaigney C, Jijon H, Yachimec C, Doyle J, Jewell L, De Simone C: Probiotic bacteria enhance murine and human intestinal epithelial barrier selleck inhibitor function. Gastroenterology 2001, 121:580–591.PubMedCrossRef

39. de Los Reyes-Gavilan CG, Suarez A, Fernandez-Garcia M, Margolles A, Gueimonde M, Ruas-Madiedo P: Adhesion of bile-adapted Bifidobacterium strains to the HT29-MTX cell line is modified after sequential gastrointestinal challenge simulated in vitro using human gastric and duodenal juices. Res Microbiol 2011, 162:514–519.PubMedCrossRef 40. Mirold S, Ehrbar K, Weissmuller A, Prager R, Tschape H, Russmann H, Hardt WD: Salmonella host cell invasion emerged by acquisition of a mosaic of separate genetic elements, including Salmonella pathogenicity island 1 (SPI1), SPI5, and sopE2. J Bacteriol 2001, 183:2348–2358.PubMedCrossRef 41. Peng L, He Z, Chen W, Holzman IR, Lin J: Effects of butyrate on intestinal barrier function in a Caco-2 cell monolayer model

of intestinal barrier. Pediatr Res 2007, 61:37–41.PubMedCrossRef 42. Touré R, Kheadr E, Lacroix C, Moroni O, Fliss I: Production of antibacterial substances by bifidobacterial isolates from infant stool active against Listeria VX-765 molecular weight monocytogenes . J Appl Microbiol 2003, 95:1058–1069.PubMedCrossRef 43. Fallingborg J, Christensen LA, Ingeman-Nielsen M, Jacobsen BA, Abildgaard K, Rasmussen HH, Rasmussen SN: Measurement of gastrointestinal pH and regional transit times in normal children. J Pediatr Gastroenterol

BLZ945 Nutr 1990, 11:211–214.PubMedCrossRef 44. Wagener S, Shankar KR, Turnock RR, Lamont GL, Baillie CT: Colonic transit time–what is normal? J Pediatr Surg 2004, 39:166–169. discussion 166–169PubMedCrossRef 45. Lesuffleur T, Barbat A, Dussaulx E, Zweibaum A: Growth adaptation to methotrexate of HT-29 human colon carcinoma cells is associated with their ability to differentiate into columnar absorptive and mucus-secreting SSR128129E cells. Cancer Res 1990, 50:6334–6343.PubMed 46. Van de Wiele TR, Verstraete W, Siciliano SD: Polycyclic aromatic hydrocarbon release from a soil matrix in the in vitro gastrointestinal tract. J Environ Qual 2004, 33:1343–1353.PubMedCrossRef 47. Kim KP, Loessner MJ: Enterobacter sakazakii invasion in human intestinal Caco-2 cells requires the host cell cytoskeleton and is enhanced by disruption of tight junction. Infect Immun 2008, 76:562–570.PubMedCrossRef Authors’ contributions AZ, MG, CC and CL conceived the study. AZ and MG carried out the experiments. AZ, MG, CL and CC analyzed results and drafted the manuscript. All authors read and approved the final manuscript.”
“Background Microbial biofilm formation is an important virulence mechanism, which allows immune evasion and survival against antibiotic treatments [1, 2]. Many bacterial nosocomial infections are associated with biofilms formed on contaminated medical devices. Dispersal of biofilm has also been proposed to augment infection spread [3–8].

It can be observed that, under 2 W/cm2 laser irradiation, the

V CPD values change slightly for all the three samples, but they increase obviously when the laser intensity increase up to 4 W/cm2 and above. Also, the increase magnitude is different for the three types of NRs. The increase of V CPD with laser intensity is most significant for NR3, similar to the increase of trapped charges. Similar surface potential variation by photogenerated charges has been obtained by Kelvin potential force Cyclopamine supplier microscopy (KPFM) [26, 27]; it was declared that the positive (negative) shift in surface potential with laser corresponds to an increase in hole (electron) density. Thus, the positive shift in V CPD with laser intensity in our experiments can also be attributed to the increase of trapped hole density, which is consistent with the above results of selleck compound charge density. As V CPD equals to (ϕ tip − ϕ sample) / e, the results declare that the work function of Si NR decrease upon laser irradiation should be due to the photogenerated holes trapped in NRs. The reason why positive charging measured on n-type Si NRs is not very clear, and further studies are required to get a clear mechanism. find more The possible mechanism may be suggested to the tunneling of photogenerated electrons to the substrate and trapping the holes in the NRs. In previous studies on the photoionization of an individual CdSe nanocrystals [16, 28], it was

found that a significant fraction of nanocrystals was positively charged and it was attributed to the tunneling of the excited electrons into the substrate. They assumed that the hole tends to be localized in the nanocrystal, while the electron is much more delocalized, with a nonnegligible fraction of the electron density outside the nanocrystal. Another possibility arises from that the holes can be captured at Si-Si bonds according to the reaction ≡ Si-Si ≡ + h → ≡Si+ + · Si≡, as reported in reference [29]. By adopting the above viewpoint, it can be suggested that when Si NRs are irradiated, free charges are

photogenerated after dissociation of mafosfamide the excitons. Due to the tunneling of photoelectrons and/or capture of holes, the Si NRs would be positively charged. To see the dynamics of charging and decharging, the time evolution of the EFM phase shift with the laser ON and OFF is present in Figure 4a,b for NR2 and NR3, respectively. As the change of phase shift with laser irradiation is too small for NR1, it is not given here. When the laser is turned on, the EFM phase shifts of both NR2 and NR3 moves to the more negative values, and the signal follows a monotonic decay to a new equilibrium value, corresponding to the charge generation and trapping process. The experimental curves can be fitted with single exponential decay, as shown in the left insets in Figure 4, giving a time constant of 7.6 and 13.6 s for NR2 and NR3, respectively.

Albuminuria

and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol. 2009;20:1813–21.PubMedCrossRef 24. Rigalleau V, Lasseur C, Raffaitin C, Beauvieux MC, Barthe N, Chauveau P, et al. Normoalbuminuric renal-insufficient diabetic patients: a lower-risk group. Diabetes Care. 2007;30:2034–9.PubMedCrossRef 25. Bruno G, Merletti F, Bargero G, Novelli G, Melis D, Soddu A, et al. Estimated glomerular filtration rate, Capmatinib mouse albuminuria and mortality in type 2 diabetes: the Casale Monferrato study. Diabetologia. 2007;50:941–8.PubMedCrossRef 26. So WY, Kong AP, Ma RC, Ozaki R, Szeto CC, Chan NN, et al. Glomerular filtration rate, cardiorenal end points, and all-cause mortality in type 2 diabetic patients. Diabetes Care. 2006;29:2046–52.PubMedCrossRef 27. Vlek AL, van der Graaf Y, Spiering W, Algra A, Visseren FL, GDC-0941 order SMART study group. Cardiovascular events and all-cause mortality by albuminuria and decreased glomerular filtration rate in patients with vascular disease. J Intern Med. 2008;264:351–60.PubMedCrossRef 28. I-BET-762 in vivo Drury PL, Zannino TD, Ehnholm C, Flack J, Whiting M, Fassett R, et al. Estimated glomerular filtration rate and albuminuria are independent predictors of cardiovascular events and death in type 2 diabetes mellitus: the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study. Diabetologia. 2011;54:32–43.PubMedCrossRef

29. Murussi M, Campagnolo N, Beck MO, Gross JL, Silveiro SP. High-normal levels of albuminuria predict the development of micro- and macroalbuminuria and increased mortality in Brazilian type 2 diabetic patients: an 8-year follow-up study. Diabetes Med. 2007;24:1136–42.CrossRef

30. Babazono T, Glutamate dehydrogenase Nyumura I, Toya K, Hayashi T, Ohta M, Suzuki K, et al. Higher levels of urinary albumin excretion within the normal range predict faster decline in glomerular filtration rate in diabetic patients. Diabetes Care. 2009;32:1518–20.PubMedCrossRef 31. Gerstein HC, Mann JF, Yi Q, Zinman B, Dinneen SF, Hoogwerf B, et al. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA. 2001;286:421–6.PubMedCrossRef 32. Fioretto P, Steffes MW, Sutherland DE, Goetz FC, Mauer M. Reversal of lesions diabetic nephropathy after pancreas transplantation. N Engl J Med. 1998;339:69–75.PubMedCrossRef 33. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS. Regression of microalbuminuria in type 1 diabetes. N Engl J Med. 2003;348:2285–93.PubMedCrossRef 34. Hovind P, Rossing P, Tarnow L, Smidt UM, Parving HH. Remission and regression in the nephropathy of type 1 diabetes when blood pressure is controlled aggressively. Kidney Int. 2001;60:277–83.PubMedCrossRef 35. Hovind P, Rossing P, Tarnow L, Toft H, Parving J, Parving HH. Remission of nephrotic-range albuminuria in type 1 diabetic patients. Diabetes Care. 2001;24:1972–7.PubMedCrossRef 36.

This makes the underestimation of the true F V′/F M′ value light intensity dependent as well, since a higher light intensity induces more non-photochemical quenching. Question 4. Which part of the leaf is probed and analyzed by a fluorescence measurement? The leaf is optically complex. In a dorsiventral selleck compound leaf, the palisade parenchyma cells have been shown to act as light guides, keeping the light more or less focused (Vogelmann and Martin 1993; Vogelmann et al. 1996). The lobed cells of the spongy mesophyll and the spaces that surround these cells, on the other hand, disperse the light (Vogelmann and Martin 1993). At the

same time, there is a strong light gradient within the leaf (Vogelmann 1989, 1993). This means that the light intensity decreases rapidly as light penetrates into the leaf. As a consequence, illuminating and probing Chl a fluorescence emission on

the check details adaxial surface of the leaf, chloroplasts located deep in the leaf will be excited Selleck GDC 0449 by a much lower photon flux density than those located close to the adaxial side of the leaf (Terashima and Saeki 1985; Fukshansky and Martinez von Remisowsky 1992). At the same time, the spectral distribution of the light changes as well: as light penetrates the mesophyll, the relative contribution of green and far-red (FR) light progressively increases, because the absorption of these wavelengths by the leaf is less efficient (Sun et al. 1998; Rappaport et al. 2007). The chloroplasts located deeper in the leaf, i.e., those of the spongy tissue, acclimate to these lower, FR-enriched light intensities by increasing the antenna size of PSII, reducing the number of RCs, and decreasing the PSI/PSII ratio (Terashima et al. 1986; Evans 1999; Fey et al. 2005; Pantaleoni et al. 2009). Since the emitted fluorescence is a linear function of the light intensity (Vogelmann and Evans 2002; cf. Schansker et al. 2006), chloroplasts located deeper in the leaf will contribute to a lesser extent to

the detected fluorescence signal. In practice, fluorescence measurements will probe mainly chloroplasts in the palisade parenchyma cells (Vogelmann and Evans 2002). The assumption that not all chloroplasts are Ribose-5-phosphate isomerase assayed is supported by the observation that a fivefold decrease in the chlorophyll content of the leaf does not affect the detected F O and F M values (Dinç et al. 2012). In fact, since the total amount of fluorescence emitted by the leaf does not change, it suggests that the light beam probes deeper in the leaf as more chlorophyll is lost. The optical properties of the leaf also mean that measurements made on the abaxial (bottom) side of the leaf have characteristics that differ considerably from those made on the adaxial (top) side of the leaf (Schreiber et al. 1977).

SigB is Protein Tyrosine Kinase inhibitor involved in HQNO-mediated emergence of SCVs and biofilm production Strains Newbould and NewbouldΔsigB were used to determine whether SigB is involved in the emergence of SCVs and biofilm production under an exposure to HQNO. Fig. 3A illustrates the ability of HQNO (10 μg/ml, overnight) to favor the emergence of the SCV phenotype only in a sigB + background. HQNO significantly increased the presence of SCVs in strain Newbould, but not

in NewbouldΔsigB (Fig. 3B). This result was confirmed with strains SH1000 and 8325-4 (data not shown), which are isogenic strains with a functional and dysfunctional SigB system, respectively [36]. Fig. 3C demonstrates that the presence selleck inhibitor of HQNO significantly inhibits the growth of both Newbould and NewbouldΔsigB (P < 0.05 at 24 h of growth

for both; two-way ANOVA followed by a Bonferroni’s post test). However, the ability of HQNO to increase biofilm formation was observed with strain Newbould, but not with NewbouldΔsigB (Fig.3D). CB-839 ic50 These results suggest that, even if the inhibition of growth caused by HQNO is not influenced by SigB (Fig. 3C), HQNO-mediated emergence of SCVs and biofilm production is triggered by a SigB-dependent mechanism (Fig. 3D). Figure 3 SigB is involved in HQNO-mediated emergence of SCVs and biofilm production. (A) Pictures show SCV colonies grown on agar containing a selective concentration of gentamicin following or not an overnight exposure to 10 μg/ml of HQNO for strains Newbould and NewbouldΔsigB. (B) Relative number of SCV CFUs recovered after 18 h of growth for strains Newbould and NewbouldΔsigB in the presence (black bars) IKBKE or not (open bars) of 10 μg HQNO/ml. Results are normalized to unexposed

Newbould (dotted line). Data are presented as means with standard deviations from at least three independent experiments. Significant differences between unexposed and HQNO-exposed conditions (*, P < 0.05), and between strains in the same experimental condition (Δ, P < 0.05) were revealed by a one-way ANOVA with tuckey’s post test. (C) Growth curves of Newbould (□) and NewbouldΔsigB (●) exposed (dotted lines) or not (solid lines) to 10 μg/ml of HQNO. (D) Relative biofilm formation as a function of the concentration of HQNO for strains Newbould (open bars) and NewbouldΔsigB (grey bars). Results are normalized to the unexposed condition for each strain (dotted line). Data are presented as means with standard deviations from two independent experiments. Significant differences between Newbould and NewbouldΔsigB for each concentration of HQNO are shown (*, P < 0.05; **, P < 0.01; two-way ANOVA with bonferroni’s post test). SigB and agr activities are modulated by an exposure to HQNO Fig. 4 shows qPCR measurements of the expression of the genes asp23, fnbA, hld (RNAIII), hla, sarA and gyrB at the exponential growth phase for strains Newbould and NewbouldΔsigB exposed or not to HQNO.

Giangregorio et al. [8] interviewed 127 Momelotinib mouse patients (82% women) who had experienced a fragility fracture in the preceding 2 years. Among this clearly high-risk group, only 43% thought that they were at increased risk of a future fracture. Risk perception in GLOW for those taking medication for osteoporosis might be interpreted in two ways. Women could respond to the question using their assessment of premedication risk or considering on-treatment risk. When we examined patterns of risk perception for the subset of women on antiosteoporosis

treatment, 41% (4,574/11,094) this website responded that their risk of fracture was greater than that of their peers, suggesting that premedication risk was being considered. The reason why some women with risk factors fail selleck chemicals to see themselves at heightened likelihood of fracture may be because they are unaware that characteristics such as prior fracture, parental history of hip fracture, low weight, smoking, early menopause, and high intake of alcohol contribute to

risk. Support for such lack of recognition of well-established risk factors comes from Satterfield et al., who surveyed 400 US women aged 60 to 80 years in a random-digit dial telephone survey [14]. They found that women correctly identified risk related Monoiodotyrosine to smoking, exercise, calcium intake, and family history of fracture more than 60% of the time, but identified risks associated with early menopause, long-term steroid use, being thin, and use of alcohol less than 50% of the time. In the multivariable model reported here, neither smoking nor heavy alcohol use appeared significantly related to a perception

of higher-than-average fracture risk. Furthermore, although significant odds ratios in our models indicate that some women appreciated the added risk conferred by five of the seven FRAX risk factors, the magnitude of these ratios (in the range of 1.5–3.4) suggest that the association is not large. Even having been given the “diagnosis of osteoporosis” or “currently taking antiosteoporosis medication” only raised risk awareness to levels of 43% (5,400/12,429) and 41% (4,574/11,094), respectively. The lack of accurate perception of fracture risk has adverse implications for successful fracture-prevention activities. Motivation for patients to seek and follow treatment is related to perceived susceptibility to a disease [15]. Cline et al. [16] reported that, among almost 1,000 women aged 45 and older residing in a Minnesota community, higher perception of susceptibility to osteoporosis was significantly associated with use of osteoporosis medications.

Among the proteins predicted to have pHGRs we have identified some fungal proteins with an extremely high level of O-glycosylation. The B. cinerea genome, for example, codes for 9 proteins with 737–1764 residues, and signal peptide for secretion, that are predicted to be O-glycosylated in more than 400 of their PD0325901 supplier amino acids, as well as 11 additional smaller proteins, up to 300 amino acids, with more than 75% O-glycosylated residues (Additional file 2). Even considering that the actual number of O-glycosylation sites maybe 68% of these

(see the overestimation rate calculated for NetOGlyc in the results section), this level of O-glycosylation does not seem compatible with the Doramapimod chemical structure globular fold typical of enzymes or effector proteins, thus leading to the hypothesis that these proteins may be involved in maintaining the structure of the cell wall or the extracellular matrix. Most of them were predicted to have a GPI anchor at the C-terminus by at least one of the available prediction tools [18, 19], while others were homologues to proteins classified click here as GPI anchored proteins in other fungi or to proteins experimentally proven to be in the cell wall.

Curiously, a BLAST search revealed that 5 out of the 9 B. cinerea proteins with more than 400 predicted O-glycosylation sites have homologues only in the closely related fungus S. sclerotiorum, but not in any other organism, raising the question of whether they make any contribution to the lifestyle of these two highly successful, broad range, plant pathogens. Some of these highly O-glycosylated proteins

in B. cinerea display interesting similarities/motifs: Bofut4_P004110.1, a 670-aa protein predicted to be O-glycosylated in 75% of its residues, is similar (BLAST expect value = 4×10-7) to the S. cerevisiae protein Sed1p [20], a structural component of the cell wall. Bofut4_P104050.1, a 903-aa protein predicted to be O-glycosylated in 453 of them, is only present in B. cinerea and S. sclerotiorum and has two CFEM motifs that were proposed to be involved in virulence [21]. Bofut4_P131790.1, a Lumacaftor 938-aa protein predicted to be O-glycosylated in 414 residues, is homologous to the Metarhizium anisopliae protein Mad1 mediating adhesion to insect cuticle, raising the question of a putative role in spore dispersion. However, most of these proteins, with more than 400 O-glycosylated residues or with more than 75% O-glycosylated residues, have no similarity to proteins of known function. It would be especially interesting to search, among those proteins highly O-glycosylated, of candidate virulence factors involved in adhesion to the host surfaces. The existence of these O-glycosylated adhesion proteins is predicted from the fact that O-glycosylation deficient mutants in fungal pathogens have been shown to be affected in adhesion to the host [5, 6, 22]. An in silico search in U.

Fig. 16 Trichoderma sp. G.J.S. 99–17. a, b Pustules. c–h Conidiophores. i Conidia. All from CMD. c–h fluorescence microscopy in calcofluor DMXAA supplier (hairs visible in b–f). Scale bars: a = 1 mm, b = 0.5 mm; c–h = 20 μm; i = 10 μm It may be impossible to distinguish T. saturnisporopsis from T. saturnisporum on the basis of their phenotypes despite their rather wide phylogenetic separation. Both species are characterized by broadly ellipsoidal, conspicuously tuberculate conidia, irregularly branched conidiophores and poorly developed pustules that have sterile hairs and an ability to grow well at 35°C. The

most conspicuous difference is that T. saturnisporopsis is better able to grow at lower temperatures (25–30°C) than T. saturnisporum, with the exception of T. saturnisporopsis strain S 19, which is overall slower than the two other known strains of T. saturnisporopsis and T. saturnisporum but has a highly dissected margin when grown at 30°C and above. Fujimori and Okuda (1994) included strain G.J.S. 99–17 (as FP5566) in an early attempt to use molecular

methods to eliminate duplicate strains from their screening for antibiotics. Because of the warted conidia, they had identified FP5566 as T. viride. Although conidia of this strain are similar to those of T. viride (Jaklitsch et al. 2006), the two species Lonafarnib chemical structure are otherwise not similar and only distantly related. 19. Trichoderma saturnisporum Hammill, Mycologia 62: 112 (1970). Teleomorph: none known. Ex-type culture: ATCC 18903 = CBS 330.70 Typical sequences: ITS Z48726, tef1 EU280044 Samuels et al. (1998) and Gams and Bissett (1998) redescribed this uncommon but wide-spread, (North America, Caribbean Ocean region, Europe, South Africa, Androgen Receptor antagonist Australia) clonal species. The species was originally described from Georgia. It is morphologically indistinguishable from the phylogenetically unrelated T. saturnisporopsis. Doi et al. (1987) proposed

Trichoderma sect. Saturnisporum for T. saturnisporum and T. ghanense. This section was characterized by the tuberculate conidia. Molecular phylogenetic results PD184352 (CI-1040) (Kuhls et al. 1997; Druzhinina et al. 2012) indicate that these two species belong to the Longibrachiatum Clade but despite the unusual conidial ornamentation, they are not closely related. Trichoderma saturnisporum does not have any close relationships in the Longibrachiatum Clade. 20. Trichoderma sinense Bissett, Kubicek & Szakacs in Bissett et al., Can. J. Bot. 81: 572 (2003, as ‘sinensis’). Teleomorph: none known Ex-type culture: DAOM 230000 = TUB F-1043 Typical sequences: ITS AF486014, tef1 AY750889 (DAOM 230004) Trichoderma sinense is unusual in the Longibrachiatum Clade for its broadly ellipsoidal, smooth conidia, although its conidiophore branching and disposition of its phialides are typical of the clade. It is known (Bissett et al. 2003) from collections made in Taiwan and tropical China (Yunnan Province) and is possibly widespread in tropical East Asia. Druzhinina et al.

5 DDDs) prednisone equivalents. Moreover, nine patients (1.2 %) were excluded as they had medication records

available for less than 6 months prior to the first extraction date. Overall, 695 patients could be randomised, with 343 allocated to the intervention group and 352 to the control group. During the follow-up period, 38 (11.1 %) patients who were allocated to the intervention group and 36 (10.2 %) patients in the control group did not receive any new glucocorticoid prescription but did collect prescriptions for other drugs. Furthermore, 63 (18.4 %) patients in the intervention group and 72 (20.5 %) patients in the control group did not collect any prescription during follow-up (Fig. 1). Fig. 1 Flow chart of the study procedure The group assigned to the intervention was slightly younger than the control group (65.9 ± 16.9 vs. PD 332991 www.selleckchem.com/products/z-vad-fmk.html 68.7 ± 15.4 years, p = 0.02) and used hydrocortisone more often in the 6 months before baseline (7.0 % vs. 3.1 %, p = 0.02). All other baseline characteristics and mean follow-up time were similar selleck compound between the intervention and the control group (Table 1). Table 1 Baseline characteristics of patients in the intervention group and control group   Control group Intervention group p value N = 352 N = 343 Follow-up (mean ± SD months) 6.2 ± 1.1 6.2 ± 1.1 NS Female 55.4 % 54.5 % NS Age (mean ± SD

years) 68.7 ± 15.4 65.9 ± 16.9 0.02 Age categories  <50 years 11.6 % 18.4 % 0.01  50–70 years 36.1 % 31.5 % oxyclozanide NS  >70 years 52.3 % 50.1 % NS Type of glucocorticoid in the 6 months before baselinea  Betamethasone 1.4 % 0.3 % NS  Cortisone acetate 3.1 % 4.4 % NS  Dexamethasone 7.9 % 6.1 % NS  Fludrocortisone 2.0 % 2.9 % NS  Hydrocortisone 3.1 % 7.0 % 0.02  Methylprednisolone 0.3 % 0.3 % NS  Prednisolone

17.2 % 17.2 % NS  Prednisone 79.3 % 75.5 % NS  Triamcinolone 1.7 % 1.5 % NS  Cumulative DDDs of prednisone equivalents in the 6 months prior to baseline (mean ± SD) 183.3 ± 161.4 185.0 ± 172.3 NS  Cumulative DDD categories   <135 DDDs 41.2 % 37.9 % NS   135–270 DDDs 44.6 % 50.7 % NS   >270 DDDs 14.2 % 11.4 % NS Co-medication in the 6 months prior to baseline  Opioid analgesics 6.2 % 7.0 % NS  Cytostatic drugs 5.7 % 3.8 % NS  Anti-emetic drugs 4.5 % 2.9 % NS  Calcium 16.7 % 16.6 % NS  Vitamin D 6.0 % 7.0 % NS  HRT or SERMs 0.9 % 2.0 % NS  Anti-ulcer drugs 43.6 % 44.3 % NS  Bisphosphonate use >6 months prior to baseline 12.2 % 10.8 % NS Comparison of baseline characteristics between groups was significant at p < 0.05 HRT hormone replacement therapy, SERM selective estrogen receptor modulator, SD standard deviation, DDD defined daily dosage. aUse of more than one type of glucocorticoids per patient is possible During a mean follow-up period of 6.2 months, the primary endpoint (a prescription for a bisphosphonate during follow-up) was achieved by 39 patients (11.4 %) in the intervention group and by 28 patients (8.0 %) in the control group.