Ann Intern Med 152:380–390PubMed 42 Bischoff-Ferrari HA,

Ann Intern Med 152:380–390PubMed 42. Bischoff-Ferrari HA, Willett WC, Wong JB, Giovannucci E, Dietrich T, wson-Hughes B (2005) Fracture prevention with vitamin D supplementation: a meta-analysis of randomized controlled trials. JAMA 293:2257–2264PubMedCrossRef selleck chemicals 43. Kanis JA, Johnell O, Oden A, Johansson H, De Laet C, Eisman JA, Fujiwara S, Kroger H, McCloskey EV, Mellstrom D, Melton LJ, Pols H,

Reeve J, Silman A, Tenenhouse A (2005) Smoking and fracture risk: a meta-analysis. Osteoporos Int 16:155–162PubMedCrossRef 44. De Laet C, Kanis JA, Oden A, Johanson H, Johnell O, Delmas P, Eisman JA, Kroger H, Fujiwara S, Garnero P, McCloskey EV, Mellstrom D, Melton LJ III, Meunier PJ, Pols HA, Reeve J, Silman A, Tenenhouse A (2005) Body mass index as a predictor of fracture Obeticholic solubility dmso risk: a meta-analysis. Osteoporos Int 16:1330–1338PubMedCrossRef”
“Introduction Two gaps in Daporinad purchase osteoporosis management are well documented: (1) most patients at high risk for fracture are not identified for treatment, and (2) adherence to osteoporosis

pharmacotherapy is suboptimal [1–3]. For example, post-fracture osteoporosis screening and treatment rates are below 20% in most settings [1, 4], and approximately half of the patients who start osteoporosis pharmacotherapy discontinue treatment within the first year of therapy [2, 3]. In theory, pharmacists may play a role in narrowing gaps in osteoporosis diagnosis and treatment adherence. First, pharmacists may help identify high-risk patients, such as those on chronic glucocorticoid therapy who can then be targeted for bone mineral density (BMD) testing and treatment initiation. Second, pharmacists can provide counseling and educate patients on medication use, fall prevention, and the importance of calcium, vitamin D, exercise, and adherence to therapy. A recent review identified that non-drug interventions by healthcare professionals improved

quality of life, treatment adherence, and calcium intake among community-dwelling postmenopausal women with osteoporosis; however, no study within the review examined pharmacist interventions [5]. We therefore completed a systematic review of old the literature to identify all articles that have examined the impact of pharmacist interventions in osteoporosis management. The purpose of our review was to use results from randomized controlled trials (RCTs) to determine if pharmacy interventions can help narrow two gaps in osteoporosis management: identifying at-risk individuals and improving adherence to therapy. Methods Data sources and study eligibility The electronic databases EMBASE, HealthStar, International Pharmaceutical Abstracts, MEDLINE, and PubMed were searched from database development to April 2010 to identify all English language publications that examined pharmacist interventions in osteoporosis management.

As shown in Figure 2, a significant (p < 0 01) increase in plasma

As shown in Figure 2, a significant (p < 0.01) increase in plasma oxidative stress markers, ROS-generating potential (Figure 2A) and protein carbonyls (Figure 2B) were TPCA-1 cell line observed 12 hours after muscle damage in both conditions. After 36 hours recovery, a gradual decrease in plasma selleck products ROS-generating potential (Figure 2A) was observed in the blueberry condition, whereas ROS-generating potential

remained elevated in the control condition (p < 0.01). A large and significant (p < 0.01) increase in plasma carbonyls was observed at 12 hours in both conditions, followed by a gradual decrease (Figure 2B). Although an accelerated decline in plasma carbonyls was observed with blueberries, Vadimezan in vivo the difference was not statistically significant (p = 0.06). Inflammatory biomarkers associated with muscle damage, CK and IL-6 were measured. A gradual and significant (p < 0.05) increase in serum CK (Figure 2C) was observed in both conditions, between pre-exercise and 36 hours after. The CK levels detected following 60 hours recovery were lower in the blueberry beverage condition for the majority (8 out of 10) of the participants, however the overall difference was not significant (p = 0.840). In addition, no interaction effect between time and treatment

was observed (p = 0.426). Assessment of plasma IL-6 (Figure 2D) during the recovery period revealed a gradual increase in plasma IL-6 following exercise. Although this was significantly (p < 0.05) PJ34 HCl different from pre-exercise levels after 36 hours and 60 hours of recovery in both the blueberry and control beverage conditions, no blueberry treatment (p = 0.198) or time x treatment

interactions (p = 0.721) were observed. Figure 2 Modulation of systemic oxidative stress and inflammatory markers after strenuous exercise. [A] Plasma oxidative capacity, [B] protein carbonyls, [C] creatine kinase or [D] interleukin (IL)-6 were assessed immediately before (pre) and then 12, 36 or 60 hours after 300 eccentric contractions of the quadriceps under control (♦) or blueberry (■) conditions. Results are expressed as mean ± standard error of percentage change from pre-eccentric exercise measurements. * P < 0.05 represents significant time difference from pre-exercise levels and § P < 0.05 represents significant treatment (blueberry) and time interaction, n = 10 volunteers. Total antioxidant capacity The consumption of blueberries had no statistical effect on plasma antioxidant capacity prior to the onset of the eccentric exercise (Figure 3A); control (p = 0.140) and blueberry (p = 0.149), respectively. However, assessment of plasma antioxidant capacity between the pre-treatment and the 60 hour recovery time point revealed a significant treatment x time interaction (p = 0.038).

The performance of a thermoelectric material is determined cooper

The performance of a thermoelectric material is determined cooperatively by the Seebeck coefficient (S), thermal conductivity

(κ), and the electrical conductivity (σ) of the material [4]. Unfortunately, these three parameters have some intercorrelations in bulk, Emricasan molecular weight limiting the thermoelectric performance of a bulk material [5]. In this regard, one-dimensional (1D) nanowires have been highlighted, where a combination of quantum confinement effect and phonon boundary scattering drastically enhances the thermoelectric performance [6–8]. However, the controlled growth of thermoelectric nanowires and the reproducible fabrication of energy conversion modules based on them should be further demonstrated. Two-dimensional (2D) thin films have the superiority in terms of the ease LY3023414 of material and Gemcitabine cell line module fabrication

and the reproducibility of the thermoelectric performance. The best thermoelectric materials reported to date include Bi2Te3 [9], AgPbmSbTe2+m [10], and In4Se3−δ [11]. These materials, however, contain chalcogens (Se, Te), heavy metals (Pb, Sb), and rare metals (Bi, In), all of which are expected to restrict the widespread use of these materials. Recently, it has been demonstrated that even a conventional semiconductor, silicon (Si), can exhibit thermoelectric performance by adopting nanostructures such as nanowires [12], nanomeshes [13], and holey thin films [14]. Although Si has a high S of 440 μV/K, its electrical conductivity is poor (0.01 ~ 0.1 S/cm) [15]. Thus, alloying Si with a good metal could lead to the improved

thermoelectric performance. Aluminum (Al) is a typical good metal that has Methisazone the advantages of high electrical conductivity (approximately 3.5 × 105 S/cm) [16], light weight, and low cost. Despite the expected high electrical conductivity, the thermal conductivity of Si-Al alloys may be still high due to the large thermal conductivities of the constituents: κ Al = 210 ~ 250 W/m K and κ Si = 149 W/m K at room temperature [17]. The thermal conductivity of the alloy can be reduced by introducing nano- or microstructures on the alloy film. For this reason, embodying nano- or microstructures on Al-Si alloy films is a critical prerequisite for the study of thermoelectric performance of heterostructures made of Al-Si alloys. In this work, aluminum silicide microparticles were formed from Al thin films on Si substrates through self-granulation. This process resulted from solid-state interdiffusion of Al and Si at hypoeutectic temperatures, which was activated by compressive stress stored in the films. This stress-induced granulation technique is a facile route to the composition-controlled microparticle formation with no need of lithography, template, and chemical precursor.

ρ i is the host electron density at atom i induced by all of the

ρ i is the host electron density at atom i induced by all of the other atoms in the system as follows: (5) where ρ i (r ij ) is the contribution to the electronic density at the site of the atom i, and r ij is the distance between the atoms i and j. Because diamond is much harder than copper, the diamond tool and indenter are both treated as a rigid body in the simulation. Therefore, the atoms in the tool are fixed to each other relatively,

and no potential is needed to describe the interaction between diamond atoms (C-C) [13]. The interaction between copper atoms and diamond atoms (Cu-C) is described by the Morse potential [14]. Although a two-body potential may lead to less accurate solutions selleck compound than a many-body potential does, its parameters can be accurately calibrated by Selleckchem Osimertinib spectrum data. For the Morse potential [14], the two-body potential energy is expressed as follows: (6) where V(r) is the potential energy, D is the cohesion energy,

α is the elastic modulus, and f ii is the second derivative of the potential energy V(r) with respect to the bond length r ij . r ij and r 0 are the instantaneous and equilibrium distances between two atoms, respectively. Table  1 shows magnitudes of these parameters. Table 1 Parameters in the standard Morse potential[14] C-Si Parameter D (eV) 0.087 α (Å−1) 5.14 r 0 (Å) 2.05 MD simulation setup In order to reduce the selleck products boundary effect and size effect, the model scale should be large. As a result, the simulation becomes computationally expensive. To avoid these problems, the periodic boundary condition is set along the Z direction [14]. The specimen surface of the X-Z plan is machined, so it is a free surface. Both Fludarabine the diamond tool and the diamond indenter are set as a rigid body. This was followed by an energy minimization to avoid overlaps in the positions of the atoms. The simulation model was equilibrated to

296 K under the microcanonical (NVE) ensemble, and the initial velocities of the atoms were assigned in accordance with the Maxwell-Boltzmann distribution. Figure  2 shows the simulation procedure of the nanoindentation test on the machining-induced surface. Firstly, the diamond tool cuts the surface along the [ī00] direction for the first time in the X-Z plane (Figure  2a, (1)). After the nanocutting stage, the relaxation starts, in which the tool is fixed in its final position and the fixed boundaries are removed so that the system can be relaxed back to another state of equilibrium (Figure  2b). Then, the diamond indenter moves along the [00ī] direction (as shown in Figure  2a (2) and returns to its initial position (3)). Figure 2 Schematic of nanoindentation tests on machining-induced surface and traces of the diamond indenter and diamond tool.

J Appl Phys 2011, 109:013710 CrossRef 2 Hurley PK, Stesmans A, A

J Appl Phys 2011, 109:013710.CrossRef 2. Hurley PK, Stesmans A, Afanas’ev VV, O’Sullivan BJ, O’Callaghan E: Analysis of P b centers at the Si(111)/SiO2 interface following rapid thermal annealing. J Appl Phys

2003, 93:3971.CrossRef 3. Stesmans A, Van Gorp G: Maximum density of P b centers at the (111) Si/SiO2 interface after vacuum anneal. Appl Phys Lett 1990, 57:2663.CrossRef 4. Akca IB, Dâna A, Aydinli A, Turan R: Comparison of electron and hole charge–discharge dynamics in germanium nanocrystal flash memories. Appl Phys Lett 2008, 92:052103.CrossRef 5. Hdiy AE, Gacem K, Troyon M, Ronda A, Bassani F, Berbezier I: Germanium nanocrystal density and size effects on carrier storage and emission. J Appl Phys 2008, 104:063716.CrossRef 6. Weissker H-C, Furthmüller J, Bechstedt F: Optical properties of Ge and Si nanocrystallites from ab initio calculations. II. Hydrogenated nanocrystallites. Phys Rev B 2002, 65:1553282. check details 7. Mao LF: Quantum size impacts on the threshold voltage in nanocrystalline silicon thin film transistors.

Microelectron Reliab in press 8. Mao LF: Dot size effects of nanocrystalline germanium on charging dynamics of memory devices. Nanoscale Res Lett 2013, 8:21.CrossRef 9. Sze SM, Kwok , Ng K: Physics of Semiconductor Devices. New York: Wiley; 2007:213–215. 10. Ando Y, Itoh T: Calculation of transmission tunneling current across arbitrary potential barriers. J Appl Phys 1987, 61:1497.CrossRef 11. Adikaari AADT, Carey find more JD, Stolojan V, Keddie JL, Silva SRP: Bandgap

enhancement of layered nanocrystalline silicon from excimer laser crystallization. Nanotechnology 2006, 17:5412.CrossRef 12. Yue G, Kong G, Zhang D, Ma Z, Sheng S, Liao X: Dielectric response IMP dehydrogenase and its AZD6738 solubility dmso light-induced change in undoped a-Si:H films below 13 MHz. Phys Rev B 1998, 57:2387.CrossRef 13. Matsuura H, Okuno T, Okushi H, Tanaka K: Electrical properties of n-amorphouslp/p-crystalline silicon heterojunctions. J Appl Phys 1984, 55:1012.CrossRef 14. Teo LW, Ho V, Tay MS, Choi WK, Chim WK, Antoniadis DA, Fitzgerald EA: Dependence of nanocrystal formation and charge storage/retention performance of a tri-layer insulator structure on germanium concentration and tunnel oxide thickness. The 4th Singapore-MIT Alliance Annual Symposium: January 19–20, 2004; Singapore. 15. Teo LW, Choi WK, Chim WK, Ho V, Moey CM, Tay MS, Heng CL, Lei Y, Antoniadis DA, Fitzgerald EA: Size control and charge storage mechanism of germanium nanocrystals in a metal-insulator-semiconductor structure. Appl Phys Lett 2002, 81:3639.CrossRef 16. Kan EWH, Koh BH, Choi WK, Chim WK, Antoniadis DA, Fitzgerald EA: Nanocrystalline Ge flash memories: electrical characterization and trap engineering. The 5th Singapore-MIT Alliance Annual Symposium: January 19–20, 2005; Singapore Competing interests The author declares that he/she has no competing interests.

Authors’ contributions JMC was the primary investigator,

Authors’ contributions JMC was the primary investigator,

designed the study, obtained grant funds, supervised subject recruitment, data acquisition, data specimen collection, and manuscript IWR-1 preparation. MWR, RG, and HJ performed data specimen analysis. JMC was primarily responsible for writing the manuscript. TM, RW, SASC, and VP made substantial contributions to manuscript writing and preparation. All authors read and approved the final manuscript.”
“Erratum to: Osteoporos Int (2006) 17: 426—432 DOI 10.1007/s00198-005-0003-z Owing to a technical error, a number of non-vertebral fractures had not been included in the database. Owing to changes in the Milciclib clinical trial informed consents for some of the participants, at the time of repeated analyses, the study cohort changed from 27,159 to 26,905 participants. A total of 1,882 non-vertebral fractures (not 1,249 as stated in the publication) were registered. After excluding all subjects with missed measurements of any metabolic syndrome criteria (n = 152), 750 men and 1108 women (not 438 men and

789 women as stated in the publication) suffered non-vertebral fractures. The risk estimates of the associations between having three or more of the metabolic syndrome criteria and non-vertebral fractures Pifithrin �� and changed to (RR 0.81, 95% CI 0.64–1.04) in men and (RR 0.78, 95% CI 0.65–0.93) in women. The trend towards reduced fracture risk by increasing mean BP in men was no longer significant

(Fig. 2). We apologize for any inconvenience caused by this unfortunate error.”
“Background MRI plays a key role in the preclinical development of new drugs, diagnostics and their delivery systems. However, very high installation and running cost of existing superconducting MRI machines limit the spread of the method. The new method of Benchtop-MRI (BT-MRI) has the potential to overcome this limitation due to much lower installation and almost no running costs. The lower quality of the NMR images is expected due to the low field strength and decreased magnet homogeneity. However, very recently we could show that BT-MRI is able to characterize floating Dapagliflozin mono- or bilayer tablets, osmotic controlled push-pull tablets [1–4] or scaffolds for tissue engineering in vitro [5]. A broad, important and increasing range of MRI applications are linked with preclinical studies on small rodents such as mice or rats [6–8]. Thereby, first developments and testing of more compact MRI systems have been reported [9, 10]. In the present study we have tested a prototype of a new in vivo BT-MRI apparatus. Clearly, BT-MRI could overcome one of the current main limitations of preclinical MRI, the high costs. However, the question arises, whether BT-MRI can achieve sufficient image quality to provide useful information for preclinical in vivo studies.

These results concur with patterns observed in coastal lowland fo

These results concur with patterns observed in coastal lowland forests of eastern Australia (Pharo et al. 1999), but they contradict results from forests of the Azores and in

Selleckchem PLX4032 Indonesia in which no correlations were found among bryophytes, macrolichens, and vascular plant cover (Kessler et al, in press; Gabriel and Bates 2005). These studies, however, did not separate liverworts from mosses, nor between epiphytic and terrestrial species. Overall, numerous studies have found that patterns of alpha diversity between different higher level taxa show only limited correlation (e.g., Lawton et al. 1998; Schulze et al. 2004; Tuomisto and Ruokolainen 2005; McMullan-Fisher 2008). Beta diversity The variability of beta diversity as revealed

by additive partitioning showed that species turnover is highly dependent the spatial scale. Generally, we found more variation in species richness between taxonomic groups within smaller spatial scales (plot) than on the regional scale. Nevertheless, by adding all species of one taxonomic group of one study site, we recorded only 55–65% of regional species richness, with the tendency of higher proportions in the epiphytic habitat. This marked regional differentiation is noteworthy bearing in mind that our study Dibutyryl-cAMP cell line taxa disperse by spores and are usually widespread, occurring well beyond the range spanned by our study sites (Gradstein et al. 2007; Kürschner and Parolly 2007; Lehnert et al. 2007; Nöske et al. 2007). Causes for this regional differentiation may involve slight climatic and geological differences between the three study sites (Gradstein et al. 2008) as well as stochastic dispersal and this website extinction

events (Wolf 1994). Ferns showed greater differences between terrestrial and epiphytic patterns at Alanine-glyoxylate transaminase the plot level than any other study group. Although in the terrestrial habitat, ca. 12% of total diversity was occurred in sampling one plot, this amount was more than doubled in the epiphytic habitat. The majority of terrestrial ferns are relatively large (e.g., Cyatheaceae, Dryopteridaceae) compared to the majority of epiphytic taxa (e.g., Hymenophyllaceae, Polypodiaceae), which may explain the lower density of terrestrial fern species on the relatively small plots. Correlations of beta diversity among our plant groups (lichens not included due to low species richness) were higher in the terrestrial than in the epiphytic habitat, and most pronounced for mosses and liverworts. Overall, congruence of beta diversity patterns among study groups was lower than that of alpha diversity. This implies that at least for our studied taxa, the use of an indicator group as a surrogate for others is more applicable for species richness than for community composition. This finding contrasts with studies among vascular plants in lowland Amazonia (Tuomisto and Ruokolainen 2005; Barlow et al.

​cgi?​taxid=​5833and PlasmoDB [23] databases The remaining 14 in

​cgi?​taxid=​5833and PlasmoDB [23] databases. The remaining 14 insertions either mapped to telomeric repetitive elements or could not be mapped to a chromosomal location through BLAST SB273005 solubility dmso searches of public databases. The identifiedpiggyBacinsertion sites were distributed throughout LOXO-101 ic50 the

genome in all 14P. falciparumchromosomes (Fig.2a) with no bias for any particular chromosome (Fig.2b). AllpiggyBacinsertions were obtained in the expected TTAA target sequences except two that integrated into TTAT and TTAG sequences. As in other organisms [17,20],piggyBacpreferentially inserted into predicted transcribed units ofP. falciparumgenome (Fig.3a), affecting 178 transcription units. Thirty-six of the insertions resulted in direct disruption of open reading frames (ORFs) and 3 insertions 4SC-202 purchase were mapped to introns. A vast majority of insertions (119) occurred in 5′ untranslated regions (UTRs) whereas only a few (22) were obtained in 3′ UTRs (Additional file 1). Figure 2 Distribution of piggyBac insertion

sites in the P. falciparum genome.(a)A representation of the 14P. falciparumchromosomes withpiggyBacinsertion loci (represented by red vertical lines) shows extensive distribution ofpiggyBacinsertions through out the parasite genome.(b)Comparison of chromosomal distribution ofpiggyBacinsertions to the percent genome content of each chromosome shows unbiased insertions intoP. falciparumgenome. Plot and curve fits of percentpiggyBacinsertions and percent chromosome size are depicted in the inset. Figure 3 piggyBac insertions in the genome are random but preferentially occur in 5′ untranslated regions. (a) Genomic transcription units were defined to include 2 kb of 5′ UTR, the coding sequence, the introns and 0.5 kb of 3′ UTR, based on previous studies oxyclozanide inPlasmodium[48,49]. (b) Comparison of gene functions of all annotated genes in the genome (outer circle) to genes inpiggyBac-inserted loci (inner circle) shows an equivalent distribution confirming random insertions in the parasite genome. (c) Comparison of stage-specific expression of all annotated genes (outer circle) to those inpiggyBac-inserted

loci (inner circle) validates the ability ofpiggyBacto insert in genes expressed in all parasite life cycle stages. (d) A comparison ofpiggyBac-inserted TTAA sequences to TTAA sequences randomly selected from the genome showed preferential insertion ofpiggyBacinto 5′ UTRs of genes (asterisk- χ2test, df 1, P = 1.5 × 10-12) whereas a significantly lower number of insertions were observed in CDS and introns (double asterisks- χ2test, df 1, P = 1.09 × 10-13). piggyBacinserts randomly into all categories of genes with a strong preference for 5′ untranslated regions Obtaining unbiased insertions into the genome is critical for whole-genome mutagenesis and other large-scale analyses. Hence, we evaluated the randomness ofpiggyBacinsertions into theP.

Figure 8 Antitumor effect of various nanoparticles in comparison

Figure 8 Antitumor effect of various nanoparticles in comparison with that of PBS. Figure 9 Representative H&E staining of tumors. Treated with PBS (A), TRAIL-loaded TPGS-b-(PCL-ran-PGA)/PEI nanoparticles (B), endostatin-loaded TPGS-b-(PCL-ran-PGA)/PEI nanoparticles (C), and TRAIL and endostatin-loaded TPGS-b-(PCL-ran-PGA)/PEI nanoparticles (D). In future studies, we will investigate the combined effect of TRAIL/endostatin gene therapy and chemotherapeutic agents such as doxorubicin, docetaxel, and floxuridine, encapsulated

in TPGS-b-(PCL-ran-PGA) nanoparticles, in different cervical cancer cell lines and animal models in order to make clear whether a combination of TRAIL/endostatin gene therapy and chemotherapy will have enhanced antitumor activity. We hypothesize that surface modification of TPGS-b-(PCL-ran-PGA) Salubrinal purchase nanoparticles with polyethyleneimine may also be a promising and useful drug and gene co-delivery system. Combretastatin A4 price Conclusions For the first time, a novel TPGS-b-(PCL-ran-PGA) nanoparticle

modified with polyethyleneimine was applied to be a vector of TRAIL and endostatin for cervical cancer gene therapy. The data showed that the nanoparticles could efficiently deliver plasmids into HeLa cells and the expression of TRAIL and endostatin was verified by RT-PCR and Western blot analysis. The cytotoxicity of the HeLa cells was significantly increased by TRAIL/endostatin-loaded nanoparticles when compared with control groups. Synergistic antitumor activities could be obtained by the use of combinations of TRAIL, endostatin, and TPGS. The images of H&E staining also indicated that tumor growth treated by TRAIL- and endostatin-loaded TPGS-b-(PCL-ran-PGA)/PEI nanoparticles was significantly inhibited in comparison with that of the PBS control. In conclusion, the TRAIL/endostatin-loaded nanoparticles offer considerable potential as an ideal candidate for in vivo cancer gene

delivery. Acknowledgements The authors gratefully acknowledge the financial support from the Natural this website Science Foundation of Guangdong Province (S2012010010046), Science, Technology and Innovation Commission of Shenzhen Municipality (JC200903180532A, JC200903180531A, Resminostat JC201005270308A, KQC201105310021A, and JCYJ20120614191936420), Doctoral Fund of Ministry of Education of China (20090002120055), Nanshan District Bureau of Science and Technology, National Natural Science Foundation of China (31270019, 51203085), and Program for New Century Excellent Talents in University (NCET-11-0275). References 1. Parkin DM, Bray F, Ferlay J, Pisani P: Estimating the world cancer burden: Globocan 2000. Int J Cancer 2001, 94:153–156.CrossRef 2. Ma Y, Huang L, Song C, Zeng X, Liu G, Mei L: Nanoparticle formulation of poly(ε-caprolactone-co-lactide)-d-α-tocopheryl polyethylene glycol 1000 succinate random copolymer for cervical cancer treatment. Polymer 2010, 51:5952–5959.CrossRef 3.

CrossRef 31 Konradsen HB: Validation of serotyping of Streptococ

CrossRef 31. Konradsen HB: Validation of serotyping of Streptococcus EPZ004777 manufacturer pneumoniae in Europe. Vaccine 2005,23(11):1368–1373.PubMedCrossRef 32. Richards JC, Perry MB, Moreau M: Elucidation and comparison of the chemical structures of the specific capsular polysaccharides of Streptococcus pneumoniae groups 11 (11F, 11B, 11C, and 11A). Adv Exp Med Biol 1988, 228:595–596. 33. Briles DE, Tart RC, Swiatlo E, Dillard JP, Smith P, Benton KA, Ralph BA, Brooks-Walter A, Crain MJ, Hollingshead SK, et al.: Pneumococcal diversity: considerations for new vaccine strategies with emphasis on pneumococcal surface protein A (PspA).

Clin Microbiol Rev CRT0066101 concentration 1998,11(4):645–657.PubMed 34. Rosenow C, Ryan P, Weiser JN, Johnson S, Fontan P, Ortqvist A, Masure HR: Contribution of novel choline-binding proteins to adherence, colonization and immunogenicity of Streptococcus pneumoniae . Mol

Microbiol 1997,25(5):819–829.PubMedCrossRef 35. Hollingshead SK, Becker R, Briles DE: Diversity of PspA: mosaic genes and evidence for past recombination in Streptococcus pneumoniae . Infect Immun 2000,68(10):5889–5900.PubMedCrossRef 36. Iannelli check details F, Oggioni MR, Pozzi G: Allelic variation in the highly polymorphic locus pspC of Streptococcus pneumoniae . Gene 2002,284(1–2):63–71.PubMedCrossRef 37. Barocchi MA, Ries J, Zogaj X, Hemsley C, Albiger B, Kanth A, Dahlberg S, Fernebro J, Moschioni M, Masignani V, et al.: A pneumococcal pilus influences virulence and host inflammatory responses. Amylase Proc Natl Acad Sci USA 2006,103(8):2857–2862.PubMedCrossRef 38. Zahner D, Gudlavalleti A, Stephens DS: Increase in pilus islet 2-encoded pili among Streptococcus pneumoniae isolates, Atlanta, Georgia, USA. Emerg Infect Dis 2010,16(6):955–962.PubMed 39. Poulsen K, Reinholdt J, Kilian M: Characterization of the Streptococcus pneumoniae immunoglobulin A1 protease gene ( iga ) and its translation product. Infect Immun 1996,64(10):3957–3966.PubMed 40. Oggioni MR, Memmi G, Maggi T, Chiavolini D, Iannelli F, Pozzi G: Pneumococcal

zinc metalloproteinase ZmpC cleaves human matrix metalloproteinase 9 and is a virulence factor in experimental pneumonia. Mol Microbiol 2003,49(3):795–805.PubMedCrossRef 41. Camilli R, Pettini E, Del Grosso M, Pozzi G, Pantosti A, Oggioni MR: Zinc metalloproteinase genes in clinical isolates of Streptococcus pneumoniae : association of the full array with a clonal cluster comprising serotypes 8 and 11A. Microbiology 2006,152(2):313–321.PubMedCrossRef 42. Chiavolini D, Memmi G, Maggi T, Iannelli F, Pozzi G: The three extra-cellular zinc metalloproteinases of Streptococcus pneumoniae have a different impact on virulence in mice. BMC Microbiology 2003, 3:14.PubMedCrossRef 43. Serizawa M, Sekizuka T, Okutani A, Banno S, Sata T, Inoue S, Kuroda M: Genomewide screening for novel genetic variations associated with ciprofloxacin resistance in Bacillus anthracis . Antimicrob Agents Chemother 54(7):2787–2792. 44.