Front Cover: In article 1800305, Lei Zhou, Guoxin Tan, and co-workers report a facile approach based on dynamic metal-ligand coordination chemistry between chondroitin sulfate (CS) and Fe3+ in the design and synthesis of a novel multifunctional metallohydrogel bioadhesive. This CS-based hydrogel not only exhibits an excellent self-healing ability and injectability, but also undergoes a rapid gel–sol transformation in response to multiple external stimuli, including pH, ions, neutral molecules, and chemical redox reactions.
A terminal composition prediction model of steel using reference heat method was established aiming to the alloys composition prediction of Ladle Furnace,and the application software was also developed.The running results of the model show that,within the composition rang of various steel grades,the accuracy of predication of carbon,manganese and silicon contents can reach 100%,while that of aluminum can reach 98%.The development of this model has some direction significance and wide application field.
Insufficient vascularization is a primary cause of bone implantation failure. The management of energy metabolism is crucial for the achievement of vascularized osseointegration. In light of the bone semiconductor property and the electric property of semiconductor heterojunctions, a three-dimensional semiconductor heterojunction network (3D-NTBH) implant has been devised with the objective of regulating cellular energy metabolism, thereby driving angiogenesis for bone regeneration. The three-dimensional heterojunction interfaces facilitate electron transfer and establish internal electric fields at the nanoscale interfaces. The 3D-NTBH was found to noticeably accelerate glycolysis in endothelial cells, thereby rapidly providing energy to support cellular metabolic activities and ultimately driving angiogenesis within the bone tissue. Molecular dynamic simulations have demonstrated that the 3D-NTBH facilitates the exposure of fibronectin's Arg-Gly-Asp peptide binding site, thereby regulating the glycolysis of endothelial cells. Further evidence suggests that 3D-NTBH promotes early vascular network reconstruction and bone regeneration in vivo. The findings of this research offer a promising research perspective for the design of vascularizing implants.
Although Chinese constitution does not express the freedom of the press,but this principle can be drawn from the relevant provisions of the constitution.In the real social life we need the supervision of the media,but because the imperfect system can not effectively lead the one hand,the standard media monitoring,media monitoring on the other hand causing undue suppression.By the relevant foreign experience in judging the infringement of the media,the judgement should be based on media,rather than merely by the feelings of news reports.
The bulk metal forming processes are calculated and analyzed by using a multi- step finite element method (FEM) based on deformation theory of plasticity. In this method, FEM solution is implemented to minimize approximated plastic potential in static equilibrium by constraint variation principle, for incompressible rigid-plastic materials. The multi-step simulation deals with the fictitious sliding constraints for intermediate configurations and iterations step by step along the deformation path, considering the contact and deformation history, which could provide rapid analysis for more complicated bulk forming problems. The one-step and multi-step forward simulations of several typical bulk metal forming problems are performed by this method, the calculated results of which are compared with those obtained by incremental FEM. The results indicate: multi-step FEM simulation of the bulk metal forming processes could give the reasonable answers with a small amount of computing time, the errors of which are less 10% compared with those of incremental FEM.
Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy. We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine. We identified 20 genes exhibiting a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. The results show that variation at the level of genes involved in membrane trafficking and antigen processing significantly influences the human response to influenza vaccination. More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits. https://doi.org/10.7554/eLife.00299.001 eLife digest Vaccines increase resistance to disease by priming the immune system to respond to specific viruses or microorganisms. By presenting a weakened (or dead) form of a pathogen, or its toxins or surface proteins, to the immune system, vaccines trigger the production of antibodies against the virus or microorganism. If a vaccinated individual then encounters the pathogen, their immune system should be able to recognize and destroy it. Many vaccines also include a secondary agent, known as an adjuvant, to further stimulate the immune response. Influenza, an RNA virus commonly referred to as the 'flu', is an infectious disease that affects both birds and mammals. Seasonal epidemics occur each year affecting 2–7% of the population. According to the World Health Organization, influenza leads to nearly 5 million hospitalizations each year and causes up to half a million deaths. Vaccination is a primary strategy for the prevention of seasonal influenza, but responses to the vaccine vary markedly, partly because of variation in the genetic makeup or genotype of individuals. However, the details of how genes influence response to vaccination, and indeed susceptibility to influenza, remain unclear. To investigate the genetic basis of variation in the immune response of healthy adults to the seasonal influenza vaccine, Franco et al. combined information about the genotypes of individuals with measurements of their gene transcription and antibody response to vaccination. They identified 20 genes that contributed to differential immune responses to the vaccine. Almost half of these encode proteins that are not specifically associated with the immune system, but have more general roles in processes such as membrane trafficking and intracellular transport. Focusing on these genes may enable researchers to spot those individuals who are less likely to respond to a vaccine. It could also open up new avenues of research for vaccine development: rather than designing adjuvants that target known immune mechanisms, researchers should develop adjuvants that target the proteins encoded by these 20 genes. https://doi.org/10.7554/eLife.00299.002 Introduction Influenza remains one of the major threats to human health worldwide and is responsible for an estimated 250,000–500,000 deaths each year (World Health Organization, 2009). Attempts at immunization pre-dated the isolation of the virus from humans in 1933 (Smith et al., 1933) and vaccination remains the cornerstone of prevention strategies. Since 1977, strains of influenza A (H3N2), influenza A (H1N1), and influenza B have been responsible for the majority of documented human infections and trivalent vaccines are updated annually to contain the circulating strains. Animal models have demonstrated that immune responses and susceptibility to influenza infection can be strongly influenced by host genetic factors (Trammell and Toth, 2008; Srivastava et al., 2009). As with viral infection, variability in the immune response to vaccination is likely to be influenced by genotype. Accordingly, twin and sibling studies have shown heritability estimates as high as 45% for a varicella vaccine (Klein et al., 2007) and 90% for a measles vaccine (Tan et al., 2001). Studies investigating influenza vaccine immunogenicity in humans have consistently shown large inter-individual variability, but the genetic contribution to this variability remains poorly understood. Gene expression is strongly controlled by common genetic variants (Morley et al., 2004; Stranger et al., 2007) with both broad (Bullaughey et al., 2009) and tissue-specific effects (Innocenti et al., 2011; Rotival et al., 2011), referred to as expression quantitative trait loci (eQTL). Moreover, genome-wide association studies have identified hundreds of variants associated with human disease risk that are also eQTL, implying that the mechanism by which they influence risk involves variation in transcriptional responses (Emilsson et al., 2008; Cookson et al., 2009; Naukkarinen et al., 2010; Nicolae et al., 2010; Rotival et al., 2011; Barreiro et al., 2012) Finally, integrative genomic studies in model organisms (Schadt et al., 2005; Amit et al., 2009) have demonstrated that the combination of genetic and transcriptional information can allow direct tests of causal mechanisms in controlled experiments. We hypothesized that integrating genome-wide genotype data with serial measurements of the transcriptional and humoral responses to an influenza vaccine in a clinical study could be used to identify loci that influence vaccine responsiveness and subsequent immunity to influenza in humans. We immunized an ethnically homogeneous group of 119 healthy adult male volunteers with licensed trivalent influenza vaccine. DNA was obtained from peripheral blood and genome-wide SNP genotyping was performed. We also measured global transcript abundance in peripheral blood RNA specimens before and at three time points (days 1, 3, and 14) after vaccination. Type-specific antibody measurements (H1N1, H3N2, and FluB) were made in serum samples before and at two time points (days 14 and 28) after vaccination. An identical study was then carried out with an independent validation cohort of 128 ethnically homogeneous healthy adult female volunteers. This experimental design allowed us to search for loci that show evidence of a transcriptional response to vaccination, genetic regulation of gene expression (cis-acting eQTL), and correlation between gene expression and the magnitude of the antibody response. Results Multiple genes show evidence of a transcriptional response to the vaccine and genetic regulation of expression We performed mixed model regression analysis with SNPs located in 1-Mb intervals around each expression reporter sequence. We began by identifying SNP-transcript pairs with both significant evidence of a cis-acting eQTL and significant changes in gene expression in response to vaccination. Thresholds for local significance were initially explored, since only SNPs flanking each reporter sequence were tested for cis association. In the discovery cohort, 3229 SNP-transcript pairs, corresponding to 408 unique genes, exhibited significant genotype-expression association (genotype effect p<1 × 10−4) and concomitant evidence of a transcriptional response to the vaccine (day effect p<0.01). Of these, 2606 SNP-transcript pairs, corresponding to 256 genes, were validated in the independent cohort of female volunteers (genotype effect p<0.05 and day effect p<0.01). When more stringent thresholds were applied, 756 SNP-transcript pairs, corresponding to 114 unique genes, exhibited significant genotype-expression association (genotype effect p<5 × 10−8) and concomitant evidence of a transcriptional response to the vaccine (day effect p<0.01) in the discovery cohort. Of these, 654 SNP-transcript pairs, corresponding to 93 genes, were validated in the second cohort (genotype effect p<0.05 and day effect p<0.01). A majority of these (467 SNP-transcript pairs, corresponding to 78 unique genes) would pass equally stringent thresholds in both cohorts (genotype effect p<5 × 10−8, day effect p<0.01). A Manhattan plot of these results is presented in Figure 1. Data for the individual SNP-transcript pairs that passed equally stringent thresholds in both cohorts, including results of significance testing and gene identifiers, are provided in Table 1 via the Interactive Results Tool (which is also available to download from Zenodo and shown within Supplementary file 1). Figure 1 Download asset Open asset Multiple genes show both a transcriptional response to the vaccine and evidence of genetic regulation of gene expression (cis-acting eQTL) in both cohorts. Manhattan plots of the genotype-expression—log10 p-values across the genome for the discovery (inner circle) and validation (outer circle) cohorts. Each dot represents a SNP-transcript pair. Red dots indicate SNP-transcript pairs for which there is evidence of significant genotype-expression association (genotype p<5 × 10−8) and evidence of a transcriptional response to the vaccine (day effect p<0.05). The 78 genes that showed both properties in the two cohorts are shown in the outer margin. https://doi.org/10.7554/eLife.00299.003 At some loci, the genetic effect is enhanced or only apparent after the experimental perturbation We hypothesized that, at some loci, the magnitude of the genetic effect could be different before and at different time points after vaccination. This type of effect, which would not be observed in a cross-sectional study design, could be directly examined with our serial expression data. We analyzed the additive effect of genotype on expression at each day in the study. Using a cis-effect significance threshold of p<1 × 10−4 in the discovery cohort and p<0.05 in the validation cohort, this analysis identified 5155 validated eQTL SNP-transcript pairs (3011 at baseline and 3417, 2496, and 3043 at days 1, 3, and 14, respectively). These SNP-transcript pairs correspond to 543 unique genes. We then identified the SNP-transcript pairs in which the expression variance explained was most strongly increased after vaccination (highest change in genetic variance explained, which we termed delta-Rg2). This analysis revealed multiple loci at which the genetic effect was either enhanced or only apparent after the experimental perturbation. An example is presented in Figure 2A, which displays local Manhattan plots for the NECAB2 locus before and 3 days after vaccination in both cohorts. Figure 2 Download asset Open asset At some loci, the magnitude of the genetic effect changes after the experimental perturbation. (A) A specific example of this phenomenon: local Manhattan plots for the gene NECAB2 before and on day 3 after vaccination in each of the two cohorts, showing an increase in the magnitude of the genotype effect (R2g) after the experimental perturbation. (B) An increase in R2g after the experimental perturbation is a general feature of the SNP-transcript pairs that show a strong cis-eQTLs and a transcriptional response to vaccination (left). The within-genotype variance is unchanged (MSE, center), while the strength of the genotype effect on expression (slope of the additive association; β, right) increases, suggesting that the latter is the main driver for the observed increase in the genetic effect after vaccination. https://doi.org/10.7554/eLife.00299.004 Theoretically, the observed temporal changes in the estimated genotype effect after vaccination could be driven by an increase in the effect size, a relative decrease in the variability within genotype strata, or both. We analyzed all SNP-transcript pairs for loci at which we observed both a strong cis-acting eQTL and a transcriptional response to vaccination, calculating the relative magnitude of slope and within-genotype variance between the pre-vaccination and maximal Rg2 time points. Figure 2B shows that an increase in the strength of the genotype effect (slope of the additive association) was the main driver for the observed change in Rg2, and that this amplitude change was a general feature of the loci in which we observed both a strong cis-acting eQTL and a transcriptional response to the vaccine stimulus. The delta-Rg2 values were consistent between the cohorts when evaluated by Spearman's rank correlation analysis using all SNP-transcript pairs (Cor = 0.25, p<2 × 10−16). To select a conservative set of candidate loci based on this property for further analysis, we identified the SNP-transcript pairs that were in the top 1% of the delta-Rg2 distribution and also showed evidence of a strong cis-acting eQTL (genotype effect p<5 × 10−8), in both cohorts. Data for the resulting set of 146 SNP-transcript pairs, including Rg2 values, are provided in Table 2 via the Interactive Results Tool (which is also available to download from Zenodo and shown within Supplementary file 1). Content analysis shows enrichment for genes involved in membrane trafficking, antigen processing, and antigen presentation Of the 78 genes that had the strongest validated evidence of a genotype effect and a transcriptional response to the vaccine, 14 were also in the list of 34 genes with the strongest evidence of an increase in the magnitude of the genetic effect after vaccination. Content analysis on the union of the two sets (98 genes) showed significant enrichment for genes involved in antigen processing and presentation, cytotoxic T-lymphocyte-mediated apoptosis of target cells, dendritic cell maturation and function, and membrane trafficking (Figure 3). Figure 3 Download asset Open asset Content analysis shows enrichment for genes involved in membrane trafficking, antigen processing, and antigen presentation. Barplots show categories with significant overrepresentation in the list of 98 genes with a strong cis-eQTL and a response to vaccination expressed as either a transcriptional response or a change in the genetic effect in both cohorts. The negative log(p-value) is plotted on the x-axis. https://doi.org/10.7554/eLife.00299.005 Integration of genotype, expression, and antibody titer data identifies 20 genes with the strongest evidence for genetic variation influencing the humoral immune response to influenza vaccination We and others have shown that for some transcripts there is significant correlation between the magnitude of the transcriptional and antibody responses to the vaccine stimulus (Zhu et al., 2010; Bucasas et al., 2011; Nakaya et al., 2011) In a combined analysis of the two cohorts in the present study, 301 transcripts were found to correlate with the magnitude of the antibody response (Figure 4). Additional details of these 301 transcripts, including correlation coefficients and days of maximum correlation, are provided in Table 3 via the Interactive Results Tool (which is also available to download from Zenodo and shown within Supplementary file 1). We imposed an additional selection threshold based on this correlation, and identified 20 genes that show evidence of significant genotype-expression association (genotype effect p<5 × 10−8), a significant correlation between the transcriptional and antibody responses (expression-antibody effect p<0.05), and either a transcriptional response to the vaccine (day effect p<0.01) or evidence of a change in the magnitude of the genetic effect after vaccination (top 1% of the delta-Rg2 distribution) in the two independent cohorts. These loci have the strongest evidence of genetic variation influencing the immune response to the vaccine, and include TAP2, SNX29, FGD2, NAPSA, NAPSB, GM2A, C1orf85, JUP, FBLN5, CHST13, DIP2A, PAM, D4S234E, C3AR1, HERC2, LST1, LRRC37A4, OAS1, RPL14, and DYNLT1. Remarkably, seven of these encode proteins involved in intracellular antigen transport and processing (Figure 5). Figure 4 Download asset Open asset Gene expression at specific loci correlates with the antibody response to vaccination. (A) Examples of positive (DYNLT1) and negative (ANKRD33) correlation between gene expression on day 1 and the magnitude of the antibody response to the vaccine. Data points and regression lines in the scatterplots display the results for the discovery (blue) and validation (magenta) cohorts. (B) A total of 301 genes showed evidence of significant correlation between gene expression and the antibody response to the vaccine in both cohorts. Of these, 281 showed evidence of positive correlation and 83 of negative correlation. Each individual is represented by a column in the heatmaps. The top heatmaps display the magnitude of the antibody response (titer response index). The bottom heatmaps display the deviations around the expression mean for each gene. Individual gene identifiers and correlation coefficients are presented in the Interactive Results Tool. https://doi.org/10.7554/eLife.00299.006 Figure 5 Download asset Open asset Genetic variation in intracellular antigen transport and processing influences the human immune response to influenza vaccination. 20 genes show evidence of a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. Remarkably, seven of these are involved in intracellular antigen transport, antigen processing, and antigen presentation. https://doi.org/10.7554/eLife.00299.007 We determined genetic associations to the antibody response using 137 eQTL SNPs from these 20 loci. The quantile-quantile plot from the association tests performed on these SNPs shows marked deviation from the empirical null distribution for QTL associations (Figure 6), supporting the idea that these loci are enriched for true genetic associations. Figure 6 Download asset Open asset SNPs at the 20 loci identified show evidence of association with the antibody response to the vaccine. 137 SNP-transcript pairs with evidence of a strong cis-eQTL, a dynamic response to the vaccine (a change in transcript abundance or in the magnitude of the genetic effect), and correlation between the transcriptional and antibody responses were selected (result SNPs, in red). The empirical quantile-quantile plot of the result SNPs shows significant deviation from the empirical distribution of the entire data set (background SNPs, in blue). https://doi.org/10.7554/eLife.00299.008 The study design permits causal and reactive model analyses We explored three types of associations in our work: genotype to gene expression (eQTL), gene expression to antibody titer, and genotype to antibody titer (QTL). We now considered alternative models for the relationships between these distinct types of association (Figure 7A), and we evaluated our data to determine which of these alternatives appears most consistent with our observations. The alternative models considered were: (i) genotype association with gene expression is independent of genotype association or trends of association with antibody response (independent model); (ii) genotype association or trends of association with antibody response are mediated by gene expression patterns that are strongly correlated with genotype (causal model); and (iii) genotype associations to antibody response are not mediated by expression, but instead gene expression patterns are a response to the antibody trait or its early correlates (reactive model). To perform a comparative analysis of these alternatives we extended the framework for causal modeling (Pearl, 2010) in eQTL data recently developed by others (Millstein et al., 2009) and applied the method to our time-course gene expression study. We used the 137 eQTL SNP-transcript pairs from the 20 loci with the strongest evidence of genetic variation influencing the immune response to the vaccine, as described above. We found that the patterns in the data trend toward the causal model compared to the reactive model (Figure 7B), but a power analysis based on the distribution of the empirical effect sizes of our observed associations also indicates that our sample size is too modest to support definitive conclusions (Figure 7C). Figure 7 Download asset Open asset The study design permits causal and reactive model analyses. (A) Three models were evaluated, each showing a candidate hypothesis for the three-way association between genotype (G), expression (E) and trait (T). In the independent model, expression and trait each associate with genotype but are not themselves directly related. In the causal model, expression mediates the association between genotype and trait. In the reactive model, genotype and expression relate through the trait, so that gene expression changes are a downstream response to the trait. (B) p-values for independent-versus-reactive and independent-versus-causal hypothesis tests. Each point shows the result for one SNP-transcript pair. Points to the right of the solid vertical line are significant (p<0.05) for the reactive hypothesis and points above the solid horizontal line are significant for the causal hypothesis. The dashed line shows a p=0.1 threshold. (C) Power for rejection of the independent hypothesis. Non-independent data were simulated with effect sizes and variances similar to those in the enrichment set (the set of SNP-transcript pairs that were found to be significant in our study). The curve shows the proportion of cases in which the simulated data rejected the independent (null) hypothesis. The dotted line indicates the combined sample size in our study. https://doi.org/10.7554/eLife.00299.009 Discussion The results provide an unbiased integrative survey of the genetic and transcriptional components of the humoral immune response to influenza vaccination in humans. They suggest that variation at the level of genes involved in antigen processing and intracellular trafficking is an important determinant of vaccine immunogenicity. Even in healthy, young individuals, there are a significant number of people who do not develop a protective antibody response after influenza vaccination. If these individuals could be identified prior to vaccination, modifications to the type or dose of vaccine could be attempted, with the goal of reducing the number of unprotected vaccinated individuals. The genes identified in this study as playing a role in variation in the humoral response to vaccination would be a logical starting point for the development of DNA- or RNA-based predictive biomarkers. Prospective evaluation of such biomarkers would be the next step towards clinical implementation. Understanding the mechanisms that underlie variation in response to the vaccine may also direct modification of factors that enhance the response. Most of the efforts to date have focused on vaccine adjuvants that activate known immunologic mechanisms. Surprisingly, many of the genes identified in this study encode proteins that are not specifically immune but play a more general role in membrane trafficking and intracellular transport. Interventions aimed at increasing vaccine antigen affinity to these proteins or altering their intracellular concentrations could represent new avenues in vaccine development. More broadly, the results demonstrate that a longitudinal, integrative genomic analysis study design, applied to a clinical intervention, is an efficient alternative to cross-sectional methods for the identification of genes involved in medically relevant complex traits. By making repeated measurements on the same individual over time after a controlled experimental perturbation, we were able to account for individual variation in a way that would not have been possible otherwise. The dynamic nature of the measurements also allowed us to uncover genetic effects that are either enhanced by or only evident after the experimental perturbation. The specificity of gene identification in this study emerges from the genome's acute response to the perturbation, which cannot be assessed by a cross-sectional eQTL analysis or a genome-wide association study. This approach could be used for a broad variety of medically important problems whenever there is the opportunity to test a well-controlled intervention such as drug, dietary, or vaccine responses. Several limitations of the study are worth noting. First, we studied two samples of healthy young adults, thereby excluding the segments of the population that are most likely to have a poor response to influenza vaccination: children, the elderly, and individuals with severe illnesses. Second, in order to minimize the risk of false associations related to population stratification, we studied an ethnically homogeneous group of individuals. Third, while an interesting aspect of our study design is that it could open the door for direct comparisons of causal and reactive models, the sample size in this study was not sufficient to establish whether or not there is a causal relationship between the loci for which an association was identified and the antibody response to the vaccine. Finally, while antibody titers have historically been used to evaluate vaccine responsiveness, it is clear that they do not capture the complexity of the human immune response to vaccination. Additional studies would be necessary to determine whether the genes identified are also related to variation in influenza vaccine responses in groups other than the one chosen for this study, whether there is a causal relationship between these genes and the antibody response, or whether they also influence the cell-mediated immune response to the vaccine. Materials and methods Visual summary Request a detailed protocol A visual representation of the study design, the resulting data sets, and the integrative analysis scheme, is presented in Figure 8. Figure 8 Download asset Open asset Study design and integrative analysis scheme. (A) Individuals were immunized on day 0 and peripheral blood RNA samples were obtained on days 0, 1, 3, and 14. Antibody titers were measured on pre-immune sera and on days 14 and 28. Genotyping was carried out on a peripheral blood genomic DNA sample obtained on day 1. Identical sample collection schemes were used, 1 year apart, for the discovery (males) and validation (females) cohorts. (B) Sample sizes and data generation platforms. (C) Integrative analysis involved identification of loci that exhibit a transcriptional response to vaccination, evidence of genetic regulation of expression (constitutive eQTL), evidence of correlation between gene expression and the antibody response, and evidence of correlation between genotype and the antibody response (QTL). Because transcript abundance was measured serially, we were able to evaluate changes in the magnitude of the genetic effect on expression at different time points following vaccination. In addition, the study design permitted QTL analysis conditional on gene expression, which led to the identification of loci whose genetic effects on the antibody response are causally linked through the eQTL. https://doi.org/10.7554/eLife.00299.010 Study subjects Request a detailed protocol Healthy volunteers ages 18 to 40 years were enrolled. Individuals who were known to have received an influenza vaccine in the previous 3 years or who had signs or symptoms of an active infection at the time of enrollment were excluded. To minimize false-positive results related to population stratification, enrollment was limited to individuals of self-reported Caucasian ancestry. Enrollment, vaccination and sample collections were conducted at a university campus. The initial (discovery) cohort was restricted to males. The validation cohort was enrolled approximately 18 months after the initial cohort and was restricted to females. The protocol was approved by the institutional review boards of all participating institutions. Informed consent was obtained from each subject prior to enrollment. Vaccine Request a detailed protocol Study participants were immunized on day 0. Those enrolled in the initial cohort received the 2008–2009 inactivated trivalent influenza vaccine (A/Brisbane/59/2007[H1N1], A/Brisbane/10/2007[H3N2], B/Florida/4/2006; Sanofi-Pasteur, Lyon, France). The validation cohort received the 2009–2010 vaccine, which came from the same manufacturer and included (A/Brisbane/59/2007), (A/Brisbane/10/2007[H3N2]), and (B/Brisbane/60/2008) strains. DNA samples Request a detailed protocol Whole blood samples (7 ml) for DNA purification were collected in Vacutainer acid citrate dextrose (ACD) tubes (Beckton-Dickinson, Franklin Lakes, NJ), on day 1 after vaccination. DNA was purified using Qiagen Gentra Puregene Blood Kits (Qiagen Sciences, Germantown, MD). Quantitation and quality control were performed with a NanoDrop-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and using the Quant-iT PicoGreen dsDNA Reagent (Life Technologies, Carlsbad, CA) in a Tecan GENios microplate reader (Tecan Group, Mannendorf, Switzerland). RNA samples Request a detailed protocol Peripheral blood samples for RNA purification were obtained immediately before (day 0) and on days 1, 3, and 14 after vaccinati
Conductive polymers (CPs) are frequently used as neural and osteogenic implants for signal recording and electrical stimulation due to their electroactive and good biocompatibility. However, these implants often fail due to bacterial infection. In this research, a novel nano-functionalized polypyrrole (PPy) with high surface potential was electrochemically developed to improve the antibacterial properties of CPs. Sulfosalicylic acid (SSA)-a biomolecule with abundant negatively charged functional groups-was introduced to not only successfully assist in the electrical assembly of Py micelles into a bacterial extracellular matrix mimetic nanostructure, but also to modulate the surface electrical properties of PPy. Scanning Kelvin probe microscopy (SKPM) results confirm the increased positive charges on SSA-assisted PPy (PPy/SSA nanorod and irregular PPy/SSA film) relative to the irregular PPy/Cl film. Plate colony-counting experiments confirm that SSA-assisted PPy has excellent antibacterial properties relative to the irregular PPy/Cl film. The higher antibacterial rate for PPy/SSA nanorods suggests a synergistic effect via the nanorod structure and the high surface potential induced by dopants. Cytotoxicity testing demonstrates that PPy/SSA samples were nontoxic to the cells. Our work provides an effective way to develop antibacterial CPs.
Cancer residues around the surgical site remain a significant cause of treatment failure with cancer recurrence. To prevent cancer recurrence and simultaneously repair surgery-caused defects, it is urgent to develop implantable biomaterials with anticancer ability and good biological activity. In this work, a functionalized implant is successfully fabricated by doping the effective anticancer element selenium (Se) into the potassium–sodium niobate piezoceramic, which realizes the wireless combination of electrotherapy and chemotherapy. Herein, we demonstrate that the Se-doped piezoelectric implant can cause mitochondrial damage by increasing intracellular reactive oxygen species levels and then trigger the caspase-3 pathway to significantly promote apoptosis of osteosarcoma cells in vitro. Meanwhile, its good biocompatibility has been verified. These results are of great importance for future deployment of wireless electro- and chemostimulation to modulate biological process around the defective tissue.
Many recent studies have shown that joint-resident mesenchymal stem cells (MSCs) play a vital role in articular cartilage (AC) in situ regeneration. Specifically, synovium-derived MSCs (SMSCs), which have strong chondrogenic differentiation potential, may be the main driver of cartilage repair. However, both the insufficient number of MSCs and the lack of an ideal regenerative microenvironment in the defect area will seriously affect the regeneration of AC. Tetrahedral framework nucleic acids (tFNAs), notable novel nanomaterials, are considered prospective biological regulators in biomedical engineering. Here, we aimed to explore whether tFNAs have positive effects on AC in situ regeneration and to investigate the related mechanism. The results of in vitro experiments showed that the proliferation and migration of SMSCs were significantly enhanced by tFNAs. In addition, tFNAs, which were added to chondrogenic induction medium, were shown to promote the chondrogenic capacity of SMSCs by increasing the phosphorylation of Smad2/3. In animal models, the injection of tFNAs improved the therapeutic outcome of cartilage defects compared with that of the control treatments without tFNAs. In conclusion, this is the first report to demonstrate that tFNAs can promote the chondrogenic differentiation of SMSCs in vitro and enhance AC regeneration in vivo, indicating that tFNAs may become a promising therapeutic for AC regeneration.
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