A dual-application poly (DL-lactic-co-glycolic) acid (PLGA)-chitosan composite scaffold for potential use in bone tissue engineering

The development of patient-friendly alternatives to bone-graft procedures is the driving force for new frontiers in bone tissue engineering. Poly (DL-lactic-co-glycolic acid), (PLGA) and chitosan are well-studied and easy-to-process polymers from which scaffolds can be fabricated. In this study, a novel dual-application scaffold system was formulated from porous PLGA and protein-loaded PLGA/chitosan microspheres. Physicochemical and in vitro protein release attributes were established. The therapeutic relevance, cytocompatibility with primary human mesenchymal stem cells (hMSCs) and osteogenic properties were tested. There was a significant reduction in burst release from the composite PLGA/chitosan microspheres compared with PLGA alone. Scaffolds sintered from porous microspheres at 37°C were significantly stronger than the PLGA control, with compressive strengths of 0.846 ± 0.272 MPa and 0.406 ± 0.265 MPa, respectively (p < 0.05). The formulation also sintered at 37°C following injection through a needle, demonstrating its injectable potential. The scaffolds demonstrated cytocompatibility, with increased cell numbers observed over an 8-day study period. Von Kossa and immunostaining of the hMSC-scaffolds confirmed their osteogenic potential with the ability to sinter at 37°C in situ

Power Comparisons.

<p>Power to detect association in a region is shown for the Generalised C-alpha test, SKAT-O and the GRANVIL test applied directly to the quantitative trait and for the Generalised C-alpha and the Binomial C-alpha tests applied to the dichotomised quantitative trait. (A) Power is shown as a function of the percentage of causal variants in a region of size 100 kb that are risk as opposed to protective when the minimum MAF of variants considered is fixed at 0.5% for a sample size of 10,000. Results show that as the proportion of risk causal variants approaches 50%, the C-alpha and SKAT-O tests maintain power and that the Generalised C-alpha applied directly to the quantitative trait has optimal power. (B) Power is also shown as a function of the minimum MAF of variants considered when the percentage of risk causal variants in a region of size 100 kb is fixed at 50% for a sample 10,000 individuals. Results show that the power of the Generalised C-alpha test is optimal for variants with MAF>∼0.3% but SKAT-O is optimal for lower MAF. For quantitative traits, the power of the Generalised C-alpha test remains better than the Binomial C-alpha applied to a dichotomized version of the trait as long as variants have MAF>∼0.1%. For binary traits, the Binomial C-alpha test has greater or equivalent power than the Generalised C-alpha test.</p

Null simulations.

<p>Observed type I errors at selected significance levels for the Generalised C-alpha test for association with a quantitative trait and a dichotomised version of a quantitative trait in a 50 kb region where the rare variants tested do not account for any of the trait variance. Tests only consider variants in the region with a maximum MAF of 1% and a minimum MAF as indicated in the table. Type I error is estimated over 100,000 replicates of data for a sample of size 2,000. Significance in each replicate of data is assessed empirically by random permutation of the quantitative trait value and recalculation of the test statistic 1,000 times as described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003694#pgen.1003694.s003" target="_blank">Text S1</a>.</p

Genes demonstrating genome-wide significant evidence of rare variant association with type-1 diabetes on chromosome 6.

a<p>Genes with a permuted p-value less than 1.7×10⁻⁶ (indicating genome wide significance assuming a significance level of 5% and that there are 30,000 genes in the human genome <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003694#pgen.1003694-3" target="_blank">[25]</a>) in a Generalised C-alpha test.</p>b<p>For these genes, results are also shown when effects are adjusted for the lead common MHC SNP (rs9268645). Both analyses are adjusted for 3 principal components to account for population structure. For the unconditional analysis results are based on 600,000 permutations; for the conditional analysis results are based on 575,000 permutations. MAF, minor allele frequency; BP, base pair; MAF: Minor Allele Frequency; MHC, Major histocompatibility complex; NCBI, National Center for Biotechnology Information.</p

Quantifying fine-mapping success.

<p>Fraction of the simulations where the fine-mapped set is reduced to fewer than 10 variants for the comparison with <i>r2</i>-derived sets (a) and multi-ethnic study designs (b). Colors denote whether the set contains the causal variants and only one variant (dark purple), causal variant and fewer than 10 variants (medium purple), fewer than 10 variants but not the causal variant (light purple), or more than 10 variants (green). In both panels, the data is split by risk allele frequency (RAF) on the horizontal plane. Panel (a) is grouped by odds ratio (OR) on the x-axis, whereas the OR was set to 1.2 in all the multi-ethnic simulations.</p

Fine-mapping the ankylosing spondylitis (AS) regions.

<p>a) Correlation between predicted power to detect genome-wide association signals and size of the 95% credible sets. Boxplots represent the distribution of the simulations at the respective power of each RAF/OR setting. The labelled dots show the distribution of the empirical AS data. Regression lines in the range of predicted power of the AS loci (15–99.9%) are derived from the simulations (dashed) and AS loci (dotted with confidence interval of regression line). b) Example <i>FCGR2A</i> region where the 99% credible set (purple dots) could be fine-mapped to a small region (pink) containing few variant.</p

Refinement of the credible sets.

<p>The y-axis shows the number of variants in the 95% (a) and 99% (b) likely credible sets. The boxplots show the median and interquartile range of the simulations, while each point denotes a single “replicate”. The color of the boxplots/points denotes the RAF of the simulated causal variant, while each panel is split by the effect size along the horizontal plane.</p

Ranking of the causal variant across simulated loci.

<p>Cumulative percentage of simulations (y-axis) with decreasing ranking of the causal variant amongst all variants in the regions (x-axis). Panels are split by risk allele frequency of the causal variant along the vertical axis.</p

Regional plot showing the results of a meta-analysis of GWA studies in three Hispanic samples (Mexico City 1, Mexico City 2 and Starr County) for TG cholesterol.

<p>The region covers approximately 200 kb on chromosome 11, and includes the genes <i>BUD13/ZNF259/APOA5</i>.</p

Descriptive information of the two Mexico City samples used for admixture mapping analysis.

<p>Values presented are averages with the standard deviation included in brackets. Average lipid concentrations do not reflect correction for lipid-lowering treatment.</p