Feasibility of binary composition in development of nanoethosomal glycolic vesicles of triamcinolone acetonide using Box-behnken design: <i>in vitro</i> and <i>ex vivo</i> characterization

<p>Triamcinolone acetonide (TA) employed for the treatment of atopic dermatitis exhibits limited penetration into the epidermis. This investigation <b>aimed</b> to explore the role of binary solvents in topical drug delivery of TA by developing nanoethosomal glycolic lipid vesicles by infusion method. Screening of vesicles (TA1-TA17) formulated by Box Behnken design identified the optimized formulation (TA10) that was developed as carbomer gels. The gels were then evaluated for pharmaceutical properties and compared with control and reference ethosomal gel (RG). Higher <i>in vitro</i> permeation was found in gels containing TA10, prepared with or without using penetration enhancer (EGP 83.76 ± 0.72% and EG 82.42 ± 0.89%, respectively). CLSM studies depicted deeper uniform penetration of fluorescent tracer into the epidermis via EG as compared with RG and control gel. Enhanced penetration was due to combinational solvent effect exerted by ethanol and propylene glycol. Histological analysis confirmed the non-irritant potential of the gel. Thus, it can be concluded that nanoethosomal glycolic vesicles proved to be an effective non irritant carrier for improvised penetration of triamcinolone acetonide for potential topical therapeutics.</p

Additional file 2: of A simulation study investigating power estimates in phenome-wide association studies

Summary Results: The summary results used to generate the figures. (XLSX 56 kb

Additional file 1: of A simulation study investigating power estimates in phenome-wide association studies

Figure S1. Binary Trait Type I Errors. The plot shows the Type I errors for different parameter settings. Each panel represents the different case number on the top and case-control ratio on the right which was used for the simulation dataset. The Type I error on the y-axis is calculated based on the number of false positive association below significance level of α = 0.00025. The disease penetrance is represented on the x-axis and each colored point represent different MAF used in the simulations. (PNG 379 kb

Property Prediction of Diesel Fuel Based on the Composition Analysis Data by two-Dimensional Gas Chromatography

The objective of the present study is to develop robust statistical models for the prediction of critical diesel properties such as cloud point, pour point, and cetane index with composition inputs such as <i>n</i>-Paraffins, Iso-paraffins, Naphthenes, and Aromatics (PINA) obtained by flow modulated two-dimensional gas chromatography with flame ionization detection (GC×GC-FID). A single gas chromatographic measurement coupled with models to predict the key physical properties is attractive for refiners to make quick decisions in optimizing diesel blending. We present a partial least-squares (PLS) linear regression statistical model that has been developed using 41 data sets of diesel samples with different compositions, out of which 33 samples were used for the calibration and eight samples for validation of the model. The <i>R</i>² values obtained for cloud point, pour point, and cetane index were 0.92, 0.93, and 0.92 with standard deviations of 1.20, 1.50, and 0.40, respectively. The average relative errors for predicted values of cloud point, pour point, and cetane index are found to be 0.86, 1.02, and 0.25, respectively. The PINA analyses of diesel and kerosene samples were carried out using flow modulated GC×GC with flame ionization detection (FID). The technique adapts reverse phase gas chromatography with two capillary chromatographic columns; the columns differ in length, diameter, stationary phase, and film thickness to get maximum peak resolution. The gravimetric blends of high purity reference standards of paraffins, naphthenes, and aromatic compounds (PINA) with variable carbon numbers were used for identification and to draw the boundaries for group types. Monoaromatic and polyaromatic content obtained for diesel and kerosene samples by the flow modulated GC×GC method were comparable to the results obtained by the High Performance Liquid Chromatographic (HPLC) method as per IP 391 or ASTM D 6591. Repeatability and reproducibility of the GC×GC analysis were performed for several samples to validate the method. It has been found that the HPLC method for the determination of aromatics content using a single calibration standard for each type, such as mono-, di-, and polyaromatics, causes a small error in the quantification in some of the samples as the refractive indices of all the aromatic species present in the diesel and kerosene samples vary depending on the addition of alkyl side chains; the presence of heteroatoms such as sulfur, nitrogen, and oxygen; etc

Overview of PheWAS with Immune Variants.

<p>This flow chart provides an overview of the steps taken to perform PheWAS between immune variants and ICD-9 diagnosis codes. The final testing dataset (purple) was formed by selecting SNPs from our array data that also exist on Immunochip and/or are within immune-related genes (yellow) and removing samples with missing genotypic or phenotypic data (green). Comprehensive associations were calculated between all final dataset SNPs and ICD-9 code based case/control status using logistic regression, with all models adjusted for age, sex and first five principal components. Replication was sought following both an exact ICD-9 code and a category ICD-9 code approach following the specified criteria. Pooled analysis was performed for both approaches using METAL. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160573#pone.0160573.s001" target="_blank">S1 Fig</a> for the full workflow from imputation through quality control, association testing, and replication for this study.</p

Cytoscape Network Showing the Connections between Phenotypes, the Genes with SNPs, and Pathways.

<p>In this network, green squares represent phenotype; red triangles represent genes; and blue circles are KEGG pathways. The colored lines highlight the link between phenotype and pathway. For the gene <i>HLA-DRA</i> with SNPs associated with “<i>714</i>: <i>rheumatoid arthritis</i>” and “<i>250</i>: <i>type 1 diabetes</i>” is present in the KEGG pathway of “<i>rheumatoid arthritis</i>” (red line) and “<i>type 1 diabetes”</i> (green line) respectively. Also, the blue edge shows the connection between <i>“714</i>: <i>rheumatoid arthritis”</i>, <i>“716</i>: <i>other specified arthropathies”</i> and the KEGG “<i>JAK-STAT signaling pathway</i>” through two interleukin genes, <i>IL23R</i> and <i>IL6</i>.</p

Summary of Data Sets Used for the Study.

<p>Summary of Data Sets Used for the Study.</p

Pleiotropy: SNPs Associated with more than One Phenotype and Replicating across more than One Study for the Same ICD-9 Category.

<p>This chromosomal ideogram has lines indicating the location of the SNP, with filled colored circles indicating different ICD-9 code diagnoses associated with that particular SNP. When there are multiple pairs of the same phenotypes in the same region, this indicates regions where several SNPs in close proximity were associated with the same pairs of phenotypes.</p

Immune-Related ICD-9 categories selected for further analysis.

<p>Immune-Related ICD-9 categories selected for further analysis.</p

Synthesis view plot showing PheWAS results replicating across MyCode^® and BioVU that have previously reported associations.

<p>The first track is the chromosomal location for each SNP. The next column lists the SNP identifier, the phenotype associated in our study, and the reported GWAS trait (p<10⁻⁵). Results representing exact matches with the NHGRI-EBI GWAS catalog and GRASP are annotated with a single asterisk and the closely related traits are represented with a double asterisk. Blue symbols represent results from MyCode^®, red symbols represent results from BioVU and green symbols are the pooled analysis results obtained using the program METAL.</p