Auto-adhesive transdermal drug delivery patches using beetle inspired micropillar structures

The patch described in this paper combines the principles of wet adhesion, which is a widely adopted biological adhesion system in nature, with transdermal drug delivery. A biologically inspired micropillar patch was fabricated that is self-adhesive, reusable, and can sustain a controlled drug release. We successfully preloaded the commercial non-steroidal anti-inflammatory generic drug unguents indomethacin, ketoprofen, diclofenac sodium and etofenamate into a polydimethylsiloxane elastomeric matrix and fabricated drug-containing micropillar patches. When examining the drug release kinetics and friction of the patches, we observed that these drug unguents can be released calculably and regularly for several days. Additionally, the drug unguents released from the patch to its attached surface are critical to increase the strength of the patch's adhesion, which is based on capillary attractive forces and is inspired by beetle feet. Here, we create a novel system combining biomimetics and drug delivery that can be modified for use across the biomedical and engineering spectra. Motivation: the objective of the present study was to characterize a micropillar PDMS patch that was inspired by a beetle's wet adhesion as a platform for conducting in vitro release studies. Commercially available non-steroid anti-inflammatory drugs (NSAIDs) were used as the model drugs for our delivery systems. An emphasis was put on quantitatively evaluating the drug release and friction manifestation of these patches

Dynamic association rules for gene expression data analysis

BACKGROUND: The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. RESULTS: We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. CONCLUSIONS: In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance

Body Mass Index–Mortality Relationship in Severe Hypoglycemic Patients With Type 2 Diabetes

AbstractBackgroundHypoglycemia is associated with a higher risk of death. This study analyzed various body mass index (BMI) categories and mortalities of severe hypoglycemic patients with type 2 diabetes mellitus (DM) in a hospital emergency department.MethodsThe study included 566 adults with type 2 diabetes who were admitted to 1 medical center in Taiwan between 2008 and 2009 with a diagnosis of severe hypoglycemia. Mortality data, demographics, clinical characteristics and the Charlson’s Comorbidity Index were obtained from the electronic medical records. Patients were stratified into 4 study groups as determined by the National institute of Health (NiH) and World Health organization classification for BMi, and the demographics were compared using the analysis of variance and χ2 test. Kaplan-Meier’s analysis and the Cox proportional-hazards regression model were used for mortality, and adjusted hazard ratios were adjusted for each BMi category among participants.ResultsAfter controlling for other possible confounding variables, BMI <18.5 kg/m2 was independently associated with low survival rates in the Cox regression analysis of the entire cohort of type 2 DM patients who encountered a hypoglycemic event. Compared to patients with normal BMI, the mortality risk was higher (adjusted hazard ratios = 4.9; 95% confidence interval [CI] = 2.4-9.9) in underweight patients. Infection-related causes of death were observed in 101 cases (69.2%) and were the leading cause of death.ConclusionsAn independent association was observed between BMI less than 18.5 kg/m2 and mortality among type 2 DM patient with severe hypoglycemic episode. Deaths were predominantly infection related

Differentiation of Foot-and-Mouth Disease-Infected pigs from Vaccinated Pigs Using Antibody-Detecting Sandwich ELISA

The presence of serum antibodies for nonstructural proteins of the foot-and-mouth disease virus (FMDV) can differentiate FMDV-infected animals from vaccinated animals. In this study, a sandwich ELISA was developed for rapid detection of the foot-and-mouth disease (FMD) antibodies; it was based on an Escherichia coli-expressed, highly conserved region of the 3ABC nonstructural protein of the FMDV O/TW/99 strain and a monoclonal antibody derived from the expressed protein. The diagnostic sensitivity of the assay was 98.4%, and the diagnostic specificity was 100% for naïve and vaccinated pigs; the detection ability of the assay was comparable those of the PrioCHECK and UBI kits. There was 97.5, 93.4 and 66.6% agreement between the results obtained from our ELISA and those obtained from the PrioCHECK, UBI and CHEKIT kits, respectively. The kappa statistics were 0.95, 0.87 and 0.37, respectively. Moreover, antibodies for nonstructural proteins of the serotypes A, C, Asia 1, SAT 1, SAT 2 and SAT 3 were also detected in bovine sera. Furthermore, the absence of cross-reactions generated by different antibody titers against the swine vesicular disease virus and vesicular stomatitis virus (VSV) was also highlighted in this assay's specificit

Influence of Y-doped induced defects on the optical and magnetic properties of ZnO nanorod arrays prepared by low-temperature hydrothermal process

One-dimensional pure zinc oxide (ZnO) and Y-doped ZnO nanorod arrays have been successfully fabricated on the silicon substrate for comparison by a simple hydrothermal process at the low temperature of 90°C. The Y-doped nanorods exhibit the same c-axis-oriented wurtzite hexagonal structure as pure ZnO nanorods. Based on the results of photoluminescence, an enhancement of defect-induced green-yellow visible emission is observed for the Y-doped ZnO nanorods. The decrease of E(2)(H) mode intensity and increase of E(1)(LO) mode intensity examined by the Raman spectrum also indicate the increase of defects for the Y-doped ZnO nanorods. As compared to pure ZnO nanorods, Y-doped ZnO nanorods show a remarked increase of saturation magnetization. The combination of visible photoluminescence and ferromagnetism measurement results indicates the increase of oxygen defects due to the Y doping which plays a crucial role in the optical and magnetic performances of the ZnO nanorods

Geographical heterogeneity and influenza infection within households

Although it has been suggested that schoolchildren vaccination reduces influenza morbidity and mortality in the community, it is unknown whether geographical heterogeneity would affect vaccine effectiveness