Novel P(3HB) Composite Films Containing Bioactive Glass Nanoparticles for Wound Healing Applications

Bioactive glass (BG) is considered an ideal material for haemostasis as it releases Ca2+ ions upon hydration, which is required to support thrombosis. In this study the effect of the presence of the BG nanoparticles in P(3HB) microsphere films on the structural properties, thermal properties and biocompatibility of the films were studied. The nanoscaled bioactive glass with a high surface area was also tested for its in vitro haemostatic efficacy and was found to be able to successfully reduce the clot detection time. In an effort to study the effect of the roughness induced by the formation of HA on the cellular functions such as cell adhesion, cell mobility and cell differentiation, the composite films were immersed in SBF for a period of 1, 3 and 7 days. From the SEM images the surface of the P(3HB)/n-BG composite microsphere films appeared fairly uniform and smooth on day 1, however on day 3 and day 7 a rough and uneven surface was observed. The presence of HA on the composite microsphere films on day 3 and day 7 influenced the surface roughness of the films. However, when the P(3HB)/n-BG composite microspheres with enhanced surface roughness were tested for biocompatibility, reduced amount of protein adsorption and cell adhesion were observed. This study thus revealed that there is an optimal surface roughness for the P(3HB) microsphere films for increased cell adhesion, beyond which it could be deleterious for cell adhesion and differentiation.

Secure and Scalable Statistical Computation of Questionnaire Data in R

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.