Extraction of PLGA-microencapsulated proteins using a two-immiscible liquid phases system containing surfactants.

PURPOSE: The extraction of proteins from PLGA/PLA microspheres by a two-immiscible liquid phases system with the addition of surfactants was investigated. METHODS: First, the extraction without surfactants and the interaction between proteins (IFN-α2b and EGF) and empty microspheres (PLGA or PLA) was studied. Next, proteins stability in presence of different surfactants was evaluated by: (1) bicinchoninic acid protein assay, (2) reversed phase-high performance liquid chromatography, and (3) enzyme-linked immunosorbent assay. Then, proteins were extracted with PBS/dichloromethane including selected surfactants and characterized by the above mentioned techniques, biological activity tests, sodium dodecyl sulfate-polyacrylamide gel electrophoresis and electrospray ionization mass spectrometry. RESULTS: Without surfactants, protein recovery was only 27-43% for IFN-α2b and 58-73% for EGF. Protein content in solutions incubated with blank microspheres decreased to 66% for IFN-α2b and 86% for EGF. It was only possible to quantify the EGF and IFN-α2b in the same manner as in PBS alone when the surfactant added was Pluronic F-68 and SDS, respectively. Addition of these surfactants allowed the complete isolation of both biomolecules from the microspheres. The extraction procedure did not affect the encapsulated proteins. CONCLUSION: Proteins can be quantitatively extracted, without changes, from PLGA/PLA microspheres using PBS/dichloromethane system that include an appropriate surfactant.

On the Heterogeneity of Existing Repositories of Movements Intended for the Evaluation of Fall Detection Systems.

Due to the serious impact of falls on the autonomy and health of older people, the investigation of wearable alerting systems for the automatic detection of falls has gained considerable scientific interest in the field of body telemonitoring with wireless sensors. Because of the difficulties of systematically validating these systems in a real application scenario, Fall Detection Systems (FDSs) are typically evaluated by studying their response to datasets containing inertial sensor measurements captured during the execution of labelled nonfall and fall movements. In this context, during the last decade, numerous publicly accessible databases have been released aiming at offering a common benchmarking tool for the validation of the new proposals on FDSs. This work offers a comparative and updated analysis of these existing repositories. For this purpose, the samples contained in the datasets are characterized by different statistics that model diverse aspects of the mobility of the human body in the time interval where the greatest change in the acceleration module is identified. By using one-way analysis of variance (ANOVA) on the series of these features, the comparison shows the significant differences detected between the datasets, even when comparing activities that require a similar degree of physical effort. This heterogeneity, which may result from the great variability of the sensors, experimental users, and testbeds employed to generate the datasets, is relevant because it casts doubt on the validity of the conclusions of many studies on FDSs, since most of the proposals in the literature are only evaluated using a single database.