Poly(vinylidene fluoride-trifluoroethylene)/NAY zeolite hybrid membranes as a drug release platform applied to ibuprofen release

Poly(vinylidene fluoride-trifluoroethylene)/NaY zeolite composite membranes were prepared by solvent casting and evaluated as a suitable drug release platform through the evaluation of loading and release of ibuprofen. The membranes were characterized at the morphological, structural and mechanical levels. The 1H-NMR spectra indicate that only the membranes with 16 and 32 % of NaY were useful for IBU encapsulation and the drug release was followed by UV-Vis spectroscopy. The release profile is independent of the zeolite content and can be described by the Korsmeyer-Peppas model. The membrane with 32 % zeolite content releases more than double IBU amount when compared with the membrane with 16 % showing that zeolite content allows tailoring membrane drug release content for specific applications. The drug release platform developed in this work is suitable for other drugs and applications.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

From perfect strangers to research partners: a study of relationships, resource sharing and perceived benefits within R&D cooperation networks

Doctoral thesis in Marketing and Strategy.