Electrospinning of Hyperbranched Poly-L-Lysine/Polyaniline Nanofibers for Application in Cardiac Tissue Engineering

Electrospun polyaniline nanofibers are one of the most promising materials for cardiac tissue engineering due to their tunable electroactive properties. Moreover, the biocompatibility of polyaniline nanofibes can be improved by grafting of adhesive peptides during the synthesis. In this paper, we describe the biocompatible properties and cardiomyocytes proliferation on polyaniline electrospun nanofibers modified by hyperbranched poly-L-lysine dendrimers (HPLys). The microstructure characterization of the HPLys/polyaniline nanofibers was carried out by scanning electron microscopy (SEM). It was observed that the application of electrical current stimulates the differentiation of cardiac cells cultured on the nanofiber scaffolds. Both electroactivity and biocompatibility of the HPLys based nanofibers suggest the use this material for culture of cardiac cells and opens the possibility of using this material as a biocompatible electroactive 3-D matrix in cardiac tissue engineering.

Tetrachlorocarbonyliridates: Water-Soluble Carbon Monoxide Releasing Molecules Rate-Modulated by the Sixth Ligand

A new family of compounds is presented as potential carbon monoxide releasing molecules (CORMs). These compounds, based on tetrachlorocarbonyliridate(III) derivatives, were synthesized and fully characterized by X-ray diffraction, electrospray mass spectrometry, IR. NMR, and density functional theory calculations. The rate of CO release was studied via the myoglobin assay. The results showed that the rate depends on the nature of the sixth ligand, trans to CO, and that a significant modulation on the release rate can be produced by changing the ligand. The reported compounds are soluble in aqueous media, and the rates of CO release are comparable with those for known CORMs, releasing CO at a rate of 0.03-0.58 mu M min(-1) in a 10 mu M solution of myoglobin and 10 mu M of the complexes.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.