Stereocomplexed poly(lactic acid)-poly(ethylene glycol) nanoparticles with dual-emissive boron dyes for tumor accumulation.

Responsive biomaterials play important roles in imaging, diagnostics, and therapeutics. Polymeric nanoparticles (NPs) containing hydrophobic and hydrophilic segments are one class of biomaterial utilized for these purposes. The incorporation of luminescent molecules into NPs adds optical imaging and sensing capability to these vectors. Here we report on the synthesis of dual-emissive, pegylated NPs with "stealth"-like properties, delivered intravenously (IV), for the study of tumor accumulation. The NPs were created by means of stereocomplexation using a methoxy-terminated polyethylene glycol and poly(D-lactide) (mPEG-PDLA) block copolymer combined with iodide-substituted difluoroboron dibenzoylmethane-poly(L-lactide) (BF2dbm(I)PLLA). Boron nanoparticles (BNPs) were fabricated in two different solvent compositions to study the effects on BNP size distribution. The physical and photoluminescent properties of the BNPs were studied in vitro over time to determine stability. Finally, preliminary in vivo results show that stereocomplexed BNPs injected IV are taken up by tumors, an important prerequisite to their use as hypoxia imaging agents in preclinical studies.

Lognormal and gamma mixed negative binomial regression

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior information, such as sparsity in the regression coefficients. By placing a gamma distribution prior on the NB dispersion parameter r, and connecting a log-normal distribution prior with the logit of the NB probability parameter p, efficient Gibbs sampling and variational Bayes inference are both developed. The closed-form updates are obtained by exploiting conditional conjugacy via both a compound Poisson representation and a Polya-Gamma distribution based data augmentation approach. The proposed Bayesian inference can be implemented routinely, while being easily generalizable to more complex settings involving multivariate dependence structures. The algorithms are illustrated using real examples. Copyright 2012 by the author(s)/owner(s).