Scale Up of PLA Nanoparticle Synthesis by Solvent Displacement and Functionalization of the Particles with a Silica Shell for Targeted Drug Delivery Applications

Biodegradable polymer nanoparticles have the properties necessary to address many of the issues associated with current drug delivery techniques including targeted and controlled delivery. A novel drug delivery vehicle is proposed consisting of a poly(lactic acid) nanoparticle core, with a functionalized, mesoporous silica shell. In this study, the production of PLA nanoparticles is investigated using solvent displacement in both a batch and continuous manner, and the effects of various system parameters are examined. Using Pluronic F-127 as the stabilization agent throughout the study, PLA nanoparticles are produced through solvent displacement with diameters ranging from 200 to 250 nm using two different methods: dropwise addition and in an impinging jet mixer. The impinging jet mixer allows for easy scale-up of particle production. The concentration of surfactant and volume of quench solution is found to have minimal impact on particle diameter; however, the concentration of PLA is found to significantly impact the diameter mean and polydispersity. In addition, the stability of the PLA nanoparticles is observed to increase as residual THF is evaporated. Lastly, the isolated PLA nanoparticles are coated with a silica shell using the Stöber Process. It is found that functionalizing the silica with a phosphonic silane in the presence of excess Pluronic F-127 decreases coalescence of the particles during the coating process. Future work should be conducted to fine-tune the PLA nanoparticle synthesis process by understanding the effect of other system parameters and in synthesizing mesoporous silica shells.

K(L) --> pi0 e+ e- and K(L) --> pi0 mu+ mu-: A binary star on the stage of flavor physics

A systematic analysis of New Physics impacts on the rare decays KL→π0ell+ell- is performed. Thanks to their different sensitivities to flavor-changing local effective interactions, these two modes could provide valuable information on the nature of the possible New Physics at play. In particular, a combined measurement of both modes could disentangle scalar/pseudoscalar from vector or axial-vector contributions. For the latter, model-independent bounds are derived. Finally, the KL→π0μ+μ- forward-backward CP-asymmetry is considered, and shown to give interesting complementary information.

Intracavity Generation of Radially Polarized CO2 Laser Beams Based on a Simple Binary Dielectric Diffraction Grating

We present experimental results on the intracavity generation of radially polarized light by incorporation of a polarization-selective mirror in a CO2 -laser resonator. The selectivity is achieved with a simple binary dielectric diffraction grating etched in the backsurface of the mirror substrate. Very high polarization selectivity was achieved, and good agreement of simulation and experimental results is shown. The overall radial polarization purity of the generated laser beam was found to be higher than 90% .

A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints

Publication bias and related bias in meta-analysis is often examined by visually checking for asymmetry in funnel plots of treatment effect against its standard error. Formal statistical tests of funnel plot asymmetry have been proposed, but when applied to binary outcome data these can give false-positive rates that are higher than the nominal level in some situations (large treatment effects, or few events per trial, or all trials of similar sizes). We develop a modified linear regression test for funnel plot asymmetry based on the efficient score and its variance, Fisher's information. The performance of this test is compared to the other proposed tests in simulation analyses based on the characteristics of published controlled trials. When there is little or no between-trial heterogeneity, this modified test has a false-positive rate close to the nominal level while maintaining similar power to the original linear regression test ('Egger' test). When the degree of between-trial heterogeneity is large, none of the tests that have been proposed has uniformly good properties.

A Faster Circular Binary Segmentation Algorithm for the Analysis of Array CGH Data

Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number (Olshen {\it et~al}, 2004). The algorithm tests for change-points using a maximal $t$-statistic with a permutation reference distribution to obtain the corresponding $p$-value. The number of computations required for the maximal test statistic is $O(N^2),$ where $N$ is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster. algorithm. Results: We present a hybrid approach to obtain the $p$-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analysis of array CGH data from a breast cancer cell line to show the impact of the new approaches on the analysis of real data. Availability: An R (R Development Core Team, 2006) version of the CBS algorithm has been implemented in the ``DNAcopy'' package of the Bioconductor project (Gentleman {\it et~al}, 2004). The proposed hybrid method for the $p$-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.

Quantifying and Comparing the Accuracy of Binary Biomarkers When Predicting a Failure Time Outcome

The positive and negative predictive value are standard measures used to quantify the predictive accuracy of binary biomarkers when the outcome being predicted is also binary. When the biomarkers are instead being used to predict a failure time outcome, there is no standard way of quantifying predictive accuracy. We propose a natural extension of the traditional predictive values to accommodate censored survival data. We discuss not only quantifying predictive accuracy using these extended predictive values, but also rigorously comparing the accuracy of two biomarkers in terms of their predictive values. Using a marginal regression framework, we describe how to estimate differences in predictive accuracy and how to test whether the observed difference is statistically significant.

A BAYESIAN SHRINKAGE MODEL FOR INCOMPLETE LONGITUDINAL BINARY DATA WITH APPLICATION TO THE BREAST CANCER PREVENTION TRIAL

We consider inference in randomized studies, in which repeatedly measured outcomes may be informatively missing due to drop out. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a non-future dependence model for the drop-out mechanism and posit an exponential tilt model that links non-identifiable and identifiable distributions. This model is indexed by non-identified parameters, which are assumed to have an informative prior distribution, elicited from subject-matter experts. Under this model, full data estimands are shown to be expressed as functionals of the distribution of the observed data. To avoid the curse of dimensionality, we model the distribution of the observed data using a Bayesian shrinkage model. In a simulation study, we compare our approach to a fully parametric and a fully saturated model for the distribution of the observed data. Our methodology is motivated and applied to data from the Breast Cancer Prevention Trial.

A unified approach to modeling multivariate binary data using copulas over partitions

Many seemingly disparate approaches for marginal modeling have been developed in recent years. We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the proposed copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts.

Boron nitride nanotubes : synthesis, characterization, functionalization, and potential applications

Boron nitride nanotubes (BNNTs) are structurally similar to carbon nanotubes (CNTs), but exhibit completely different physical and chemical properties. Thus, BNNTs with various interesting properties may be complementary to CNTs and provide an alternative perspective to be useful in different applications. However, synthesis of high quality of BNNTs is still challenging. Hence, the major goals of this research work focus on the fundamental study of synthesis, characterizations, functionalization, and explorations of potential applications. In this work, we have established a new growth vapor trapping (GVT) approach to produce high quality and quantity BNNTs on a Si substrate, by using a conventional tube furnace. This chemical vapor deposition (CVD) approach was conducted at a growth temperature of 1200 °C. As compared to other known approaches, our GVT technique is much simpler in experimental setup and requires relatively lower growth temperatures. The as-grown BNNTs are fully characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron energy loss spectroscopy (EELS), Energy Filtered Mapping, Raman spectroscopy, Fourier Transform Infra Red spectroscopy (FTIR), UV-Visible (UV-vis) absorption spectroscopy, etc. Following this success, the growth of BNNTs is now as convenient as growing CNTs and ZnO nanowires. Some important parameters have been identified to produce high-quality BNNTs on Si substrates. Furthermore, we have identified a series of effective catalysts for patterned growth of BNNTs at desirable or pre-defined locations. This catalytic CVD technique is achieved based on our finding that MgO, Ni or Fe are the good catalysts for the growth of BNNTs. The success of patterned growth not only explains the role of catalysts in the formation of BNNTs, this technique will also become technologically important for future device fabrication of BNNTs. Following our success in controlled growth of BNNTs on substrates, we have discovered the superhydrophobic behavior of these partially vertically aligned BNNTs. Since BNNTs are chemically inert, resistive to oxidation up to ~1000°C, and transparent to UV-visible light, our discovery suggests that BNNTs could be useful as self-cleaning, insulating and protective coatings under rigorous chemical and thermal conditions. We have also established various approaches to functionalize BNNTs with polymeric molecules and carbon coatings. First, we showed that BNNTs can be functionalized by mPEG-DSPE (Polyethylene glycol-1,2-distearoyl-sn-glycero-3-phosphoethanolamine), a bio-compatible polymer that helps disperse and dissolve BNNTs in water solution. Furthermore, well-dispersed BNNTs in water can be cut from its original length of >10µm to(>20hrs). This success is an essential step to implement BNNTs in biomedical applications. On the other hand, we have also succeeded to functionalize BNNTs with various conjugated polymers. This success enables the dispersion of BNNTs in organic solvents instead of water. Our approaches are useful for applications of BNNTs in high-strength composites. In addition, we have also functionalized BNNTs with carbon decoration. This was performed by introducing methane (CH4) gas into the growth process of BNNT. Graphitic carbon coatings can be deposited on the side wall of BNNTs with thicknesses ranging from 2 to 5 nm. This success can modulate the conductivity of pure BNNTs from insulating to weakly electrically conductive. Finally, efforts were devoted to explore the application of the wide bandgap BNNTs in solar-blind deep UV (DUV) photo-detectors. We found that photoelectric current generated by the DUV light was dominated in the microelectrodes of our devices. The contribution of photocurrent from BNNTs is not significant if there is any. Implication from these preliminary experiments and potential future work are discussed.

FUNCTIONALIZATION OF CARBON NANOTUBES FOR MESENCHYMAL STEM CELL-ASSISTED PHOTOTHERMAL THERAPY

Carbon nanotubes were first cut and functionalized with a newly developed reaction involving autoclaving and sonication in hydrogen peroxide. The functionalized nanotubes were characterized and evaluated for aqueous solubility. Studies which relate reaction conditions to final carbon nanotube length were conducted. Hydroxyl groups present on the carbon nanotubes served as a platform for a series of addition reactions, with the objective of conjugating streptavidin and fluorescent markers onto the carbon nanotubes. The modified nanotubes were attached onto the surface of biotinylated mesenchymal stem cells, creating a novel, tumor-homing delivery system for photothermal anticancer agents.

Biomimetic Coatings and Their Biological Functionalization

The biomimetic coating technique can be used to deposit layers of calcium phosphate (CaP) on medical devices to improve their osteoconductivity and osseointegration.The inorganic layer generated is akin to mineralized bone matrix and can be degraded as such. The biomimetic coating technique involves the nucleation and growth of bone-like crystals on a pretreated substrate by immersing it in a supersaturated solution of CaP under physiological conditions of temperature (37°C) and pH (7.4). The method, originally developed by Kokubo and his co-workers in 1990, has since undergone improvement and refinement by several groups of investigators. Biomimetic coatings are valuable in that they can serve as a vehicle for the slow, sustained release of osteogenic agents at the site of implantation. This attribute is rendered possible by the near-physiological conditions under which these coatings are prepared, which permits the incorporation of bioactive agents into the inorganic crystal latticework rather than their superficial adsorption on preformed layers. In addition, the biomimetic coating technique can be applied to implants of an organic as well as of a metallic nature and to those with irregular surface geometries, which is not possible using conventional methodologies.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. METHODS: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. RESULTS: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. CONCLUSION: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.