Novel approaches circumventing blood-ocular barriers in systemic drug delivery are lacking. We hypothesize receptor-mediated delivery of curcumin (CUR) across intestinal and ocular barriers leads to decreased inflammation in a model of lens-induced uveitis. CUR was encapsulated in double-headed polyester nanoparticles using gambogic acid (GA)–coupled polylactide-co-glycolide (PLGA). Orally administered PLGA-GA₂-CUR led to notable aqueous humor CUR levels and was dosed (10 mg/kg twice daily) to adult male beagles (n = 8 eyes) with induced ocular inflammation. Eyes were evaluated using a semiquantitative preclinical ocular toxicology scoring (SPOTS) and compared to commercial anti-inflammatory treatment (oral carprofen 2.2 mg/kg twice daily) (n = 8) and untreated controls (n = 8). PLGA-GA₂-CUR offered improved protection compared with untreated controls and similar protection compared with carprofen, with reduced aqueous flare, miosis, and chemosis in the acute phase (<4 hours). This study highlights the potential of PLGA-GA₂ nanoparticles for systemic drug delivery across ocular barriers.

In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there is an abundance of auxiliary information, which often gets under-utilized in determining the joint dependency structure. In this article, we consider a Bayesian approach to model undirected graphs underlying heterogeneous multivariate observations with additional assistance from covariates. Building on product partition models, we propose a novel covariate-dependent Gaussian graphical model that allows graphs to vary with covariates so that observations whose covariates are similar share a similar undirected graph. To efficiently embed Gaussian graphical models into our proposed framework, we explore both Gaussian likelihood and pseudo-likelihood functions. For Gaussian likelihood, a G-Wishart distribution is used as a natural conjugate prior, and for the pseudo-likelihood, a product of Gaussianconditionals is used. Moreover, the proposed model has large prior support and is flexible to approximate any v-Hölder conditional variance-covariance matrices with v ∈ ( 0,1 ] . We further show that based on the theory of fractional likelihood, the rate of posterior contraction is minimax optimal assuming the true density to be a Gaussian mixture with a known number of components. The efficacy of the approach is demonstrated via simulation studies and an analysis of a protein network for a breast cancer dataset assisted by mRNA gene expression as covariates.

Gaussian graphical models are a popular tool to learn the dependence structure in the form of a graph among variables of interest. Bayesian methods have gained in popularity in the last two decades due to their ability to simultaneously learn the covariance and the graph. There is a wide variety of model-based methods to learn the underlying graph assuming various forms of the graphical structure. Although for scalability of the Markov chain Monte Carlo algorithms, decomposability is commonly imposed on the graph space, its possible implication on the posterior distribution of the graph is not clear. An open problem in Bayesian decomposable structure learning is whether the posterior distribution is able to select a meaningful decomposable graph that is “close” to the true non-decomposable graph, when the dimension of the variables increases with the sample size. In this article, we explore specific conditions on the true precision matrix and the graph, which results in an affirmative answer to this question with a commonly used hyper-inverse Wishart prior on the covariance matrix and a suitable complexity prior on the graph space. In absence of structural sparsity assumptions, our strong selection consistency holds in a high-dimensional setting where $p=O(n^{\alpha })$ for $\alpha <1/3$. We show when the true graph is non-decomposable, the posterior distribution concentrates on a set of graphs that are minimal triangulations of the true graph.

Astrocytes regulate synaptic activity across large brain territories via their complex, interconnected morphology. Emerging evidence supports the involvement of astrocytes in shaping relapse to opioid use through morphological rearrangements in the nucleus accumbens (NAc). However, a comprehensive assessment of astrocyte structural diversity within and between NAc subdivisions is lacking because of limitations in existing methodologies to quantify meaningful alterations in astrocyte structure. We developed a methodological pipeline that integrates supervised and unsupervised learning techniques to rigorously quantify astrocyte morphological features and spatial organization across the brain, leveraging expression of cytoskeletal markers. Application of this pipeline reveals that morphological characteristics of individual astrocytes predict their location within the NAc. Our analysis also indicates that after heroin use, astrocyte structural plasticity is impaired in portions of the NAc associated with the extinction of conditioned responses and is uniquely engaged in the dorsomedial portion of the NAc shell, an undercharacterized subdivision of the structure.

Long non-coding RNAs (lncRNAs) are a large and diverse class of transcribed RNAs, which have been shown to play a significant role in developing cancer. In this study, we apply integrative modeling framework to integrate the DNA copy number variation (CNV), lncRNA expression, and downstream target protein expression to predict patient survival in breast cancer. We develop a 3-stage model combining a mechanical model (lncRNA regressed on CNV and target proteins regressed on lncRNA) and a clinical model (survival regressed on estimated effects from the mechanical models). Using lncRNAs (such as HOTAIR and MALAT1) along with their CNV, target protein expressions, and survival outcomes from The Cancer Genome Atlas (TCGA) database, we show that predicted mean square error and integrated Brier score (IBS) are both lower for the proposed 3-step integrated model than that of 2-step model. Therefore, the integrative model has better predictive ability than the 2-step model not considering target protein information.