Hypotonic, gel-forming delivery system for vaginal drug administration.

Vaginal drug delivery is often preferred over systemic delivery to reduce side effects and increase efficacy in treating diseases and conditions of the female reproductive tract (FRT). Current vaginal products have drawbacks, including spontaneous ejection of drug-eluting rings and unpleasant discharge from vaginal creams. Here, we describe the development and characterization of a hypotonic, gel-forming, Pluronic-based delivery system for vaginal drug administration. The rheological properties were characterized with and without common hydrogel polymers to demonstrate the versatility. Both qualitative and quantitative approaches were used to determine the Pluronic F127 concentration below the critical gel concentration (CGC) that was sufficient to achieve gelation when formulated to be hypotonic to the mouse vagina. The hypotonic, gel-forming formulation was found to form a thin, uniform gel layer along the vaginal epithelium in mice, in contrast to the rapidly forming conventional gelling formulation containing polymer above the CGC. When the hypotonic, gel-forming vehicle was formulated in combination with a progesterone nanosuspension (ProGel), equivalent efficacy was observed in the prevention of chemically-induced preterm birth (PTB) compared to commercial Crinone® vaginal cream. Further, ProGel showed marked benefits in reducing unpleasant discharge, reducing product-related toxicity, and improving compatibility with vaginal bacteria in vitro. A hypotonic, gel-forming delivery system may be a viable option for therapeutic delivery to the FRT.

miRNA-211 maintains metabolic homeostasis in medulloblastoma through its target gene long-chain acyl-CoA synthetase 4.

The prognosis of childhood medulloblastoma (MB) is often poor, and it usually requires aggressive therapy that adversely affects quality of life. microRNA-211 (miR-211) was previously identified as an important regulator of cells that descend from neural cells. Since medulloblastomas primarily affect cells with similar ontogeny, we investigated the role and mechanism of miR-211 in MB. Here we showed that miR-211 expression was highly downregulated in cell lines, PDXs, and clinical samples of different MB subgroups (SHH, Group 3, and Group 4) compared to normal cerebellum. miR-211 gene was ectopically expressed in transgenic cells from MB subgroups, and they were subjected to molecular and phenotypic investigations. Monoclonal cells stably expressing miR-211 were injected into the mouse cerebellum. miR-211 forced expression acts as a tumor suppressor in MB both in vitro and in vivo, attenuating growth, promoting apoptosis, and inhibiting invasion. In support of emerging regulatory roles of metabolism in various forms of cancer, we identified the acyl-CoA synthetase long-chain family member (ACSL4) as a direct miR-211 target. Furthermore, lipid nanoparticle-coated, dendrimer-coated, and cerium oxide-coated miR-211 nanoparticles were applied to deliver synthetic miR-211 into MB cell lines and cellular responses were assayed. Synthesizing nanoparticle-miR-211 conjugates can suppress MB cell viability and invasion in vitro. Our findings reveal miR-211 as a tumor suppressor and a potential therapeutic agent in MB. This proof-of-concept paves the way for further pre-clinical and clinical development.

On the effect of neuronal spatial subsampling in small-world networks.

The analysis of real-world networks of neurons is biased by the current ability to measure just a subsample of the entire network. It is thus relevant to understand if the information gained in the subsamples can be extended to the global network to improve functional interpretations. Here we showed how average clustering coefficient (CC), average path length (PL), and small-world propensity (SWP) scale when spatial sampling is applied to small-world networks. This extraction mimics the measurement of physical neighbors by means of electrical and optical techniques, both used to study neuronal networks. We applied this method to in silico and in vivo data and we found that the analyzed properties scale with the size of the sampled network and the global network topology. By means of mathematical manipulations, the topology dependence was reduced during scaling. We highlighted the behaviors of the descriptors that, qualitatively, are shared by all the analyzed networks and that allowed an approximated prediction of those descriptors in the global graph using the subgraph information. In contrast, below a spatial threshold, any extrapolation failed; the subgraphs no longer contain enough information to make predictions. In conclusion, the size of the chosen subgraphs is critical to extend the findings to the global network.