Oxidation behavior of Co-25Cr and Co-35Cr alloys Topical report, 1 Nov. 1967 - 31 Mar. 1968

Oxidation characteristics of cobalt-chromium base alloy

Nanoparticles for Local Drug Delivery to the Oral Mucosa: Proof of Principle Studies

Purpose To determine if solid lipid nanoparticles represent a viable strategy for local delivery of poorly water soluble and unstable chemopreventive compounds to human oral tissues. Methods Nanoparticle uptake and compound retention evaluations employed monolayer-cultured human oral squamous cell carcinoma (OSCC) cell lines and normal human oral mucosal explants. Feasibility of nanoparticle delivery was also evaluated with respect to the presence of phase-III efflux transporters in normal oral mucosal tissue and OSCC tissues. Results Functional uptake assays confirmed significantly greater internalization of nanoparticle-delivered fluorescent probe relative to free-fluorescent probe delivery, while concurrently demonstrating nanoparticle uptake rate differences among the OSCC cell lines and the phagocytic control human monocyte cell line. Mucosal explants exhibited nanoparticle penetration and internalization in the spinous and basal epithelial layer

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%