Therapeutic Efficacy of an ω-3-Fatty Acid-Containing 17-β Estradiol Nano-Delivery System against Experimental Atherosclerosis

Atherosclerosis and its consequences remain prevalent clinical challenges throughout the world. Initiation and progression of atherosclerosis involves a complex, dynamic interplay among inflammation, hyperlipidemia, and endothelial dysfunction. A multicomponent treatment approach targeted for delivery within diseased vessels could prove beneficial in treating atherosclerosis. This study was undertaken to evaluate the multimodal effects of a novel ω-3-fatty acid-rich, 17-β-estradiol (17-βE)-loaded, CREKA-peptide-modified nanoemulsion system on experimental atherosclerosis. In vitro treatment of cultured human aortic endothelial cells (ECs) with the 17-βE-loaded, CREKA-peptide-modified nanoemulsion system increased cellular nitrate/nitrite, indicating improved nitric oxide formation. In vivo, systemic administration of this nanoemulsion system to apolipoprotein-E knock out (ApoE^-/-) mice fed a high-fat diet significantly improved multiple parameters related to the etiology and development of occlusive atherosclerotic vasculopathy: lesion area, circulating plasma lipid levels, and expression of aortic-wall inflammatory markers. These salutary effects were attributed selectively to the 17-βE and/or ω-3 polyunsaturated fatty acid components of the nano-delivery system. At therapeutic doses, the 17-βE-loaded, CREKA-peptide modified nanoemulsion system appeared to be biocompatible in that it elicited no apparent adverse/toxic effects, as indexed by body weight, plasma alanine aminotransferase/aspartate aminotransferase levels, and liver and kidney histopathology. The study demonstrates the therapeutic potential of a novel, 17-βE-loaded, CREKA-peptide-modified nanoemulsion system against atherosclerosis in a multimodal fashion by reducing lesion size, lowering the levels of circulating plasma lipids and decreasing the gene expression of inflammatory markers associated with the disease.     

Functional rarefaction: estimating functional diversity from field data

Studies in biodiversity-ecosystem function and conservation biology have led to the development of diversity indices that take species' functional differences into account. We identify two broad classes of indices: those that monotonically increase with species richness (MSR indices) and those that weight the contribution of each species by abundance or occurrence (weighted indices). We argue that weighted indices are easier to estimate without bias but tend to ignore information provided by rare species. Conversely, MSR indices fully incorporate information provided by rare species but are nearly always underestimated when communities are not exhaustively surveyed. This is because of the well-studied fact that additional sampling of a community may reveal previously undiscovered species. We use the rarefaction technique from species richness studies to address sample-size-induced bias when estimating functional diversity indices. Rarefaction transforms any given MSR index into a family of unbiased weighted indices, each with a different level of sensitivity to rare species. Thus rarefaction simultaneously solves the problem of bias and the problem of sensitivity to rare species. We present formulae and algorithms for conducting a functional rarefaction analysis of the two most widely cited MSR indices: functional attribute diversity (FAD) and Petchey and Gaston's functional diversity (FD). These formulae also demonstrate a relationship between three seemingly unrelated functional diversity indices: FAD, FD and Rao's quadratic entropy. Statistical theory is also provided in order to prove that all desirable statistical properties of species richness rarefaction are preserved for functional rarefaction.