The antihyperglycemic effect of () extract was proven, but it still needs to be formulated into a suitable dosage form. We aimed at preparing an oral stabilized SLNs for with high payload, to be used as powder for reconstitution, filled into capsule or compressed into tablet. SLNs were prepared by emulsion solvent evaporation technique. Preliminary characterization was performed followed by full assessment of the optimized SLNs suspension and/or its lyophilized form: particle size, zeta potential, surface morphology, percentage entrapment efficiency (% EE), DSC, FTIR and in vitro release studies. The optimized SLNs lyophilized formula (F3) exhibited acceptable compressibility and flowability. The reconstituted F3L showed % sedimentation volume of 91.83 %, re-dispersibility of 95%, viscosity of 764.33 cp, uniform particle size of 30.28 nm as shown by TEM, polydispersity index (PDI) of 0.16, zeta potential of -36.4 mV, % EE of 89.64 % and drug content of 97.69 %. The physical mixture and F3 FTIR spectrum indicated compatibility of components. In vitro release study showed a burst release in lyophilized formulations followed by slow-release, calculated as total phenolic content. Our previously reported work revealed that the total extracts of and SLNs formulations with the greatest lipid content F3, demonstrated a considerable blood glucose-lowering effect in diabetic rats. The obtained lyophilized SLNs is promising for preparation of a suitable stable dosage form for extract to be used in treatment of diabetes.

The number of hypercholesterolemic people is increasing rapidly worldwide, with elevated lipid profiles representing a major risk factor of coronary heart diseases. Dietary intervention was shown to improve the lipid profile, thus enhancing the quality of life. Dietary fiber is a nondigestible form of carbohydrates, due to the lack of the digestive enzyme in humans required to digest fiber, and is classified according to its water solubility properties as either soluble (SDF) or insoluble dietary fiber (IDF). Consumption of SDF is associated with several health benefits such as reduced lipid levels, lower blood pressure, improved blood glucose control, improved immune function, and reduced inflammation. SDF has been shown to lower blood cholesterol by several action mechanisms including directly due to the gelling, mucilaginous, and viscous fiber nature, and indirectly due to its fermented products and modulation of the gut microbiome. This review aims to provide a holistic overview on how SDF impacts the lipid profile. We start by providing an overview of the chemical structure of the major SDFs including mucilage, gums (gum arabic and guar gum), pectin, and inulin.

In spatial transcriptomics (ST) data, biologically relevant features such as tissue compartments or cell-state transitions are reflected by gene expression gradients. Here, we present SpaceWalker, a visual analytics tool for exploring the local gradient structure of 2D and 3D ST data. The user can be guided by the local intrinsic dimensionality of the high-dimensional data to define seed locations, from which a flood-fill algorithm identifies transcriptomically similar cells on the fly, based on the high-dimensional data topology. In several use cases, we demonstrate that the spatial projection of these flooded cells highlights tissue architectural features and that interactive retrieval of gene expression gradients in the spatial and transcriptomic domains confirms known biology. We also show that SpaceWalker generalizes to several different ST protocols and scales well to large, multi-slice, 3D whole-brain ST data while maintaining real-time interaction performance.

Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state.