Multiple Water-in-Oil-in-Water Emulsion Gels Based on Self-Assembled Saponin Fibrillar Network for Photosensitive Cargo Protection

A gelled multiple water-in-oil-in-water (W₁/O/W₂) emulsion was successfully developed by the unique combination of emulsifying and gelation properties of natural glycyrrhizic acid (GA) nanofibrils, assembling into a fibrillar hydrogel network in the continuous phase. The multiple emulsion gels had relatively homogeneous size distribution, high yield (85.6–92.5%), and superior storage stability. The multilayer interfacial fibril shell and the GA fibrillar hydrogel in bulk can effectively protect the double emulsion droplets against flocculation, creaming, and coalescence, thus contributing to the multiple emulsion stability. Particularly, the highly viscoelastic bulk hydrogel had a high storage modulus, which was found to be able to strongly prevent the osmotic-driven water diffusion from the internal water droplets to the external water phase. We show that these multicompartmentalized emulsion gels can be used to encapsulate and protect photosensitive water-soluble cargos by loading them into the internal water droplets. These stable multiple emulsion gels based on natural, sustainable saponin nanofibrils have potential applications in the food, pharmaceutical, and personal care industries

Flow chart of the image segmentation scheme.

<p>The proposed segmentation algorithm includes five consecutive steps: DCE micro-CT images acquisition, data dimension reduction, supervoxel generation, supervoxel classification, and target organs’ extraction.</p

Visual comparison of the segmentation results with the reference datasets, shown in 2-D images.

<p>The organ boundaries of manual segmentation (M1) and automatic segmentation based on the SVM and the RF are superimposed on two coronal images (A, B) and two sagittal images (C, D).</p

Visual comparison of the segmentation results with the reference datasets, shown in 3-D isosurface rendering.

<p>Left column: left lateral view. Right column: posterior view. Top row: manual segmentation (M1). Middle row: segmentation obtained by the SVM. Bottom row: segmentation obtained by the RF.</p

Quantitative evaluation of the proposed methods by comparison to manual segmentation.

<p>‘SM1’ and ‘RM1’ represents the comparison of the automatic segmentation by SVM and RF with the manual segmentation (M1), respectively. ‘M1M3’ compares the manual segmentations of two independent experts. ‘M1M2’ compares two manual segmentation repetitions of one expert. (A) Dice similarity coefficient. (B) False positive ratio. (C) False negative ratio. (* Indicates <i>p</i> < 0.05.)</p

Influence of the number of training samples on segmentation accuracy.

<p>10%, 30%, 50%, 70%, and 90% of the total samples of each category were selected randomly from the total data set and consisted of training sets for classification. Each case was repeated five times. The means and standard deviations of Dice similarity coefficients for the heart, liver, spleen, lung, and kidney were calculated (compared with M1). (A) The DSC of the organs classified by SVM. (B) The DSC of the organs classified by RF. For the lung, one value at 10% and two values at 30% were excluded from statistics because it failed to extract the lung by post-processing.</p

Dynamic contrast enhancement procedure after contrast agent administration.

<p>(A) Representative coronal micro-CT images before contrast agent injection and at 0 s, 50 s, 100 s, 150 s, and 200 s post-contrast injection. The image at 0 s was acquired during the inflow of contrast agent. All of the images are displayed with the same gray scale window. (B) The relative signal enhancement versus time curves of regions depicted by the arrows in (A) with the same colors.</p

Number of supervoxels for each category chosen to constitute the total data set.

<p>Number of supervoxels for each category chosen to constitute the total data set.</p

Controlled Hydrophobic Biosurface of Bacterial Cellulose Nanofibers through Self-Assembly of Natural Zein Protein

A novel, highly biocompatible bacterial cellulose (BC)-zein composite nanofiber with a controlled hydrophobic biosurface was successfully developed through a simple and green solution impregnation method, followed by evaporation-induced self-assembly (EISA) of adsorbed zein protein. The surface hydrophobicity of the zein-modified BC nanofibers could be controlled by simply changing the zein concentration, which is able to tune the morphology of self-assembled zein structures after EISA, thus affecting the surface roughness of composite membranes. Zein self-assembly at low concentrations (5 mg/mL) resulted in the formation of hierarchical zein structures (spheres and bicontinuous sponges) on the BC surface, thus increasing the surface roughness and leading to high hydrophobicity (the water contact angle reached 110.5°). However, at high zein concentrations, these large zein spheres assembled into a flat zein film, which decreased the surface roughness and hydrophobicity of membranes. The homogeneous incorporation of zein structures on the BC surface by hydrogen bonding did not significantly change the internal structure and mechanical performance of BC nanofibers. In comparison with pure BC, the BC-zein nanofibers had a better biocompatibility, showing a significantly increased adhesion and proliferation of fibroblast cells. This is probably due to the rough surface structure of BC-zein nanofibers as well as the high biocompatibility of natural zein protein. The novel BC-zein composite nanofibers with controlled surface roughness and hydrophobicity could be of particular interest for the design of BC-based biomaterials and biodevices that require specific surface properties and adhesion

Structure–Function Relationship of a Novel PR‑5 Protein with Antimicrobial Activity from Soy Hulls

An alkaline isoform of the PR-5 protein (designated GmOLPc) has been purified from soybean hulls and identified by MALDI-TOF/TOF-MS. GmOLPc effectively inhibited in vitro the growth of <i>Phytophthora soja</i> spore and <i>Pseudomonas syringae pv glycinea</i>. The antimicrobial activity of GmOLPc should be mainly ascribed to its high binding affinity with vesicles composed of DPPG, (1,3)-β-d-glucans, and weak endo-(1,3)-β-d-glucanase activity. From the 3D models, predicted by the homology modeling, GmOLPc contains an extended negatively charged cleft. The cleft was proved to be a prerequisite for endo-(1,3)-β-d-glucanase activity. Molecular docking revealed that the positioning of linear (1,3)-β-d-glucans in the cleft of GmOLPc allowed an interaction with Glu83 and Asp101 that were responsible for the hydrolytic cleavage of glucans. Interactions of GmOLPc with model membranes indicated that GmOLPc possesses good surface activity which could contribute to its antimicrobial activity, as proved by the behavior of perturbing the integrity of membranes through surface hydrophobic amino acid residues (Phe89 and Phe94)