Solid dispersion-based pellet for colon delivery of tacrolimus through time- and pH-dependent layer coating: preparation, in vitro and in vivo studies

The intent of the present investigation is to develop and evaluate colon-specific coated tacrolimus solid dispersion pellet (SDP) that retards drug release in the stomach and small intestine but progressively releases in the colon. Tacrolimus-SDP was prepared by extrusion-spheronization technology and optimized by the micromeritic properties including flowability, friability, yields and dissolution rate. Subsequently, the pH-dependent layer (Eudragit L30D55) and time-dependent layer (Eudragit NE30D and L30D55) were coated on the SDP to form tacrolimus colon-specific pellets (CSP) using a fluidized bed coater. Under in vitro gradient pH environment, tacrolimus only released from CSP after changing pH to 6.8 and then quickly released in the phosphate buffer solution of pH 7.2. The Cmax of CSP was 195.68 ± 3.14 ng/mL at Tmax 4.5 ± 0.24 h where as in case of SDP, the Cmax was 646.16 ± 8.15 ng/mL at Tmax 0.5 ± 0.03 h, indicating the ability of CSP targeted to colon. The highest area under the curve was achieved 2479.58 ± 183.33 ng·h/mL for SDP, which was 2.27-fold higher than tacrolimus suspension. However, the best biodistribution performance was achieved from CSP. In conclusion, SDP combining of pH- and time-dependent approaches was suitable for targeted delivery of tacrolimus to colon

Optimal extraction parameters of Theabrownin from Sichuan Dark Tea

Background: Sichuan Dark Tea is a popular beverage with hypolipidemic and lifting greasy properties in the minority neighborhoods of Sichuan and Tibet regions. The theabrownin, an important pigment of dark tea, has been proven for the role of the hypolipidemic property in Sichuan Dark Tea. The objective of the study investigated the extraction process of theabrownin.Materials and Methods: Theabrownin was extracted from Sichuan Dark Tea with water and organic solvents. The quadratic regression orthogonally rotational combinational design experiment was performed to obtain the optimal extraction parameters.Results: The extraction yield of theabrownin was significantly influenced by both water extraction temperature and solid-liquid ratio, and the contribution of these factors on theabrownin yield was as follows: water temperature﹥solid-liquid ratio﹥organic solvent temperature. Moreover, the polynomial regression model established could predict the experimental value accurately.Conclusion: The optimum extraction process of theabrownin from Sichuan Dark Tea was established, which water temperature at 65.69-77.88℃, organic solvent temperature at 13.65-17.48℃ and a solid liquid ratio of 1:43.58-1:50.75(g/mL).Keywords: theabrownin; extraction; optimization; Sichuan Dark TeaAbbreviations list: SDT: Sichuan Dark Tea; TB: Theabrowni

Enhancement of polar phases in PVDF by forming PVDF/SiC nanowire composite

Different contents of silicon carbide (SiC) nanowires were mixed with Poly(vinylidene fluoride) (PVDF) to facilitate the polar phase crystallization. It was shown that the annealing temperature and SiC content affected on the phase and crystalline structures of PVDF/SiC samples. Furthermore, the addition of SiC nanowire enhanced the transformation of non-polar α phase to polar phases and increased the relative fraction of β phase in PVDF. Due to the nucleating agent mechanism of SiC nanowires, the ion-dipole interaction between the negatively charged surface of SiC nanowires and the positive CH2 groups in PVDF facilitated the formation of polar phases in PVDF

Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning

Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations of one image based on contrastive learning. By utilizing the consistency of two augmentations, the burden of manual annotations can be freed. Contrastive learning exploits instance-level information to learn robust features. However, the learned information is probably confined to different views of the same instance. In this paper, we attempt to leverage the similarity between two distinct images to boost representation in self-supervised learning. In contrast to instance-level information, the similarity between two distinct images may provide more useful information. Besides, we analyze the relation between similarity loss and feature-level cross-entropy loss. These two losses are essential for most deep learning methods. However, the relation between these two losses is not clear. Similarity loss helps obtain instance-level representation, while feature-level cross-entropy loss helps mine the similarity between two distinct images. We provide theoretical analyses and experiments to show that a suitable combination of these two losses can get state-of-the-art results. Code is available at https://github.com/guijiejie/ICCL.Comment: This paper is accepted by IEEE Transactions on Image Processin

Effects of Particle Size and Gluten Content on Starch Digestibility of Noodles in Vitro

In order to study the effects of particle size and gluten content on the starch digestibility of noodles in vitro， 0%， 5% and 10% of gluten were added into wheat flour with different particle size to make Chinese noodles， and noodles with large and small particles were separated after drying and grinding. The morphology， structural thermal properties and in vitro starch digestion properties of noodle samples were investigated. The results showed that with the increase of gluten content in noodle samples， the surface smoothness and gelatinization temperatures were increased， while the enthalpy values were decreased， and the area of gluten network formed in the noodles after cooking were increased. The in vitro digestion kinetics results showed that the starch digestion extent was decreased with the decreasing of flour size or the increasing of gluten content