Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification.

Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines

The anti-photoaging effect of C-phycocyanin on ultraviolet B-irradiated BALB/c-nu mouse skin

Introduction: C-phycocyanin (C-PC), a photosynthetic protein obtained from Spirulina, is regarded a highly promising commercially available biochemical. Numerous in vitro and in vivo studies have provided evidence of C-PC’s ability to mitigate the inflammatory response, alleviate oxidative stress, and facilitate wound healing. However, despite the existing knowledge regarding C-PC’s protective mechanism against cellular apoptosis induced by ultraviolet B (UVB) radiation, further in vivo experiments are needed to explore its anti-photoaging mechanism.Methods: In this study, a UVB-induced skin photoaging model was established using BALB/c-nu mice, and the potential protective effects of topically administered c-PC were investigated by various molecular biology tools. In addition, a novel delivery system, C-PC nanodispersion, was developed to facilitate the transdermal delivery of C-PC.Results: C- PC demonstrated significant anti-photoaging activities in the UVB-induced skin. The application of C-PC to the dorsal skin of the mice resulted in improved macroscopic characteristics, such as reduced sagging and coarse wrinkling, under UVB irradiation Histological analyses showed that C-PC treatment significantly decreased the symptoms of epidermal thickening, prevented dermal collagen fiber loosening, increased the hydroxyproline (Hyp) content and activities of antioxidant enzymes (such as superoxide dismutase, catalase, and glutathione peroxidase) in mouse skin, decreased malondialdehyde levels and expressions of inflammatory factors (interleukin-1α [IL-1α], IL-1β, IL-6, and tumor necrosis factor-α), reduced matrix metalloproteinase [MMP-3 and MMP-9] expressions, and inhibited the phosphorylation of c-Jun N-terminal kinase, extracellular signal-regulated kinase, and p38 proteins in the mitogen-activated protein kinase family.Discussion: By analyzing the results of the study, a new drug delivery system, C-PC nano-dispersion, was proposed, and the anti-photoaging effect of C-PC and its mechanism were investigated

Design of Passive UHF RFID Tag Antennas and Industry Application

Nowadays, there is a growing demand for reliable assets security and management in various industries. The company SolarWave is eager to implement a comprehensive security system to produce active protection for their expensive product: solar panels. This security system is not only including assets tracking, monitoring but also combined with a control system, which is used to binary control a switch of solar panel to be on in presence of the correct ID and off in absence of the correct ID. One of the technologies that made this concept viable is known as Radio Frequency Identification (RFID). The thesis project is a sub-project in the development project whose content is mentioned as above. It contains two main parts. One is the system solution for the company. The other is RFID tag design which is in parallel with the company solution in order to reach a scientific level of a master thesis. In this thesis, I systematically analyze the operating mechanism and characteristics of RFID, and propose both active and passive RFID solutions for the company. And I also suggest an alternative radio technology ZigBee which can be used instead or as a complement to RFID. Meanwhile, I propose two designs of RFID tag according to the specification of the solar panel. One is modified meandering antenna. This kind of antenna is very effective and popular in RFID tag design in order to minimize the size of antenna. The other is inductively coupled loop antenna. It is a very useful method for conjugate matching in RFID tag antenna. The required input resistance and reactance can be achieved separately by choosing appropriate geometry parameters. It makes the antenna easier to match to the tag chips. Both the RFID antenna designs are simulated on Ansoft HFSS 12

Target Characterization of Kaempferol against Myocardial Infarction Using Novel In Silico Docking and DARTS Prediction Strategy

Target identification is a crucial process for advancing natural products and drug leads development, which is often the most challenging and time-consuming step. However, the putative biological targets of natural products obtained from traditional prediction studies are also informatively redundant. Thus, how to precisely identify the target of natural products is still one of the major challenges. Given the shortcomings of current target identification methodologies, herein, a novel in silico docking and DARTS prediction strategy was proposed. Concretely, the possible molecular weight was detected by DARTS method through examining the protected band in SDS-PAGE. Then, the potential targets were obtained from screening and identification through the PharmMapper Server and TargetHunter method. In addition, the candidate target Src was further validated by surface plasmon resonance assay, and the anti-apoptosis effects of kaempferol against myocardial infarction were further confirmed by in vitro and in vivo assays. Collectively, these results demonstrated that the integrated strategy could efficiently characterize the targets, which may shed a new light on target identification of natural products

Hybrid-order Stochastic Block Model

Community detection is a research hotspot in machine learning and data mining. However, most of the existing community detection methods only rely on the lower-order connectivity patterns, while ignoring the higher-order connectivity patterns, and unable to capture the building blocks of the complex network. In recent years, some community detection methods based on higher-order structures have been developed, but they mainly focus on the motif network composed of higher-order structures, which violate the original lower-order topological structure and are affected by the fragmentation issue, resulting in the deviation of community detection results. Therefore, there is still a lack of community detection methods that can effectively utilize higher-order connectivity patterns and lower-order connectivity patterns. To overcome the above limitations, this paper proposes the Hybrid-order Stochastic Block Model (HSBM) from the perspective of the generative model. Based on the classical stochastic block model, the generation of lower-order structure and higher-order structure of the network is modeled uniformly, and the original topological properties of the network are maintained while using higher-order connectivity patterns. At the same time, a heuristic algorithm for community detection is proposed to optimize the objective function. Extensive experiments on six real-world datasets show that the proposed method outperforms the existing approaches

Target Characterization of Kaempferol against Myocardial Infarction Using Novel In Silico Docking and DARTS Prediction Strategy

Target identification is a crucial process for advancing natural products and drug leads development, which is often the most challenging and time-consuming step. However, the putative biological targets of natural products obtained from traditional prediction studies are also informatively redundant. Thus, how to precisely identify the target of natural products is still one of the major challenges. Given the shortcomings of current target identification methodologies, herein, a novel in silico docking and DARTS prediction strategy was proposed. Concretely, the possible molecular weight was detected by DARTS method through examining the protected band in SDS-PAGE. Then, the potential targets were obtained from screening and identification through the PharmMapper Server and TargetHunter method. In addition, the candidate target Src was further validated by surface plasmon resonance assay, and the anti-apoptosis effects of kaempferol against myocardial infarction were further confirmed by in vitro and in vivo assays. Collectively, these results demonstrated that the integrated strategy could efficiently characterize the targets, which may shed a new light on target identification of natural products

Synthesis of the Most Potent Isomer of μ-Conotoxin KIIIA Using Different Strategies

In the chemical synthesis of conotoxins with multiple disulfide bonds, the oxidative folding process can result in diverse disulfide bond connectivities, which presents a challenge for determining the natural disulfide bond connectivities and leads to significant structural differences in the synthesized toxins. Here, we focus on KIIIA, a μ-conotoxin that has high potency in inhibiting Nav1.2 and Nav1.4. The non-natural connectivity pattern (C1—C9, C2—C15, C4—C16) of KIIIA exhibits the highest activity. In this study, we report an optimized Fmoc solid-phase synthesis of KIIIA using various strategies. Our results indicate that free random oxidation is the simplest method for peptides containing triple disulfide bonds, resulting in high yields and a simplified process. Alternatively, the semi-selective strategy utilizing Trt/Acm groups can also produce the ideal isomer, albeit with a lower yield. Furthermore, we performed distributed oxidation using three different protecting groups, optimizing their positions and cleavage order. Our results showed that prioritizing the cleavage of the Mob group over Acm may result in disulfide bond scrambling and the formation of new isomers. We also tested the activity of synthesized isomers on Nav1.4. These findings provide valuable guidance for the synthesis of multi-disulfide-bonded peptides in future studies

Rapid Identification of Wild <i>Gentiana Genus</i> in Different Geographical Locations Based on FT-IR and an Improved Neural Network Structure Double-Net

Gentiana Genus, a herb mainly distributed in Asia and Europe, has been used to treat the damp heat disease of the liver for over 2000 years in China. Previous studies have shown significant differences in the compositional contents of wild Gentiana Genus samples from different geographical origins. Therefore, the traceable geographic locations of the wild Gentiana Genus samples are essential to ensure practical medicinal value. Over the last few years, the developments in chemometrics have facilitated the analysis of the composition of medicinal herbs via spectroscopy. Notably, FT-IR spectroscopy is widely used because of its benefit of allowing rapid, nondestructive measurements. In this paper, we collected wild Gentiana Genus samples from seven different provinces (222 samples in total). Twenty-one different FT-IR spectral pre-processing methods that were used in our experiments. Meanwhile, we also designed a neural network, Double-Net, to predict the geographical locations of wild Gentiana Genus plants via FT-IR spectroscopy. The experiments showed that the accuracy of the neural network structure Double-Net we designed can reach 100%, and the F1_score can reach 1.0