Metabonomics Study of the Hematopoietic Effect of Medicinal Wine Maoji Jiu on a Blood Deficiency Rat Model by Ultra-High-Performance Liquid Chromatography Coupled to Quadrupole Time-of-Flight Mass Spectrometry and a Pattern Recognition Approach

Maoji Jiu (MJ) is a kind of medicinal wine that has been widely used by Chinese people for many years to nourish and promote blood circulation. The purpose of this study was to investigate the hematopoietic effect of MJ on the metabolism of blood deficient rats and to explore the underlying hematopoietic regulation mechanisms. Blood deficiency model rats were induced by subcutaneous injection of N-acetylphenylhydrazine (APH) and intraperitoneal injection of cyclophosphamide (CTX). The plasma metabolic fingerprints of blood deficiency model rats with and without MJ treatment were obtained by using metabonomics based on ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC–QTOF/MS). Orthogonal partial least squares-discriminant analysis (OPLS–DA) was used to evaluate the hematopoietic effect of MJ and identify potential biomarkers in the plasma of blood deficiency model rats. The levels of white blood cells (WBC), red blood cells (RBC) and hemoglobin (HGB) and the activity of antioxidant capacity showed a recovery trend to the control group after MJ treatment, while the dose of 10 mL/kg showed the best effect. In this study, thirteen potential biomarkers were identified, which were mainly related to seven metabolic pathways, including linoleic acid metabolism, d-glutamine and d-glutamate metabolism, alanine, aspartate and glutamate metabolism, tryptophan metabolism, pyrimidine metabolism, porphyrin and chlorophyll metabolism and arginine biosynthesis. Metabolomics was applied frequently to reflect the physiological and metabolic state of organisms comprehensively, indicating that the rapid plasma metabonomics may be a potentially powerful tool to reveal the efficacy and enriching blood mechanism of MJ.     

The Extracts of Polygonum cuspidatum Root and Rhizome Block the Entry of SARS-CoV-2 Wild-Type and Omicron Pseudotyped Viruses via Inhibition of the S-Protein and 3CL Protease

COVID-19, resulting from infection by the SARS-CoV-2 virus, caused a contagious pandemic. Even with the current vaccines, there is still an urgent need to develop effective pharmacological treatments against this deadly disease. Here, we show that the water and ethanol extracts of the root and rhizome of Polygonum cuspidatum (Polygoni Cuspidati Rhizoma et Radix), a common Chinese herbal medicine, blocked the entry of wild-type and the omicron variant of the SARS-CoV-2 pseudotyped virus into fibroblasts or zebrafish larvae, with IC₅₀ values ranging from 0.015 to 0.04 mg/mL. The extracts were shown to inhibit various aspects of the pseudovirus entry, including the interaction between the spike protein (S-protein) and the angiotensin-converting enzyme II (ACE2) receptor, and the 3CL protease activity. Out of the chemical compounds tested in this report, gallic acid, a phytochemical in P. cuspidatum, was shown to have a significant anti-viral effect. Therefore, this might be responsible, at least in part, for the anti-viral efficacy of the herbal extract. Together, our data suggest that the extracts of P. cuspidatum inhibit the entry of wild-type and the omicron variant of SARS-CoV-2, and so they could be considered as potent treatments against COVID-19.     

一类具有Neumann边界条件的曲率方程解的估计

研究了一类具有Neumann边界条件的抛物方程解的性质,通过微分方法得到了一类边值问题解的估计,并得到了拟线性方程解的存在性.     

A MXene-derived redox homeostasis regulator perturbs the Nrf2 antioxidant program for reinforced sonodynamic therapy

Ultrasound (US)-mediated sonodynamic therapy (SDT) has emerged as a spatiotemporally controllable therapeutic modality in combating cancer because of its high tissue-penetration depth and minimal invasiveness. However, the elevated nuclear factor erythroid 2-related factor 2 (Nrf2) antioxidant program in cancer cells can serve as a chief reactive oxygen species (ROS) detoxification system to alleviate oxidative injury and promote tumorigenesis, and thus greatly antagonize the therapeutic efficacy of ROS-mediated anticancer therapies. Herein, we report that vanadium carbide MXene-derived carbon dots (PMQDs) can act as high-efficacy sonosensitizers to efficiently generate ROS upon US irradiation and simultaneously hinder the Nrf2 antioxidant program for enhanced sonodynamic therapy of cancer. These PMQDs show superior US-triggered ROS generating ability because of their efficient migration/separation of electron–hole pairs and narrow bandgap. Importantly, these PMQDs can serve as efficient redox homeostasis regulators to perturb the Nrf2 antioxidant mechanism and thus reduce its effects on ROS neutralization for enhanced SDT efficacy. Overall, the present study will not only provide a new paradigm to augment SDT by perturbing the Nrf2 antioxidant program, but also give valuable insights into developing high-efficacy MXene-derived nanoagents for cancer therapy.

A redox homeostasis regulator has been developed as a high-efficacy sonosensitizer to efficiently generate reactive oxygen species upon ultrasound irradiation and simultaneously hinder the Nrf2 antioxidant program for enhanced sonodynamic therapy.     

Dynamic Variation of Amino Acid Contents and Identification of Sterols in Xinyang Mao Jian Green Tea

As important biomolecules in Camellia sinensis L., amino acids (AAs) are considered to contribute to the overall green tea sensory quality and undergo dynamic changes during growth. However, limited by analytical capacity, detailed AAs composition in different growth stages remains unclear. To address this question, we analyzed the dynamic changes of 23 AAs during leaf growth in Xinyang Mao Jian (XYMJ) green tea. Using amino acid analyzer, we demonstrated that most AAs are abundant on Pure Brightness Day and Grain Rain Day. After Grain Rain, 23 AAs decreased significantly. Further analysis shows that theanine has a high level on the day before Spring Equinox and Grain Rain, accounting for 44–61% of the total free AAs content in tea leaves. Glu, Pro, and Asp are the second most abundant AAs. Additionally, spinasterol and 22,23-dihydrospinasterol are first purified and identified in ethanol extract of XYMJ by silica gel column chromatography method. This study reveals the relationship between plucking days and the dynamic changes of AAs during the growth stage and proves the rationality of the traditional plucking days of XYMJ green tea.     

3英寸6H-SiC单晶的生长

SiC是宽带隙半导体材料的典型代表,具有优良的热学、力学、化学和电学性质,不但可以用作基于GaN的蓝色发光二极管的衬底材料,同时又是制作高温、高频、大功率电子器件的最佳材料之一,因此高质量、大直径SiC单晶的生长一直是材料研究领域的热点课题。目前美国的Cree公司在SiC单晶生长领域研发方面起步早、投入大,SiC单晶的直径达到4英寸,处于领先地位。我国在“十五”期间投入了一定的人力、物力进行了SiC单晶生长的研究,在生长2英寸SiC单晶的工作中取得了一定的成绩[1],但更大直径的SiC单晶生长技术进展缓慢,至今未见国内报道。而对…     

Phenothiazine-based covalent organic frameworks with low exciton binding energies for photocatalysis

Designing delocalized excitons with low binding energy (E_b) in organic semiconductors is urgently required for efficient photochemistry because the excitons in most organic materials are localized with a high E_b of >300 meV. In this work, we report the achievement of a low E_b of ∼50 meV by constructing phenothiazine-based covalent organic frameworks (COFs) with inherent crystallinity, porosity, chemical robustness, and feasibility of bandgap engineering. The low E_b facilitates effective exciton dissociation and thus promotes photocatalysis by using these COFs. As a demonstration, we subject these COFs to photocatalytic polymerization to synthesize polymers with remarkably high molecular weight without any requirement of the metal catalyst. Our results can facilitate the rational design of porous materials with low E_b for efficient photocatalysis.

We report the construction of phenothiazine-based covalent organic frameworks, which exhibited diverse structures, the feasibility of bandgap engineering, and unprecedented ultralow exciton binding energy of ∼50 meV for photocatalytic polymerization.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

Na2S-Mediated One-Pot Selective Deoxygenation of α-Hydroxyl Carbonyl Compounds including Natural Products

A practical method for the deoxygenation of α-hydroxyl carbonyl compounds under mild reaction conditions is reported here. The use of cheap and easy-to-handle Na₂S·9H₂O as the reductant in the presence of PPh₃ and N-chlorosuccinimide (NCS) enables the selective dehydroxylation of α-hydroxyl carbonyl compounds, including ketones, esters, amides, imides and nitrile groups. The synthetic utility is demonstrated by the late-stage deoxygenation of bioactive molecule and complex natural products.     

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.