PCBs的超(亚)临界水催化氧化及还原裂解

简要分析了多氯联苯(PCBs)的来源及其对环境构成的危害,介绍了PCBs在超（亚）临界水中的反应及其处理效果。分别从超临界水氧化、超临界水裂解及亚临界水还原三个方面阐明了超临界反应过程中PCBs降解的反应路径和降解效率,解释了共溶剂（甲醇、苯）、碱催化剂（Na₂CO₃、NaOH）、氧化剂（NaNO₃、NaNO₂）等对PCBs脱氯和分解的增效作用机理。发现在超临界水氧化与超临界水裂解条件下CH3OH对PCBs降解反应的促进机制有所不同,碱催化剂通过中和反应过程中产生的HCl生成NaCl沉淀导致体系中Cl的含量降低,从而促进脱氯反应的进行。对反应器防腐、处理的经济性方面略作讨论,在总结上述研究工作的基础上提出了PCBs的超临界反应处理技术未来发展的若干研究方向。     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

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.     

晶格畸变检测仪研究碳化硅晶片中位错缺陷分布

利用晶格畸变检测仪研究了SiC晶片位错分布情况,通过对熔融KOH腐蚀后的SiC晶片进行全片或局部扫描,从而得到完整SiC晶片或局部区域的位错分布.与LEXT OLS40003D激光共聚焦显微镜扫描腐蚀图进行比较,晶格畸变检测仪扫描腐蚀图可以将晶片上位错腐蚀坑信息完全呈现出来,且根据腐蚀坑呈现的颜色及尺寸大小,可以分辨出...     

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…     

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.     

Packed-Fiber Solid Phase-Extraction Coupled with HPLC-MS/MS for Rapid Determination of Lipid Oxidative Damage Biomarker 8-Iso-Prostaglandin F2α in Urine

The 8-iso-prostaglandin F_2α (8-iso-PGF_2α) biomarker is used as the gold standard for tracing lipid oxidative stress in vivo. The analysis of urinary 8-iso-PGF_2α is challenging when dealing with trace amounts of 8-iso-PGF_2α and the complexity of urine matrixes. A packed-fiber solid-phase extraction (PFSPE)–coupled with HPLC-MS/MS–method, based on polystyrene (PS)-electrospun nanofibers, was developed for the specific determination of 8-iso-PGF_2α in urine and compared with other newly developed LC-MS/MS methods. The method, which simultaneously processed 12 samples within 5 min on a self-made semi-automatic array solid-phase extraction processor, was the first to introduce PS-electrospun nanofibers as an adsorbent for the extraction of 8-iso-PGF_2α and was successfully applied to real urine samples. After optimizing the PFSPE conditions, good linearity in the range of 0.05–5 ng/mL with R² > 0.9996 and a satisfactory limit of detection of 0.015 ng/mL were obtained, with good intraday and interday precision (RSD < 10%) and recoveries of 95.3–103.8%. This feasible method is expected to be used for the batch quantitative analysis of urinary 8-iso-PGF_2α.     

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.     

Precise electro-reduction of alkyl halides for radical defluorinative alkylation

Reported here is a precise electro-reduction strategy for radical defluorinative alkylation towards the synthesis of gem-difluoroalkenes from α-trifluoromethylstyrenes. According to the redox-potential difference of the radical precursors, direct or indirect electrolysis is respectively adopted to realize the precise reduction. An easy-to-handle, catalyst-and metal-free condition is developed for the reduction of alkyl radical precursors that are generally easier to be reduced than α-trifluorome...     

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.