The effect of phenyl substituents on the release rates of esterase-sensitive coumarin-based prodrugs

A coumarin-based prodrug system has been recently developed in our laboratory for the preparation of esterase-sensitive prodrugs of amines, peptides, and peptidomimetics. The drug release rates from this prodrug system were found to be dependent on the structural features of the drug moiety. In certain cases, the release can be undesirably slow for drugs that are secondary amines with relatively high pKa's. Aimed at finding ways to manipulate the release rates to suit the need of different drugs, we have examined the effect of the phenyl ring substitutions on the release kinetics of such prodrugs and found that appropriately positioned alkyl substituents on the phenyl ring could help to facilitate the release by as much as 16-fold. Therefore, introduction of alkyl substituents on the phenyl ring should allow us to manipulate the release rates and, therefore, time profiles for different drugs.     

2-Amino-4-bromobutanoic Acid

(2S)- and (2R)-2-Amino-4-bromobutanoic acid were prepared starting from N-Boc-glutamic acid α tert-butyl ester. The double tert-butyl protection was necessary to prevent a partial racemisation during Barton’s radical decarboxylation used to transform the γ-carboxylic group into a bromide. This bromide reacted with different nitrogen, oxygen and sulphur nucleophiles to give nonnatural amino acids characterised by basic or heterocyclic side chains. The title compound was also used to prepare a conformationally constrained peptidomimetic.     

Synthesis of PNIPAAm-g-P4VP Microgel as Draw Agent in Forward Osmosis by RAFT Polymerization and Reverse Suspension Polymerization to Improve Water Flux

Microgels have unique and versatile properties allowing their use in forward osmosis areas as a draw agent. In this contribution, poly(4-vinylpyridine) (P4VP) was synthesized via RAFT polymerization and then grafted to a poly(N-Isopropylacrylamide) (PNIPAAm) crosslinking network by reverse suspension polymerization. P4VP was successfully obtained by the quasiliving polymerization with the result of nuclear magnetic resonance and gel permeation chromatography characterization. The particle size and particle size distribution of the PNIPAAm-g-P4VP microgels containing 0, 5, 10, 15 and 20 wt% P4VP were measured by means of a laser particle size analyzer. It was found that all the microgels were of micrometer scale and the particle size was increased with the P4VP load. Inter/intra-molecular-specific interactions, i.e., hydrogen bond interactions were then investigated by Fourier infrared spectroscopy. In addition, the water flux measurements showed that all the PNIPAAm-g-P4VP microgels can draw water more effectively than a blank PNIPAAm microgel. For the copolymer microgel incorporating 20 wt% P4VP, the water flux was measured to be 7.48 L∙m⁻²∙h⁻¹.     

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 molecular mechanics approach for analyzing tensile nonlinear deformation behavior of single-walled carbon nanotubes

In this paper, by capturing the atomic information and reflecting the behaviour governed by the nonlinear potential function, an analytical molecular mechanics approach is proposed. A constitutive relation for single-walled carbon nanotubes (SWCNT’s) is established to describe the nonlinear stress-strain curve of SWCNT’s and to predict both the elastic properties and breaking strain of SWCNT’s during tensile deformation. An analysis based on the virtual internal bond (VIB) model proposed by P. Zhang et al. is also presented for comparison. The results indicate that the proposed molecular mechanics approach is indeed an acceptable analytical method for analyzing the mechanical behavior of SWCNT’s. The project supported by the National Natural Science Foundation of China (10121202, 90305015 and 10328203), the Key Grant Project of Chinese Ministry of Education (0306) and the Research Grants Council of the Hong Kong Special Administrative Region, China (HKU 7195/04E).     

KEADA: Identifying Key Classes in Software Systems Using Dynamic Analysis and Entropy-Based Metrics

Software maintenance is indispensable in the software development process. Developers need to spend a lot of time and energy to understand the software when maintaining the software, which increases the difficulty of software maintenance. It is a feasible method to understand the software through the key classes of the software. Identifying the key classes of the software can help developers understand the software more quickly. Existing techniques on key class identification mainly use static analysis techniques to extract software structure information. Such structure information may contain redundant relationships that may not exist when the software runs and ignores the actual interaction times between classes. In this paper, we propose an approach based on dynamic analysis and entropy-based metrics to identify key classes in the Java GUI software system, called KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics). First, KEADA extracts software structure information by recording the calling relationship between classes during the software running process; such structure information takes into account the actual interaction of classes. Second, KEADA represents the structure information as a weighted directed network and further calculates the importance of each node using an entropy-based metric OSE (One-order Structural Entropy). Third, KEADA ranks classes in descending order according to their OSE values and selects a small number of classes as the key class candidates. In order to verify the effectiveness of our approach, we conducted experiments on three Java GUI software systems and compared them with seven state-of-the-art approaches. We used the Friedman test to evaluate all approaches, and the results demonstrate that our approach performs best in all software systems.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

埋地热油管道正常运行的数值模拟研究

对高凝原油管道输送的水力热力问题进行分析研究,掌握管线运行规律,保证管线安全经济运行有着重要意义.本文建立了埋地热油管道正常运行的数学模型,采用非结构化网格和有限容积法对该问题进行了研究,计算结果与实验测量值吻合良好.     

Flavonoids from Lycium barbarum Leaves Exhibit Anti-Aging Effects through the Redox-Modulation

Lycium barbarum leaves are a kind of vegetable, and modern nutrition studies have found that they have an anti-aging function. Our study aims to investigate the anti-aging effects of Lycium barbarum leaf flavonoid (LBLF) extracts and its underlying molecular mechanism. LBLFs were purified using D101 and polyamide resin, characterized by ultraperformance liquid chromatography coupled with mass spectrometry, and administered to hydrogen peroxide (H₂O₂)-treated human umbilical vein endothelial cells (HUVECs) and Caenorhabditis elegans. Appropriate enrichment conditions were optimized through dynamic adsorption and desorption experiments, the content of flavonoids reached 909.84 mg/g, rutin and kaempferol being the main ones. LBLFs attenuated H₂O₂-induced HUVEC apoptosis, decreased reactive oxygen species and malondialdehyde production levels, increased superoxide dismutase, glutathione peroxidase and catalase activities. Furthermore, pre-treatment with LBLF increased mRNA expression of erythropoietin (EPO) and heme oxygenase-1 (HO-1) via the mitogen-activated protein kinase (MAPK) signaling pathway in HUVECs. Compared with 100 µM rutin monomer, LBLF prolonged the lifespan of Caenorhabditis elegans, enhanced their mobility in middle life stages and upregulated expression of sod-2, gcs-1 and skn-1 genes, which indicated that the anti-aging effects of LBLF were due to its redox-modulation.     

Synthesis,Structure and Theoretical Calculation of Metal Coordination Polymers with Pyrazine-2,3-dicarboxylic Acid and 3-(2-Pyridyl)pyrazole as Co-ligands

A new 2D metal coordination polymer(MCP),[Mn(pzdc)_(0.5)(L)]_n(1,pzdc=pyrazine-2,3-dicarboxylic acid,HL=3-(2-pyridyl)pyrazole),was synthesized under hydrothermal conditions and characterized by single-crystal X-ray diffraction,powder XRD,FT-IR,TG,fluorescence and elemental analysis techniques.Pale yellow crystals crystallize in orthorhombic system,space group Fdd2 with a=11.2368(6),b=38.280(2),c=10.5682(6) A,V=4545.9(4) A~3,C_(11)H_7MnN_4O_2,M_r=282.15,D_c=1.649 g/cm~3,μ(MoKα)=1.159 mm~(-1),F(000)=2272,Z=16,the final R=0.0613 and wR=0.1773 for 2856 observed reflections(I 2σ(I)).It shows a two-dimensional network structure and is further assembled into a three-dimensional supramolecular framework via hydrogen bonds and abundant π-π interactions.In addition,we analyzed natural bond orbital(NBO) of 1 in using the PBE0/LANL2DZ method established in Gaussian 03 Program.There is obvious covalent interaction between the coordinated atoms and Mn(Ⅱ) ions.