[Exploration for and Practice of New-Model Undergraduate Education for Innovative Talents in Biomedicine].

With the rapid development of modern biomedical technology industry, background and knowledge of a single discipline will not be adequate to meet the needs of research and development of cutting-edge technology. The cultivation of innovative research talents with interdisciplinary background at the undergraduate level poses great challenges for higher education institutions. National-level research institutes, including state key laboratories and national clinical research centers, for example, have an enormous supply of technological human resources and resources for research and teaching, which is of critical importance for the training of innovative talents at the undergraduate level. Herein, taking as an example the West China Innovation Class of State Key Laboratory of Biotherapy, a special undergraduate program founded by the State Key Laboratory of Biotherapy, Sichuan University in 2016, we reported on the explorations and practices of a new model for cultivating innovative research talents at the undergraduate level. The new model features the leadership of a national-level research institute and an interdisciplinary approach.

[Inhibition Effect of Eriodictyol to Growth of DG-75 Cells and the Related Action Mechanism].

OBJECTIVE: To explore the effects of Eriodictyol to the growth, apoptosis and oxidative stress of Burkitt lymphoma (BL) cells and phosphorylation of protein kinase B (AKT) in children. METHODS: The effects of Eriodictyol (0, 1.25, 2.5, 5, 10, 20, 40, 80, 160, 320 µmol/L) to viability of BL cell line DG-75 cells were detected by CCK-8. The effects of Eriodictyol (0, 10, 20, 40 µmol/L) to the proliferation activity of DG-75, apoptosis rate, levels of apoptosis-related proteins, oxidative stress indexes ï¼»superoxide dismutase (SOD), malondialdehyde (MDA)ï¼½, mitochondrial membrane potential (MMP) and phosphorylation level of phosphatidylinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycinm (mTOR) were detected by clony formation assay and Wester blot. RESULTS: When the treatment concentration of Eriodictyol was 20 µmol/L, the proliferation activity of the cells was decreased (P<0.05). The concentrations at 10, 20, 40 µmol/L were selected for subsequent experiments. Compared with 0 µmol/L Eriodictyol, the proliferation activity of DG-75, SOD activity, MMP, phosphorylation levels of PI3K, AKT and mTOR in 20 and 40 µmol/L Eriodictyol treatment groups were significantly decreased (P<0.05), while cells apoptosis rate, Cleaved-Caspase-3/Caspase-3, Bax/Bcl-2 and MDA level were significantly increased (P<0.05). CONCLUSION: Eriodictyol may promote the mitochondrial apoptosis pathway by inhibiting the abnormal activation of PI3K/AKT/mTOR to reduce the proliferation activity of DG-75, and inhibit oxidative stress response to increase the apoptosis rate and play anti-tumor roles.

Prediction of drug-induced eosinophilia adverse effect by using SVM and naïve Bayesian approaches.

Drug-induced eosinophilia is a potentially life-threatening adverse effect; clinical manifestations, eosinophilia-myalgia syndrome, mainly include severe skin eruption, fever, hematologic abnormalities, and organ system dysfunction. Using experimental methods to evaluate drug-induced eosinophilia is very complicated, time-consuming, and costly in the early stage of drug development. Thus, in this investigation, we established computational prediction models of drug-induced eosinophilia using SVM and naïve Bayesian approaches. For the SVM modeling, the overall prediction accuracy for the training set by means of fivefold cross-validation is 91.6 and for the external test set is 82.9 %. For the naïve Bayesian modeling, the overall prediction accuracy for the training set is 92.5 and for the external test set is 85.4 %. Moreover, some molecular descriptors and substructures considered as important for drug-induced eosinophilia were identified. Thus, we hope the prediction models of drug-induced eosinophilia built in this work should be applied to filter early-stage molecules for potential eosinophilia adverse effect, and the selected molecular descriptors and substructures of toxic compounds should be taken into consideration in the design of new candidate drugs to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

Evaluation of efficiency of traps baited with frequency trembler grid lamps and trap plants for control Heortia vitessoides infectwed in Aquilaria sinensis / 中国中药杂志

Heortia vitessoides has been a serious defoliating pest of Aquilaria sinensis forests in recent years.The adults displayed strong tropism to the frequency trembler grid lamps and the nectar source plants.The favorite nectar source plants of H.vitessoides adults as the trap plants and the frequency trembler grid lamps in the integrated management of H.vitessoides were studied in the adult eclosion period through both the laboratory and field.The results showed that Kuhnia rosmarnifolia and Santalum album plants showed strong attraction to the H.vitessoides adults, with significant differences among the different nectar source plants.K.rosmarnifolia and S.album as trap plants with board type of planting area to total planting area of 5%-10%, and the frequency trembler grid lamps trapped significantly more adults of H.vitessoides. These results suggested that the frequency trembler grid lamps and trap plants could play an important role in the integrated management of the pest H.vitessoides of A.sinensis.

Combination of pharmacophore hypothesis, genetic function approximation model, and molecular docking to identify novel inhibitors of S6K1.

S6K1 has emerged as a potential target for the treatment for obesity, type II diabetes and cancer diseases. Discovery of S6K1 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening method that involves pharmacophore hypothesis, genetic function approximation (GFA) model, and molecular docking technology has been used to discover S6K1 inhibitors especially with novel scaffolds. The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors. Then, the molecular docking method was carried out to re-filter these screened compounds. Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

Synthesis, preliminary structure-activity relationships, and in vitro biological evaluation of 6-aryl-3-amino-thieno[2,3-b]pyridine derivatives as potential anti-inflammatory agents.

In our previous study, a series of 6-aryl-3-amino-thieno[2,3-b]pyridine derivatives exhibited potent antiproliferative activities and an unique hepatocellular carcinoma (HCC)-specific anticancer activity was also observed. In further anti-inflammatory research, thienopyridine derivative 1a showed potent inhibition of nitric oxide (NO) production. So a series of thienopyridine analogues of 1a were synthesized and evaluated for anti-inflammatory activities. The structure-activity relationships (SARs) revealed that the most potent analogues 1f and 1o were identified as potent inhibitors of NO production with IC50 values of 3.30 and 3.24 µM, respectively. These results suggest that these 6-aryl-3-amino-thieno[2,3-b]pyridine derivatives might potentially constitute a novel class of anti-inflammatory agents, which require further studies.

Screening for virulence strains of Metarhizium against Dorysthenes hydropicus pascoes / 中国中药杂志

<p><b>OBJECTIVE</b>The aim of the present study was to screen the Metarhizium strains with high virulence against the larvae of Dorysthenes hydropicus, a serious pest of Citrus grandis.</p><p><b>METHOD</b>Thirty six strains of Metarhiziums were isolated from the soil of C. grandis GAP base and collected from other institutions, and the pathogenicity of these strains against 1st instar larvae of D. hydropicus was detected at concentration of 1 x 10(8) conidia/g. The high violence strains against D. hydropicus were cultivated in sabouraud dextrose yeast medium at first, then transfer to rice grain. And the sporulations of these violent strains against D. hydropicus were detected.</p><p><b>RESULT</b>Twenty-eight strains showed virulence against D. hydropicus by preliminary study, and 7 strains of them were collected for further study, 6 of the 7 showed high virulence, the highest cadaver rate was higher than 74%. The conidia production of strain 1 and strain 4 were 2.35 +/- 0. 25 (1 x 10(9) conidia/g), 2.21 +/- 0.27 (1 x 10(9) conidia/g), respectively, showed significantly higher than other strains.</p><p><b>CONCLUSION</b>Strain 1 and strain 4 of the 36 Metarhiziums strains showed high virulence against D. hydropicus, and the highest sporulation ability, so they have a best application prospect.</p>

Novel thienopyridine derivatives as specific anti-hepatocellular carcinoma (HCC) agents: synthesis, preliminary structure-activity relationships, and in vitro biological evaluation.

Novel thienopyridine derivatives 1b-1r were synthesized, based on a hit compound 1a that was found in a previous cell-based screening of anticancer drugs. Compounds 1a-1r have the following features: (1) their anticancer activity in vitro was first reported by our group. (2) The most potent analog 1g possesses hepatocellular carcinoma (HCC)-specific anticancer activity. It can specifically inhibit the proliferation of the human hepatoma HepG2 cells with an IC(50) value of 0.016µM (compared with doxorubicin as a positive control, whose IC(50) was 0.37µM). It is inactive toward a panel of five different types of human cancer cell lines. (3) Compound 1g remarkably induces G(0)/G(1) arrest and apoptosis in HepG2 cells in vitro at low micromolar concentrations. These results, especially the HCC-specific anticancer activity of 1g, suggest their potential in targeted chemotherapy for HCC.

UPLC-Q-TOF/MS analysis of naringin and naringenin and its metabolites in rat urine and feces after intragastric administration of alcohol extract of Exocarpium Citri grandis / 药学学报

To analyze naringin, naringenin and its metabolites in rat urine and feces after intragastric administration of alcohol extract of Exocarpium Citri Grandis, healthy SD rats were fed with alcohol extract of Exocarpium Citri Grandis for 3 days. On the last day, 0-24 h feces and 0-4 h, 4-8 h, 8-24 h urine were collected and analyzed by UPLC-Q-TOF/MS. The post-acquisition data were processed using Metabolynx The result is that naringin and its 6 metabolites, naringenin and its 4 metabolites were detected in the urine of rat. Meanwhile, naringin and its 3 metabolites, naringenin and its 2 metabolites were detected in the feces of rat.

[BPNN simulation of photocatalytic degradation of reactive scarlet BES by UV-Vis spectrophotometer].

The use of chemometric techniques and multivariate experimental designs for the photocatalytic reaction of reactive scarlet BES in aqueous solution under ultraviolet light irradiation is described. The efficiency of photocatalytic degradation was evaluated by the analysis of the parameter of decoloration efficiency determined by UV absorption at 540 nm using a UV-Vis spectrophotometer in different conditions. Five factors, such as the amount of titanium oxide ([TiO2]), the concentrations of reactive scarlet BES (c(0)), irradiation time (t), the pH value (pH) and temperature (T), were studied. [TiO2]. c(0), t and pH selected on the basis of the results of variance analysis by Plackett-Burman design were used as independent variables. Training sets and test sets of back propagation neural network (BPNN) were formed by Box-Behnken design and uniform design U10 (10 x 5(2) x 2) respectively. The process of photocatalytic degradation of the target object was simulated by the BPNN model. The correlation coefficient (r) of the calculation results for training set and test set by BPNN is 0.996 4 and 0.963 6 respectively, and the mean relative errors between the predictive value and experimental value of decoloration efficiency are 6.14 and 7.76, respectively. The modeled BPNN was applied to analyze the influence of four factors on decoloration efficiency. The results showed that the initial conditions of c(0) being lower, pH 5.0 and appropriate amount of [TiO2] contribute to improving the decoloration efficiency of reactive scarlet BES. Under the condition of c(0) = 40 mg x L(-1), the optimized experimental condition of the system was obtained: [TiO2] = 1.20 g x L(-1) and pH 5.0. Under the optimized experimental condition, the experimental value of decoloration efficiency is 98.20% when irradiation time is 35 minutes and the predictive value of decoloration efficiency is 99.16% under the same condition. The relative error of decoloration efficiency between the predictive value and experimental value is only -0.96%. The experimental value is very close to the model predicted value.

Three-class classification models of logS and logP derived by using GA-CG-SVM approach.

In this investigation, three-class classification models of aqueous solubility (logS) and lipophilicity (logP) have been developed by using a support vector machine (SVM) method combined with a genetic algorithm (GA) for feature selection and a conjugate gradient method (CG) for parameter optimization. A 5-fold cross-validation and an independent test set method were used to evaluate the SVM classification models. For logS, the overall prediction accuracy is 87.1% for training set and 90.0% for test set. For logP, the overall prediction accuracy is 81.0% for training set and 82.0% for test set. In general, for both logS and logP, the prediction accuracies of three-class models are slightly lower by several percent than those of two-class models. A comparison between the performance of GA-CG-SVM models and that of GA-SVM models shows that the SVM parameter optimization has a significant impact on the quality of SVM classification model.

Support vector machine and pharmacophore-based prediction models of multidrug-resistance protein 2 (MRP2) inhibitors.

Overexpression of multidrug-resistance protein 2 (MRP2) is one of the main causes that lead the curative effect reduction of many drugs, particularly anticancer drugs. Development of MRP2 inhibitors is the aim to overcome multidrug resistance due to MRP2. In this study, computational prediction models of MRP2 inhibitors have been developed by using support vector machine (SVM) and pharmacophore modeling method. For the SVM model, the overall prediction accuracy is 82.9% for the training set (257 compounds) and 77.1% for the independent test set (61 compounds). And 16 descriptors have been used in the SVM modeling; but from which it is difficult to get understanding about the action mechanism. The established pharmacophore model Hypo1 consists of two hydrogen bond acceptors and one hydrophobic feature. With the use of Hypo1, 78.1% of MRP2 inhibitors and 69.6% non-inhibitors can be predicted correctly. The overall prediction accuracy is 73.9%. Although the prediction accuracy of the pharmacophore model is lower than that of SVM model, it gives a clear picture of chemical features necessary for the MRP2 inhibitors. Taken together, the SVM model is capable of predicting MRP2 inhibitors with considerable good accuracy. But the gain of action mechanism related information needs the help of pharmacophore model.

In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach.

Drug-induced mitochondrial toxicity has become one of the key reasons for which some drugs fail to enter market or are withdrawn from market. Thus early identification of new chemical entities that injure mitochondrial function grows to be very necessary to produce safer drugs and directly reduce attrition rate in later stages of drug development. In this study, support vector machine (SVM) method combined with genetic algorithm (GA) for feature selection and conjugate gradient method (CG) for parameter optimization (GA-CG-SVM), has been employed to develop prediction model of mitochondrial toxicity. We firstly collected 288 compounds, including 171 MT+ and 117 MT-, from different literature resources. Then these compounds were randomly separated into a training set (253 compounds) and a test set (35 compounds). The overall prediction accuracy for the training set by means of 5-fold cross-validation is 84.59%. Further, the SVM model was evaluated by using the independent test set. The overall prediction accuracy for the test set is 77.14%. These clearly indicate that the mitochondrial toxicity is predictable. Meanwhile impacts of the feature selection and SVM parameter optimization on the quality of SVM model were also examined and discussed. The results implicate the potential of the proposed GA-CG-SVM in facilitating the prediction of mitochondrial toxicity.

An integrated scheme for feature selection and parameter setting in the support vector machine modeling and its application to the prediction of pharmacokinetic properties of drugs.

OBJECTIVE: Support vector machine (SVM), a statistical learning method, has recently been evaluated in the prediction of absorption, distribution, metabolism, and excretion properties, as well as toxicity (ADMET) of new drugs. However, two problems still remain in SVM modeling, namely feature selection and parameter setting. The two problems have been shown to have an important impact on the efficiency and accuracy of SVM classification. In particular, the feature subset choice and optimal SVM parameter settings influence each other; this suggested that they should be dealt with simultaneously. In this paper, we propose an integrated scheme to account for both feature subset choice and SVM parameter settings in concert. METHOD: In the proposed scheme, a genetic algorithm (GA) is used for the feature selection and the conjugate gradient (CG) method for the parameter optimization. Several classification models of ADMET related properties have been built for assessing and testing the integrated GA-CG-SVM scheme. They include: (1) identification of P-glycoprotein substrates and nonsubstrates, (2) prediction of human intestinal absorption, (3) prediction of compounds inducing torsades de pointes, and (4) prediction of blood-brain barrier penetration. RESULTS: Compared with the results of previous SVM studies, our GA-CG-SVM approach significantly improves the overall prediction accuracy and has fewer input features. CONCLUSIONS: Our results indicate that considering feature selection and parameter optimization simultaneously, in SVM modeling, can help to develop better predictive models for the ADMET properties of drugs.

Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA-CG-SVM method.

In this study, support vector machine (SVM) method combined with genetic algorithm (GA) for feature selection and conjugate gradient (CG) method for parameter optimization (GA-CG-SVM), has been employed to develop prediction models of human plasma protein binding rate (PPBR) and oral bioavailability (BIO). The advantage of the GA-CG-SVM is that it can deal with feature selection and SVM parameter optimization simultaneously. Five-fold cross-validation as well as independent test set method were used to validate the prediction models. For the PPBR, a total of 692 compounds were used to train and test the prediction model. The prediction accuracy by means of 5-fold cross-validation is 86% and that for the independent test set (161 compounds) is 81%. These accuracies are markedly higher over that of the best model currently available in literature. The number of descriptors selected is 29. For the BIO, the training set is composed of 690 compounds and external 76 compounds form an independent validation set. The prediction accuracy for the training set by using 5-fold cross-validation and that for the independent test set are 80% and 86%, respectively, which are better than or comparable to those of other classification models in literature. The number of descriptors selected is 25. For both the PPBR and BIO, the descriptors selected by GA-CG method cover a large range of molecular properties which imply that the PPBR and BIO of a drug might be affected by many complicated factors.

Pharmacophore modelling and virtual screening for identification of new Aurora-A kinase inhibitors.

Aurora-A has been identified as one of the most attractive targets for cancer therapy and a considerable number of Aurora-A inhibitors have been reported recently. In order to clarify the essential structure-activity relationship for the known Aurora-A inhibitors as well as identify new lead compounds against Aurora-A, 3D pharmacophore models were developed based on the known inhibitors. The best hypothesis, Hypo1, was used to screen molecular structural databases, including Specs and China Natural Products Database for potential lead compounds. The hit compounds were subsequently subjected to filtering by Lipinski's rules and docking study to refine the retrieved hits and as a result to reduce the rate of false positive. Finally, 39 compounds were purchased for further in vitro assay against several human tumour cell lines including A549, MCF-7, HepG2 and PC-3, in which Aurora-A is overexpressed. Two compounds show very low micromolar inhibition potency against some of these tumour cells. And they have been selected for further investigation.

Detailed conformational dynamics of juxtamembrane region and activation loop in c-Kit kinase activation process.

The stem cell factor receptor (c-Kit) plays critical roles in initiating cell growth and proliferation. Its kinase functional abnormality has been thought to associate with several human cancers. The regulation of c-Kit kinase activity is achieved by phosphorylation on the residues Tyr568 and Tyr570 within juxtamembrane region (JMR) and subsequent structural transition of JMR and activation loop (A-loop). However, the detailed conformational dynamics of JMR and A-loop are far from clear, especially whether their conformational changes are coupled or not during the kinase activation transition. In this investigation, the complete conformational transition pathway was determined using a series of nanosecond conventional molecular dynamics (MD) and targeted molecular dynamics (TMD) simulations in explicit water systems. The results of the MD simulations show that the phosphorylation of residues Tyr568 and Tyr570 within JMR induces the detachment of JMR from the kinase C-lobe and increases the fluctuation in the structure of JMR, thus appearing to initiate the kinase activation process. During the course of the TMD simulation, which characterizes the conformational transition of c-Kit from autoinhibitory to activated state, the JMR undergoes a rapid departure from the allosteric binding site and drifts into solvent, followed by the conformational flip of A-loop from inactive (fold) state to active (extended) state. A change in the orientation of helix alphaC in response to the motion of JMR and A-loop has also been observed. The computational results presented here indicate that the dissociation of JMR from the kinase domain is prerequisite to c-Kit activation, which is consistent with previous experiments.

Bioassay of estrogenic activity of effluent and influent in a farm wastewater treatment plant using an in vitro recombinant assay with yeast cells / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>Environmental estrogens at an elevated concentration are known to produce adverse effects on human and animal life. However, the majority of researches have been focused on industrial discharges, while the impact of livestock wastes as a source of endocrine disrupters in aquatic environments has been rarely elucidated. In order to investigate the contribution of environmental estrogens from livestock, the estrogenic activity in water samples from a farm wastewater treatment plant was analyzed by a recombinant yeast screening method.</p><p><b>METHODS</b>The extracts prepared from 15 selected water samples from the farm wastewater treatment plant, among which 6 samples were from pre-treatment section (influents) and 9 from post-treatment section (effluents), were analyzed for estrogenic activity by cellar bioassay. Yeast cells transfected with the expression plasmid of human estrogen receptor and the Lac Z reporter plasmid encoding beta-galactossidase, were used to measure the estrogen-like compounds in the farm wastewater treatment plant.</p><p><b>RESULTS</b>The wastewater samples from influents showed a higher estrogenic potency than the effluent samples showing a low induction of beta-galactossidase relative to solvent control condition. By comparison with a standard curve for 17 beta-estradiol (E2), estrogenic potency in water samples from the influents was calculated as E2-equivalent and ranged from 0.1 to 150 pM E2-equivalent. The estrogenic potency in water samples from the effluents was significantly lower than that in the influents, and 7 water samples had less detectable limit in the total of 9 samples.</p><p><b>CONCLUSION</b>Yeast bioassay of estrogenic activity in most of the samples from the farm wastewater after disposal by traditional sewage treatment showed negative results.</p>

Primary studies on biological characteristics of Dorysthenes hydropicus / 中国中药杂志

<p><b>OBJECTIVE</b>To study the biological characteristics of Dorysthenes hydropicus in the farm of Cirtus grandis, and offer scientific evidence for prevention and controlling of D. hydropicus.</p><p><b>METHOD</b>Indoor-rearing and light trap were applied to study the biological characteristics, development course and harmful effect of D. hydropicus.</p><p><b>RESULT</b>D. hydropicus reproduces one generation in 1-2 year in Guangdong province, and overwinters in the form of larvae. Its imago comes out of the earth mainly in late May after mature. The body length has great individual diversity normally ranged from 25-60 cm, It also shows strong phototaxy. One lamp can trap more than 2 000 of them per night. Female imago has a large egg load with the maximum amount of 543. The eggs hatching is in depth of 1-3 cm soil. The dominant hatching period of egg is from late June to early July, and hatchability is over 85%. The living space of larva ranges from 15-60 cm in soil. D. hydropicus has caused serious harm and lead to thousands of Cirtus grandis trees death every year.</p><p><b>CONCLUSION</b>Dorysthenes hydropicus showed serious threat to the growth of Cirtus grandis and should be prevented and controlled.</p>

Free energy profiles for monomer capture in Grubbs- and SHOP-type olefin polymerization catalysts: a constraint ab initio molecular dynamics study.

Density functional theory together with Car-Parrinello ab initio molecular dynamics simulation has been used to investigate the free energy profiles (FEP) of monomer capture in Grubbs- and SHOP-type olefin polymerization catalysts. The FEPs along the reaction coordinates at 300 K were determined directly by a point wise thermodynamic integration technique. Comparison between potential energy profile (PEP) and the FEP has been made. The results show that, for both catalysts, the PEP for the monomer ethylene uptake by the metal center is a typical Morse curve without energy barrier. However, a small barrier (1.8 kcal/mol for Grubbs catalyst and 2.4 kcal/mol for SHOP catalyst) exists on the FEP. The pi complexation energy on the FES at 300 K is higher by 10-12 kcal/mol over that on the PES. The differences between FES and PES are due to entropy contribution. Slow growth simulations on the ethylene capture process show that the ethylene attacks the metal center by an asynchronous mode. This indicates that the forming of the pi-bonding between the metal and ethylene is initiated by electrophilic attack of the metal to one of the ethylene carbons.