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The construction of digital experimental teaching platform needs to have a forward-looking comprehensive plan and supporting construction to guide and support. Based on the optimization and improvement in the physical laboratory, we continue to advance informatization process, such as building a wealth of data resources, strengthening the usability and ease of the system, improving interaction between systems more smoothly, enhancing timeliness of hardware maintenance and software updates to ensure the smooth and efficient operation platform, etc. The new teaching mode on the basis of it, such as online education, are explored and practiced. Fully functional, highly integrated and efficient platform will play its important role in the cultivation of innovative talents.

In order to enhance the extraction rate of polysaccharides, a series of statistical approach was used to optimize the ultrasonic-assisted extraction conditions from the mycelium of Paecilomyces tenuipes Pt196. [Metho The optimization of conditions was carried out in two stages. Firstly, the effects of various experimental parameters considered for the investigation (ultrasonic power, ultrasonic time and liquid-solid ratio) were studied using the method of single factor test design experiments. Secondly, a 15-run Box-Behnken design was performed to optimize the extraction conditions of polysaccharides. The experimental results of Box-Behnken design were analyzed by response surface methodology and artificial neural network together with genetic algorithm. [Result The optimum conditions for polysaccharides extraction obtained by the application of artificial neural network-genetic algorithm were ultrasonic time 345s, ultrasonic power 320 W and liquid-solid ratio 95 mL·g-1. [Conclusion Artificial neural network-genetic algorithm can effectively select the best extraction conditions of polysaccharides in this study.

To inspire students interest in learning, cultivate their spirit of innovation, and comprehensively improve the level of experimental lesson, the virtual system of physiological function experiments based on networks was developed, applied. [Methods] With establishment of the data management and service functions, and the support of the database, this system provides material library for various functional virtual experiments It could respond the clients access successfully. In addition, the system is available for campus network and internet access with employing network architecture. [Results] Through the actual operation, it is shown that the system run stably and well with vivid images. It is conducive to stimulate students initiative and develop their ability to innovate. [Conclusions] This system can be used in classroom, or for self-studying with networks. Therefore, it could enhances the students interest in learning and improves their creativity and imagination.

An experimental mixture design coupled with data analysis by means of both response surface methodology(RSM) and artificial neural network(ANN) was applied to explore the optimum process parameters for adenosine extraction from cultured mycelium of Cordyceps militaris. With the extraction rate of adenosine as index, the critical factors selected for the investigation were extracting temperature, extracting time and solid-liquid radio. The results obtained by the application of ANN were more reliable since better statistical parameters were obtained. The optimum extraction procedure was as follow: extracting time 2.3 h, extracting temperature 48 °C, solid-liquid ratio 1:38 g⋅mL-1. Under the optimal conditions, the corresponding response value predicted for adenosine production was 4.59 mg g-1, which was confirmed by validation experiments.

Nanosized hydroxyapatite (nsHAp) was synthesized to examine its possibility as a controlled release carrier of protein. To achieve effective protein release from nanosized hydroxyapatite, the study of the adsorptive properties of protein on nsHAp and different influence parameters such as pH, calcium, and phosphate concentrations during the adsorption process is necessary. Ovalbumin(OVA) was selected as the model of growth factors. The results show that the amount of OVA adsorbed onto nsHAp in acetic buffer(pH = 3.6) is more than that in acetic buffer(pH =5.6) because of the electric interaction. The amount of OVA adsorption in phosphate buffer solution(PBS) is smaller than that in acetic buffer because of surface complexation and surface hydroxylation. The presence of Ca2+ dramatically increases the adsorbed amount of OVA in acetic buffer on maintaining the same pH. Meanwhile, the release kinetics of OVA adsorbed onto nsHAp(nsHAp-OVA) was also examined. The amount of released OVA in PBS(pH =5.6) was significantly smaller than that released in solution of pH = 7.0. All the results suggest that nanosized hydroxyapatite particles could be successfully used as controlled released carrier of protein.

Jia-Yuan-Qing pill (JYQP) composed of Porcellio laevis Latreille, Corydalis Rhizoma and Radix Cynanchi Paniculati at a ratio of 9:7:7 has been found to be an effective analgesic agent. The present study aimed to evaluate the safety, addictive potential and anti-cancer pain activity of JYQP in a rat model. During the 6-month chronic toxicity test, no significant changes in general behavior, defecation, postural abnormalities, dietary or water intake or blood biochemical parameters were observed in male and female rats. Although a high dose of JYQP (5 g/kg) caused swelling of the liver, spleen and kidney in male and female rats, no pathological changes were observed in all organs examined via hematoxylin and eosin staining. The analgesic effect of JYQP on bone cancer pain was successfully confirmed in a rat model of Walker 256 cell-induced bone cancer. In contrast to morphine, in a physical dependence test, JYQP produced no withdrawal symptoms following chronic administration. The data from this study provide experimental evidence supporting the clinical use of JYQP as an effective, safe and non-addictive agent for the treatment of bone cancer pain.

Partial least squares (PLS) and radial basis function neural network (RBFNN) combined with near infrared spectros- copy (NIR) were applied to develop models for cordycepic acid, polysaccharide and adenosine analysis in Paecilomyces hepialid fermentation mycelium. The developed models possess well generalization and predictive ability which can be applied for crude drugs and related productions determination. During the experiment, 214 Paecilomyces hepialid mycelium samples were obtained via chemical mutagenesis combined with submerged fermentation. The contents of cordycepic acid, polysaccharide and adenosine were determined via traditional methods and the near infrared spectroscopy data were collected. The outliers were removed and the numbers of calibration set were confirmed via Monte Carlo partial least square (MCPLS) method. Based on the values of degree of approach (Da), both moving window partial least squares (MWPLS) and moving window radial basis function neural network (MWRBFNN) were applied to optimize characteristic wavelength variables, optimum preprocessing methods and other important variables in the models. After comparison, the RBFNN, RBFNN and PLS models were developed successfully for cordycepic acid, polysaccharide and adenosine detection, and the correlation between reference values and predictive values in both calibration set (R2c) and validation set (R2p) of optimum models was 0.9417 and 0.9663, 0.9803 and 0.9850, and 0.9761 and 0.9728, respectively. All the data suggest that these models possess well fitness and predictive ability.

This is a systemic review of plants used traditionally for neuroendocrinological diseases related to hypothalamus-pitutary-adrenal (HPA) and hypothalamus-pitutary-gland (HPG) axis. By searching from PubMed literature search system (1950-2013), Medline (1950-2013) and CNKI (China Journals of Full-text database; 1989-2013), 105 papers met the inclusion criteria were displayed in this review. 96 herbal drugs were classified into two parts which include hormones mainly related to HPA and HPG axis. The full scientific name of each herbal medicine, dose ranges and routes, models or diseases, affect on hormones and pertinent references are presented via synoptic tables. Herbal remedies that have demonstrable the activities of hormones have provided a potential to various diseases related to neunoendocrine and deserve increased attention in future studies. This review provides a basis for herbs use in neuroendocrinological diseases. The data collected here will benefit to further research associated to herbal medicines and hormones.

A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.

To optimize the extraction condition for the polysaccharides from Marasmius androsaceus Mycelium.The single factor tests and response surface methodology (RSM) were applied. Overall research was proceeded by rational desingn. Technological condition parameters were determined according to regression analysis by SAS.The optimum condition was as follows: extraction temperature was 78 degrees C , extraction-duration was 1. 5h, water to material ratio was 36: 1. The optimum extracting rate for the polysaccharide was 11. 59%.The RSM is for optimum extraction process and it will improve the extraction conditions of polysaccharides.

This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm performance. Simulation results show that the proposed method obtains high quality precision in the generalization, compared with multiple linear regression, and that it is an efficient approach to regression estimation.