Synthesis, Anti-Varicella-Zoster Virus and Anti-Cytomegalovirus Activity of 4,5-Disubstituted 1,2,3-(1H)-Triazoles.

BACKGROUND: Clinical drugs for herpesvirus exhibit high toxicity and suffer from significant drug resistance. The development of new, effective, and safe anti-herpesvirus agents with different mechanisms of action is greatly required. OBJECTIVE: Novel inhibitors against herpesvirus with different mechanisms of action from that of clinical drugs. METHODS: A series of novel 5-(benzylamino)-1H-1,2,3-triazole-4-carboxamides were efficiently synthesized and EC50 values against Human Cytomegalovirus (HCMV), Varicella-Zoster Virus (VZV) and Herpes Simplex Virus (HSV) were evaluated in vitro. RESULTS: Some compounds present antiviral activity. Compounds 5s and 5t are potent against both HCMV and VZV. Compounds 5m, 5n, 5s, and 5t show similar EC50 values against both TK+ and TK- VZV strains. CONCLUSION: 5-(Benzylamino)-1H-1, 2,3-triazole-4-carboxamides are active against herpesviruses and their activity is remarkably affected by the nature and the position of substituents in the benzene ring. The results indicate that these derivatives are independent of the viral thymidine kinase (TK) for activation, which is indispensable for current drugs. Their mechanisms of action may differ from those of the clinic anti-herpesvirus drugs.

Synthesis and Ativiral Activity of 5-(Benzylthio)-4-carbamyl-1,2,3-triazoles Against Human Cytomegalovirus (CMV) and Varicella-zoster Virus (VZV).

BACKGROUND: All of the clinical drugs for herpesvirus infections exhibit high toxicity and suffer from significant drug-resistantance. There is a great need for the development of new, effective, and safe anti-herpesvirus agents with different mechanisms of action. METHODS: A series of novel 5-(benzylthio)-1H-1,2,3-triazole-4-carboxamides were efficiently synthesized and EC50 values against human cytomegalovirus (HCMV), varicella-zoster virus (VZV) and herpes simplex virus (HSV) were evaluated in vitro. RESULTS: Some compounds possess antiviral activity. Compound 7f exhibits promising inhibitory activity against both HCMV and VZV. Our results also indicate that these derivatives are independent of the viral thymidine kinase (TK) for activation, which is indispensable for current drugs. CONCLUSION: 4,5-Bissubstiuted triazoles are active against herpesviruses and the nature and the position of substituents in the benzene ring remarkably affect their activity, such as bromo, cyano and cyanovynil substituents. Future studies should be undertaken to investigate the mechanism of action of these compounds.

Discovery of Potential Orthosteric and Allosteric Antagonists of P2Y1R from Chinese Herbs by Molecular Simulation Methods.

P2Y1 receptor (P2Y1R), which belongs to G protein-coupled receptors (GPCRs), is an important target in ADP-induced platelet aggregation. The crystal structure of P2Y1R has been solved recently, which revealed orthosteric and allosteric ligand-binding sites with the details of ligand-protein binding modes. And it suggests that P2Y1R antagonists, which recognize two distinct sites, could potentially provide an efficacious and safe antithrombotic profile. In present paper, 2D similarity search, pharmacophore based screening, and molecular docking were used to explore the potential natural P2Y1R antagonists. 2D similarity search was used to classify orthosteric and allosteric antagonists of P2Y1R. Based on the result, pharmacophore models were constructed and validated by the test set. Optimal models were selected to discover potential P2Y1R antagonists of orthosteric and allosteric sites from Traditional Chinese Medicine (TCM). And the hits were filtered by Lipinski's rule. Then molecular docking was used to refine the results of pharmacophore based screening and analyze the binding mode of the hits and P2Y1R. Finally, two orthosteric and one allosteric potential compounds were obtained, which might be used in future P2Y1R antagonists design. This work provides a reliable guide for discovering natural P2Y1R antagonists acting on two distinct sites from TCM.

[Identification of Official Rhubarb Samples by Using PLS and Terahertz Time-Domain Spectroscopy].

The development of terahertz technology is attracting broad intention in recent years. The quality identification is important for the quality control of Chinese medicine production. In the present work, terahertz time-domain spectroscopy (THz-TDS) combined with partial least squares (PLS) were used for the identification model building and studied based on 41 official and unofficial rhubarb samples. First, the THz-TDS spectra of rhubarb samples were collected and were preprocessed by using chemometrics methods rather than transformed to absorption spectra. The identification models were then established based on the processed terahertz time domain spectra. The spectral preprocessing methods include Savitzky-Golay (S-G) first derivative, detrending, standard normal transformation (SNV), autoscaling, and mean centering. The identification accuracy of 90% was accomplished by using proper pretreatment methods, which was higher than the classified accuracy of 80% without any preprocessing for the time domain spectra. The component number of the PLS model was evaluated by leave-one-out cross-validation (LOOCV). The minimum values of the root-mean squared error of cross-validation (RMSECV) and root-mean squared error of prediction (RMSEP) were 0.076 6 and 0.169 0 by using mean centering method, respectively. The results of this work showed that the combination of terahertz time domain spectroscopy technology with chemometrics methods, as well as PLS can be applied for the recognition of genuine and counterfeit Chinese herbal medicines, as well as official and unofficial rhubarbs. The advantage of using terahertz time domain spectra directly with no transformation into absorption spectra is: (1) the thickness of samples could not be considered in the model establishment, and (2) the spectral processing was simplified. The proposed method based on the combination of THz-TDS and chemometrics proved to be rapid, simple, non-pollution and solvent free, suitable to be developed as a promising tool for quality control of many other Chinese herbal medicines.

[Determination of Carbaryl in Rice by Using FT Far-IR and THz-TDS Techniques].

Determination of carbaryl in rice by using Fourier transform far-infrared (FT- Far-IR) and terahertz time-domain spectroscopy (THz-TDS) combined with chemometrics was studied and the spectral characteristics of carbaryl in terahertz region was investigated. Samples were prepared by mixing carbaryl at different amounts with rice powder, and then a 13 mm diameter, and about 1 mm thick pellet with polyethylene (PE) as matrix was compressed under the pressure of 5-7 tons. Terahertz time domain spectra of the pellets were measured at 0.5~1.5 THz, and the absorption spectra at 1.6. 3 THz were acquired with Fourier transform far-IR spectroscopy. The method of sample preparation is so simple that it does not need separation and enrichment. The absorption peaks in the frequency range of 1.8-6.3 THz have been found at 3.2 and 5.2 THz by Far-IR. There are several weak absorption peaks in the range of 0.5-1.5 THz by THz-TDS. These two kinds of characteristic absorption spectra were randomly divided into calibration set and prediction set by leave-N-out cross-validation, respectively. Finally, the partial least squares regression (PLSR) method was used to establish two quantitative analysis models. The root mean square error (RMSECV), the root mean square errors of prediction (RMSEP) and the correlation coefficient of the prediction are used as a basis for the model of performance evaluation. For the R,, a higher value is better; for the RMSEC and RMSEP, lower is better. The obtained results demonstrated that the predictive accuracy of. the two models with PLSR method were satisfactory. For the FT-Far-IR model, the correlation between actual and predicted values of prediction samples (Rv) was 0.99. The root mean square error of prediction set (RMSEP) was 0.008 6, and for calibration set (RMSECV) was 0.007 7. For the THz-TDS model, R. was 0. 98, RMSEP was 0.004 4, and RMSECV was 0.002 5. Results proved that the technology of FT-Far-IR and THz- TDS can be a feasible tool for quantitative determination of carbaryl in rice. This paper provides a new method for the quantitative determination pesticide in other grain samples.

Synthesis and Bioactivity of Novel Trisubstituted Triazole Nucleosides.

A series of novel trisubstituted 1,2,3-triazole purine nucleosides were efficiently synthesized via Huisgen 1,3-dipolar cycloaddition in good yields. Bioactivity against cytomegalovirus (CMV) and varicella-zoster virus (VZV) in human embryonic lung cell cultures was evaluated and all compounds show low antiviral activity.

[Application of terahertz technology in medical testing and diagnosis].

Terahertz science and technology is increasingly emphasized in science and industry, and has progressed significantly in recent years. There is an important aspect of attention in the application of terahertz technology to medicine. The overview of the terahertz characters, terahertz spectroscopy and terahertz imaging technology is introduced. This paper focuses on reviewing the use of and research progress in terahertz spectroscopy and terahertz imaging technology in medical testing and diagnosis. Furthermore, the problems to be solved and development directions of terahertz spectroscopy and terahertz imaging technology are discussed.

[Diagnosis of endometrial cancer based on logistic regression and near infrared spectroscopy].

Endometrial carcinoma is one of the most common gynecologic cancers. The present paper reports a new application of Logistic regression to building model of endometrial cancer. Near infrared (NIR) spectra was introduced. In our study, the NIR spectra of 77 specimens were pretreated by principal component-linear discriminant analysis (PC-LDA) and support vector machine discriminant analysis (SVM-DA). Latin partition method for selecting training and test sets was used to determine the significant parameters for Logistic regression model. From the predicted results of logistic regression model, both the categories of samples and the trends of samples belonging to other class were clear and concordant with the clinical result. The proposed procedure proved to be suitable to being developed as a noninvasive diagnosis method for cancer tissue.

[Study on artificial neural network combined with near infrared spectroscopy for wood species identification].

In the present article, near infrared spectra of 89 wood samples of different geographical provenances and species were measured, and back propagation artificial neural networks(BPANN) and generalized regression neural network (GRNN) were used for modeling of wood species NIRS identifying. Parameters for two neural networks were chosen via analysis of variance, respectively; and networks were trained with optimum parameters. Considering the difference between spectra, spectra with different levels of white noise and different levels of bias were simulated and predicted by using the models built. It was found that both the two models had satisfactory prediction results, identification correct rates obtained by BPANN model applied to spectra with bias level no higher than 2% and noise level no higher than 4% were above 97%; correct rates obtained by GRNN model applied to spectra with bias level no higher than 2% and noise level no higher than 4% were above 99%.

[Early stage diagnosis of endometrial cancer based on near infrared spectroscopy and support vector machine].

Near-infrared spectroscopy combined with chemometrics methods for diagnosis of cancer has been reported in literatures. In our study, the NIR spectra of 77 specimens of different physiological stages of endometrium were collected. Spectral data were pretreated firstly by multiplicative scatter correction (MSC), orthogonal signal correction (OSC), and both of them, respectively, and then by SG smoothing. Latin partition method was used to select 3/4 samples as a training set, and the other 1/4 samples for test set. Support vector machine (SVM) model was built for classification, and the classification results was compared with that of partial least squares (PLS) model based on the same pretreatment methods. Samples of malignant, hyperplasia and normal endometrium were classified better by SVM (classification accuracy was 92%) than PLS (classification accuracy was 90%). The results suggested that classification accuracy was affected by pretreatment methods and models. SVM combined with endometrial tissue near infrared spectroscopy is expected to develop into a new approach to tumor diagnosis.

[Study of near infrared spectral preprocessing and wavelength selection methods for endometrial cancer tissue].

Near infrared spectroscopy was applied to measure the tissue slice of endometrial tissues for collecting the spectra. A total of 154 spectra were obtained from 154 samples. The number of normal, hyperplasia, and malignant samples was 36, 60, and 58, respectively. Original near infrared spectra are composed of many variables, for example, interference information including instrument errors and physical effects such as particle size and light scatter. In order to reduce these influences, original spectra data should be performed with different spectral preprocessing methods to compress variables and extract useful information. So the methods of spectral preprocessing and wavelength selection have played an important role in near infrared spectroscopy technique. In the present paper the raw spectra were processed using various preprocessing methods including first derivative, multiplication scatter correction, Savitzky-Golay first derivative algorithm, standard normal variate, smoothing, and moving-window median. Standard deviation was used to select the optimal spectral region of 4 000-6 000 cm(-1). Then principal component analysis was used for classification. Principal component analysis results showed that three types of samples could be discriminated completely and the accuracy almost achieved 100%. This study demonstrated that near infrared spectroscopy technology and chemometrics method could be a fast, efficient, and novel means to diagnose cancer. The proposed methods would be a promising and significant diagnosis technique of early stage cancer.

[Quality analysis of olive oil and quantification detection of adulteration in olive oil by near-infrared spectrometry and chemometrics].

Discriminant analysis was used to classify 20 olive oil samples based on their near-infrared (NIR) spectra. The samples were successfully classified into two categories which are consistent with extra virgin olive oil and ordinary olive oil defined in the products. The NIR spectra of olive-oil mixtures containing colza oil, corn oil, peanut oil, camellia oil, sunflower oil, and poppy seed oil were collected, respectively. The volume percent of adulterants ranged from 0 to 100%. The best spectrum bands for analysis were selected before developing partial least-squares (PLS) calibration models. The relative errors of prediction ranged from -5.67% to 5.61%. Results showed that the method combined with chemometrics methods and near-infrared spectrometry is simple, fast and credible for qualitative and quantitative analyses of olive oil samples.

[Quantitative prediction of holocellulose, lignin, and microfibril angle of Chinese fir by BP-ANN and NIR spectrometry].

The amount of holocellulose, lignin, and microfibril angle of Chinese fir was predicted by using back-propagation neural network (BP-ANN) combined with near infrared (NIR) spectrometry. First, the data of original spectra were pretreated by Savitzky-Golay smoothing algorithm and the second derivative, then the data of near infrared spectrometry with 171 points were compressed to 86 points by using wavelet transform, and finally, the models were established by using BP-ANN. The models were validated using leave-n-out cross-validation approach, and the influences of the number of hidden neurons, learning rate, momentum, and epochs were discussed in the present paper. The prediction samples, which were not used in the model generation, were predicted by using the obtained models, the correlation coefficients (R2) of holocellulose, lignin and microfibril angle were 0.91, 0.90 and 0.87, respectively. The root mean square errors of prediction (RMSEP) of the established models were 0.86%, 0.33%, and 4.9%, respectively. The obtained results showed that the method is fast and nondestructive and can basically satisfy the requirement of quantitative analysis.