ABSTRACT
OBJECTIVE: To investigate the effects of arsenic trioxide (As2O3) on K562 cell proliferation by regulating cell cycle protein D1 and cyclin-dependent kinase inhibitor p27kip1. METHODS: MTT was used to detect the effect of As2O3 on K562 cell proliferation, so as to screen out the appropriate drug concentration. Furthermore, the K562 cell apoptosis was observed by microscopy. The expression of CyclinD1 and p27kip1 in K562 cells treated with As2O3 was analyzed by reverse transcription-polymerase chain reaction(RT-PCR), immunohistochemistry and Western blot. RESULTS: As2O3 could inhibit the proliferation of K562 cells in a dose- and time- dependent manner (r= 0.967). And the apoptosis cell number in As2O3 group was significantly higher than that in the control group(P<0.05). As2O3 could markedly inhibit the expression of CyclinD1 in K562 cells(P<0.05), but the expression of P27kip1 was not significantly changed after As2O3 treatment. CONCLUSIONS: As2O3 can induce K562 cell apoptosis and inhibit K562 cell proliferation by regulating the expression of CyclinD1.
Subject(s)
Apoptosis , Cell Proliferation , Antineoplastic Agents , Arsenic Trioxide , Arsenicals , Cell Line, Tumor , Humans , K562 Cells , OxidesABSTRACT
Gaussian process (GP) is applied in the present paper as a chemometric method to explore the complicated relationship between the near infrared (NIR) spectra and ingredients. After the outliers were detected by Monte Carlo cross validation (MCCV) method and removed from dataset, different preprocessing methods, such as multiplicative scatter correction (MSC), smoothing and derivate, were tried for the best performance of the models. Furthermore, uninformative variable elimination (UVE) was introduced as a variable selection technique and the characteristic wavelengths obtained were further employed as input for modeling. A public dataset with 80 NIR spectra of corn was introduced as an example for evaluating the new algorithm. The optimal models for oil, starch and protein were obtained by the GP regression method. The performance of the final models were evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (r). The models give good calibration ability with r values above 0.99 and the prediction ability is also satisfactory with r values higher than 0.96. The overall results demonstrate that GP algorithm is an effective chemometric method and is promising for the NIR analysis.
ABSTRACT
For the rapid detection of the total organic carbon (TOC) content and cation exchange capacity (CEC) in soil, visible/ near infrared spectra (Vis/NIR) of 300 soil samples were analyzed. The algorithm of fast independent component analysis (FastICA) was used to decompose the data of Vis/NIR spectrum, and their independent components and the mixing matrix were obtained. Then, the calibration model with three-level artificial neural networks structure was built by using Back-Propagation (BP) algorithm. Genetic algorithm was used to revise the weights of neural networks to quicken the rate of convergence and overcome the problem of falling easily into local minimums, and finally the ICA-GA-BP model was built. The models were used to estimate the content of TOC and CEC in soil samples both in calibration set and predicted set. Correlation coefficient (R2) of prediction and root mean square error of prediction (RMSEP) were used as the evaluation indexes. The results indicate that the R for the prediction of TOC content and CEC can both reach 0.98. These indicated that the results of analysis were satisfiable based on ICA method, and offer a new approach to the fast prediction of components' contents in soil.
ABSTRACT
A dataset of 310 samples of tablet were obtained by using near infrared spectroscopy (NIR) technique, and then the NIR data were used to discriminate the four types of tablets with three scales. Wavelet clustering algorithm, a new unsupervised method, which applied a classical clustering strategy on the suitably chosen subset of wavelet coefficients, was introduced to improve the clustering performance. The optimal wavelet decomposition and wavelet coefficients partition were determined according to the index of discriminant accuracy. The total accuracy rates for laboratory-scale, pilot-scale and full-scale tablets samples were 100%, 100% and 99.2%, respectively, with only one sample misclassified. The overall results indicated that the wavelet clustering was an effective way for the discrimination analysis. NIR combined with wavelet clustering method is surely much more rapid and easier to use, and offers a feasible solution to the quality control of pharmaceutical tablet products.
ABSTRACT
For the rapid detection of the ethanol, pH and rest sugar in red wine, infrared (IR) spectra of 44 wine samples were analyzed. The algorithm of fast independent component analysis (FastICA) was used to decompose the data of IR spectra, and their independent components and the mixing matrix were obtained. Then, the ICA-NNR calibration model with three-level artificial neural network (ANN) structure was built by using back-propagation (BP) algorithm. The models were used to estimate the contents of ethanol, pH and rest sugar in red wine samples for both in calibration set and predicted set. Correlation coefficient (r) of prediction and root mean square error of prediction (RMSEP) were used as the evaluation indexes. The results indicate that the r and RMSEP for the prediction of ethanol content, pH and rest sugar content are 0.953, 0.983 and 0.994, and 0.161, 0.017 and 0.181, respectively. The maximum relative deviations between the ICA-NNR method predicted value and referenced value of the 22 samples in predicted set are less than 4%. The results of this paper provide a foundation for the application and further development of IR on-line red wine analyzer.
