Pharmacokinetic Analysis of Four Bioactive Iridoid and Secoiridoid Glycoside Components of Radix Gentianae Macrophyllae and Their Synergistic Excretion by HPLC-DAD Combined with Second-Order Calibration.

An HPLC-DAD method combined with second-order calibration based on the alternating trilinear decomposition (ATLD) algorithm with the aid of region selection was developed to simultaneously and quantitatively characterize the synergistic relationships and cumulative excretion of the four bioactive ingredients of Radix Gentianae Macrophyllae in vivo. Although the analytes spectra substantially overlapped with that of the biological matrix, the overlapping profiles between analytes and co-eluting interferences can be successfully separated and accurately quantified by the ATLD method on the basis of the strength of region selection. The proposed approach not only determined the content change but also revealed the synergistic relationships and the cumulative excretion in vivo of the four ingredients in urine and feces samples collected at different excretion time intervals. In addition, several statistical parameters were employed to evaluate the accuracy and precision of the method. Quantitative results were confirmed by HPLC-mass spectrometry. Satisfactory results indicated that the proposed approach can be utilized to investigate the pharmacokinetics of Radix Gentianae Macrophyllae excretion in vivo.

Detection of unexpected frauds: Screening and quantification of maleic acid in cassava starch by Fourier transform near-infrared spectroscopy.

Fourier transform near-infrared (FT-NIR) spectroscopy and chemometrics were adopted for the rapid analysis of a toxic additive, maleic acid (MA), which has emerged as a new extraneous adulterant in cassava starch (CS). After developing an untargeted screening method for MA detection in CS using one-class partial least squares (OCPLS), multivariate calibration models were subsequently developed using least squares support vector machine (LS-SVM) to quantitatively analyze MA. As a result, the OCPLS model using the second-order derivative (D2) spectra detected 0.6%(w/w) adulterated MA in CS, with a sensitivity of 0.954 and specificity of 0.956. The root mean squared error of prediction (RMSEP) was 0.192(w/w, %) by using the standard normal variate (SNV) transformation LS-SVM. In conclusion, the potential of FT-NIR spectroscopy and chemometrics was demonstrated for application in rapid screening and quantitative analysis of MA in CS, which also implies that they have other promising applications for untargeted analysis.

Automatic time-shift alignment method for chromatographic data analysis.

Time shift among samples remains a significant challenge in data analysis, such as quality control of natural plant extracts and metabolic profiling analysis, because this phenomenon may lead to invalid conclusions. In this work, we propose a new time shift alignment method, namely, automatic time-shift alignment (ATSA), for complicated chromatographic data analysis. This technique comprised the following alignment stages: (1) automatic baseline correction and peak detection stage for providing useful chromatographic information; (2) preliminary alignment stage through adaptive segment partition to correct alignment for the entire chromatogram; and (3) precise alignment stage based on test chromatographic peak information to accurately align time shift. In ATSA, the chromatographic peak information of both reference and test samples can be completely employed for time-shift alignment to determine segment boundaries and avoid loss of information. ATSA was used to analyze a complicated chromatographic dataset. The obtained correlation coefficients among samples and data analysis efficiency indicated that the influences of time shift can be considerably reduced by ATSA; thus accurate conclusion could be obtained.

Simple automatic strategy for background drift correction in chromatographic data analysis.

Chromatographic background drift correction, which influences peak detection and time shift alignment results, is a critical stage in chromatographic data analysis. In this study, an automatic background drift correction methodology was developed. Local minimum values in a chromatogram were initially detected and organized as a new baseline vector. Iterative optimization was then employed to recognize outliers, which belong to the chromatographic peaks, in this vector, and update the outliers in the baseline until convergence. The optimized baseline vector was finally expanded into the original chromatogram, and linear interpolation was employed to estimate background drift in the chromatogram. The principle underlying the proposed method was confirmed using a complex gas chromatographic dataset. Finally, the proposed approach was applied to eliminate background drift in liquid chromatography quadrupole time-of-flight samples used in the metabolic study of Escherichia coli samples. The proposed method was comparable with three classical techniques: morphological weighted penalized least squares, moving window minimum value strategy and background drift correction by orthogonal subspace projection. The proposed method allows almost automatic implementation of background drift correction, which is convenient for practical use.

A simple multi-scale Gaussian smoothing-based strategy for automatic chromatographic peak extraction.

Peak detection is a critical step in chromatographic data analysis. In the present work, we developed a multi-scale Gaussian smoothing-based strategy for accurate peak extraction. The strategy consisted of three stages: background drift correction, peak detection, and peak filtration. Background drift correction was implemented using a moving window strategy. The new peak detection method is a variant of the system used by the well-known MassSpecWavelet, i.e., chromatographic peaks are found at local maximum values under various smoothing window scales. Therefore, peaks can be detected through the ridge lines of maximum values under these window scales, and signals that are monotonously increased/decreased around the peak position could be treated as part of the peak. Instrumental noise was estimated after peak elimination, and a peak filtration strategy was performed to remove peaks with signal-to-noise ratios smaller than 3. The performance of our method was evaluated using two complex datasets. These datasets include essential oil samples for quality control obtained from gas chromatography and tobacco plant samples for metabolic profiling analysis obtained from gas chromatography coupled with mass spectrometry. Results confirmed the reasonability of the developed method.

[Study on the anti-apoptosis effect of insulin-like growth factor I on the regulation of neural precursor cells survival].

OBJECTIVE: To investigate the effect of insulin-like growth factor I (IGF-I) on the anti-apoptosis of neural precursor cells. METHODS: Rat neural precursor cells (Ge6) were used as experiment object. 10 ng/mL IGF- I were applied to cultured Ge6 differentiated 2 d, 4 d, 6 d and the untreated cells as control. Quantitative reverse transcriptase PCR (qRT-PCR) was used to detect the effect of the IGF-I on caspase-3 mRNA in the Ge6 of different groups and Immunofluorescent staining were applied to obverse the positive rate of caspase-3. RESULTS: qRT-PCR demonstrated that caspase-3 mRNA level of experimental group decreased compared to that of the control group, the differences between experimental group cultured for 4 d (2.007 +/- 0.297), 6 d (1.194 +/- 0.244) and the control group cultured for 4 d (5.530 +/- 1.027), 6 d (5.371 +/- 0.513) were statistically significant (P < 0.05). Immunofluorescent staining indicated that the caspase-3 positive rate of experimental group cultured for 2 d, 4 d and 6 d were reduced compared to that of the control group, the differences between experimental group cultured for 4 d (3.5% +/- 10.2%), 6 d (5.1% +/- 0.1%) and the control group cultured for 4 d (7.0% +/- 10.5%), 6 d (7.4% +/- 0.4%) were statistically significant (P < 0.05). CONCLUSION: IGF-I plays an anti-apoptosis role on neural precursor cells.