Pregnane X receptor (PXR) deficiency protects against spinal cord injury by activating NRF2/HO-1 pathway.

INTRODUCTION: As a devastating neurological disease, spinal cord injury (SCI) results in severe tissue loss and neurological dysfunction. Pregnane X receptor (PXR) is a ligand-activated nuclear receptor with a major regulatory role in xenobiotic and endobiotic metabolism and recently has been implicated in the central nervous system. In the present study, we aimed to investigate the role and mechanism of PXR in SCI. METHODS: The clip-compressive SCI model was performed in male wild-type C57BL/6 (PXR+/+ ) and PXR-knockout (PXR-/- ) mice. The N2a H2 O2 -induced injury model mimicked the pathological process of SCI in vitro. Pregnenolone 16α-carbonitrile (PCN), a mouse-specific PXR agonist, was used to activate PXR in vivo and in vitro. The siRNA was applied to knock down the PXR expression in vitro. Transcriptome sequencing analysis was performed to discover the relevant mechanism, and the NRF2 inhibitor ML385 was used to validate the involvement of PXR in influencing the NRF2/HO-1 pathway in the SCI process. RESULTS: The expression of PXR decreased after SCI and reached a minimum on the third day. In vivo, PXR knockout significantly improved the motor function of mice after SCI, meanwhile, inhibited apoptosis, inflammation, and oxidative stress induced by SCI. On the contrary, activation of PXR by PCN negatively influenced the recovery of SCI. Mechanistically, transcriptome sequencing analysis revealed that PXR activation downregulated the mRNA level of heme oxygenase-1 (HO-1) after SCI. We further verified that PXR deficiency activated the NRF2/HO-1 pathway and PXR activation inhibited this pathway in vitro. CONCLUSION: PXR is involved in the recovery of motor function after SCI by regulating NRF2/HO-1 pathway.

[Using a modified remote sensing imagery for interpreting changes in cultivated saline-alkali land].

This paper developed a new interpretation symbol system for grading and classifying saline-alkali land, using Huanghua, a cosatal city in Hebei Province as a case. The system was developed by inverting remote sensing images from 1992 to 2011 based on site investigation, plant cover characteristics and features of remote sensing images. Combining this interpretation symbol system with supervising classification method, the information on arable land was obtained for the coastal saline-alkali ecosystem of Huanghua City, and the saline-alkali land area, changes in intensity of salinity-alkalinity and spatial distribution from 1992 to 2011 were analyzed. The results showed that salinization of arable land in Huanghua City alleviated from 1992 to 2011. The severely and moderately saline-alkali land area decreased in 2011 compared with 1992, while the non/slightly saline land area increased. The moderately saline-alkali land in southeast transformed to non/slightly saline-alkaline, while the severely saline-alkali land in west of the city far from the coastal zone became moderately saline-alkaline. The center of gravity (CG) of severely and non/slightly saline-alkali land moved closer the coastline, while that of the moderately saline-alkali land moved from southwest coastal line to northwest. Factors influencing changes in arable land within the saline-alkali ecosystem of Huanghua City were climate, hydrology and human activities.

Prediction of quantitative calibration factors of some organic compounds in gas chromatography.

A quantitative structure-property relationship (QSPR) methodology that involves multilinear (Hansch-type) and nonlinear (radial basis function neural network (RBFNN)) approaches was performed to correlate the quantitative molar calibration factors (f(M)) of 140 organic compounds against structural factors. The statistical characteristics provided by the multiple linear model (R(2) = 0.963; RMS = 0.089; AARD = 3.86% for test set) indicated satisfactory stability and predictive ability, while the predictive ability of the RBFNN model is somewhat superior (R(2) = 0.983; RMS = 0.075; AARD = 3.19% for test set). The multilinear model provided some insight into the main structure factors that modulate the quantitative calibration factor of the investigated compounds.

Quantitative structure-property relationship study for estimation of quantitative calibration factors of some organic compounds in gas chromatography.

Quantitative structure-property relationship (QSPR) models have been used to predict and explain gas chromatographic data of quantitative calibration factors (f(M)). This method allows for the prediction of quantitative calibration factors in a variety of organic compounds based on their structures alone. Stepwise multiple linear regression (MLR) and non-linear radial basis function neural network (RBFNN) were performed to build the models. The statistical characteristics provided by multiple linear model (R2=0.927, RMS=0.073; AARD=6.34% for test set) indicated satisfactory stability and predictive ability, while the predictive ability of RBFNN model is somewhat superior (R2=0.959; RMS=0.0648; AARD=4.85% for test set). This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for quantitative analysis by gas chromatography, and can be useful in predicting the quantitative calibration factors of other compounds.

Determination of copper in coal fly ash in the presence of excess titanium by dynamic reaction cell inductively coupled plasma mass spectrometry.

Inductively coupled plasma quadrupole mass spectrometry (ICP-QMS) was used for the accurate determination of copper in coal fly ash samples in the presence of excess titanium, using the reaction of Cu(+) ions with NH(3) in the cell. The method eliminated the effect of polyatomic isobaric interferences at m/z 63 and 65 caused by the formation of (47)Ti(16)O(+), (49)Ti(16)O(+) and (47)Ti(18)O(+) on (63)Cu(+) and (65)Cu(+) by detecting Cu(+) as the product cluster ion Cu(NH(3))(2)(+). As the signal of (63)Cu(NH(3))(2)(+) overlapped with that of (97)Mo(+) which existed in the samples, (65)Cu(NH(3))(2)(+) was detected at m/z 99. The effect of the operating conditions of DRC system was studied in order to obtain the best signal to noise ratio for Cu(NH(3))(2)(+) at m/z 99. The formation of Cu(NH(3))(2)(+) was through the clustering reaction Cu(+)+2NH(3)-->Cu(NH(3))(2)(+) which resulted in the separation of analyte from the interfering oxide. The detection limit for Cu(NH(3))(2)(+) was 0.015 ng mL(-1) as Cu. The method was applied to the determination of copper in NIST SRM 1633a and 1633b coal fly ash reference materials. The precision between sample replicates was better than 2.0% and the analysis results were in good agreement with the certified values.