Protective effects of the notoginsenoside R1 on acute lung injury by regulating the miR-128-2-5p/Tollip signaling pathway in rats with severe acute pancreatitis.

Notoginsenoside R1 (NG-R1), the extract and the main ingredient of Panax notoginseng, has anti-inflammatory effects and can be used in treating acute lung injury (ALI). In this study, we explored the pulmonary protective effect and the underlying mechanism of the NG-R1 on rats with ALI induced by severe acute pancreatitis (SAP). MiR-128-2-5p, ERK1, Tollip, HMGB1, TLR4, IκB, and NF-κB mRNA expression levels were measured using real-time qPCR, and TLR4, Tollip, HMGB1, IRAK1, MyD88, ERK1, NF-κB65, and P-IκB-α protein expression levels using Western blot. The NF-κB and the TLR4 activities were determined using immunohistochemistry, and TNF-α, IL-6, IL-1ß, and ICAM-1 levels in the bronchoalveolar lavage fluid (BALF) using ELISA. Lung histopathological changes were observed in each group. NG-R1 treatment reduced miR-128-2-5p expression in the lung tissue, increased Tollip expression, inhibited HMGB1, TLR4, TRAF6, IRAK1, MyD88, NF-κB65, and p-IκB-α expression levels, suppressed NF-κB65 and the TLR4 expression levels, reduced MPO activity, reduced TNF-α, IL-1ß, IL-6, and ICAM-1 levels in BALF, and alleviated SAP-induced ALI. NG-R1 can attenuate SAP-induced ALI. The mechanism of action may be due to a decreased expression of miR-128-2-5p, increased activity of the Tollip signaling pathway, decreased activity of HMGB1/TLR4 and ERK1 signaling pathways, and decreased inflammatory response to SAP-induced ALI. Tollip was the regulatory target of miR-128-2-5p.

[Prediction of Cadmium Content in the Leaves of Navel Orange in Heavy Metal Contaminated Soil Using VIS-NIR Reflectance Spectroscopy].

Visual and Near-infrared (VIS-NIR) reflectance spectroscopy had been used widely in monitoring agricultural pollution in recent years, however, it was rarely applied in monitoring the contamination of heavy metal in orchards. In the present paper, Newhall navel orange (Citrus sinensis [L.] Osbeck cv. Newhall) were cultivated in the potted soil contaminated with cadmium (Cd) at different levels, and the spectral reflectance and Cd content in the leaves were measured simultaneously at different growing seasons, which then were used to establish the prediction model by partial least squares regression (PLSR) based on spectral reflectance and by linear regression based on spectral index. The results showed that Cd was more easily transferred to and cumulated in the new leaves, and this phenomenon was more obvious in heavily contaminated soils with Cd. Blue shift in red edge was found in the band of 700-730 nm in the new leaves, however, no such phenomenon was found in the old leaves. The coefficient of determination (R²) of linear regression model based on spectral index was nearly 0. 8, while the PLSR model had a better result in predicting Cd content in the new leaves than the linear regression with R²CV of approximately 0.9. Furthermore, the standard normal variate transformation(SNV) in spectral preprocessing can improve the precision significantly in PLSR model. These results suggest that the VIS-NIR method has a great potential in monitoring heavy metal pollution in the navel orange.

[Predicting soil salinity based on spectral symmetry under wet soil condition].

There has been a growing interest in using spectral reflectance as a rapid and inexpensive tool for soil salinity monitoring in recent years. However, since soil moisture often exerts a tremendous influence on soil reflectance, the monitoring accuracy under various moisture conditions cannot fully satisfy the requirements of agricultural practice. In the present paper, a linear model was built to relate the spectral symmetry in the band of 1 370 - 1 610 nm with the salt content and moisture content of the saline soil based on regularly measured data of reflectance, soil moisture and salt content of the surface of 5 soil columns during the simulated evaporation process in laboratory. The results showed that the model was good with r greater than 0.8. By inversing the model, soil salt content then was predicted after moisture content was determined. The results showed that the prediction accuracy was acceptable with a root mean square error (RMSE) of 2.059 g x kg(-1) and an r of 0.656. The results demonstrated the feasibility of using spectral symmetry to predict soil salt content under various moisture conditions.