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Flos Carthami (FC) has a good therapeutic effect on chronic alcoholic liver injury (CALI) in clinical practice, but the treatment mechanism is not very clear. Therefore, elucidating the molecular mechanism of action of FC in treating CALI is of great significance for the further development and application of drugs. Male Wistar rats were as the research object. The model group rats were orally administered with 8 mL/kg alcohol for 28 days to establish a CALI model, while the treatment group rats were orally administered with high (4.2903 g/kg), medium (1.4301 g/kg), and low (0.4767 g/kg) doses of FC extract. Potential biomarkers related to CALI were identified using rat serum metabonomics analysis methods combined with ultra-high performance liquid chromatography mass spectrometry (UHPLC-MS) technology, and the regulatory mechanisms of FC on these biomarkers were investigated. The BP neural network model was established by MATLAB software to deal with the classification problem of omics data. The H&E staining experiment found that after gavage, high-dose FC extract can reduce the degree of liver cell damage. Compared with the model group, the expression levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in the high-dose FC group decrease, indicating that the high-dose FC extract has liver protective effect. The classification accuracy of the BP neural network model is 95.8%, and the classification effect is good. Through volcanic map analysis, a total of twenty biomarkers related to CALI are identified, and FC can have a callback effect on these biomarkers. The results indicated that FC may exert therapeutic effect on CALI by regulating the metabolism of triglycerides, fatty acids, phospholipids, bile acids, amino acids and Vitamin E. This study provides a theoretical foundation for the promotion and clinical application.

Abstract Background Although stem cell transplantation is a promising approach for the treatment of myocardial infarction (MI), there are still some problems faced such as the low survival rate of stem cells. Here, we investigated the role of Notoginsenoside R1 (NGR1) pretreatment in improving the effects of neonatal rat bone marrow mesenchymal stem cell (MSC) transplantation for treatment of MI. Methods Cardiac functions were detected by echocardiography and the myocardial infarct size was determined by Masson’s trichrome staining in a rat model of MI. The cardioprotective effects of NGR1/LY294002 co-pretreated MSCs was evaluated to explore the underlying mechanism. The angiogenesis was determined by vWF and α-SMA immunofluorescence staining and cell apoptosis was detected by TUNEL. In vitro, the effects of NGR1 on stem cell proliferation was examined by CCK-8 and levels of P-Akt, P-CREB, P-FoxO1 were detected by western blot. Apoptosis, ROS content, and cytokine levels were examined by DAPI and TUNEL staining, a ROS assay kit, and ELISA, respectively. Results NGR1 elevated the therapeutic effect of MSC transplantation on infarction by preserving cardiac function, increasing angiogenesis and expressions of IGF-1, VEGF, and SDF-1, and reducing cell apoptosis, whereas the addition of LY294002 prior to NGR1 treatment significantly counteracted the foregoing effects of NGR1. NGR1 pretreatment and SC79 pretreatment were similar in that both significantly increased P-Akt and P-FoxO1 levels in MSC and did not affect P-CREB levels. Besides, both NGR1 and SC79 promoted VEGF, SCF and bFGF levels in MSC cultures, and significantly reduced ROS accumulation and the attenuated cell apoptosis in MSC triggered by H2O2. Similarly, addition of LY294002 before NGR1 treatment significantly counteracted the aforementioned effects of NGR1 in vitro. Conclusions NGR1 pretreatment enhances the effect of MSC transplantation for treatment of MI through paracrine signaling, and the mechanism underlying this effect may be associated with PI3K/Akt/FoxO1 signaling pathways.

Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD (Surface Radiation Budget Network) sites, 6 ARM (Atmospheric Radiation Measurement) sites, and 9 NDBC (National Data Buoy Center) sites. The results indicate that a relatively consistent performance among Landsat 5, 7, and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity (LSE) correction methods for Landsat 5, 7, and 8 sensors. Large bias and root mean square error (RMSE) of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index (NDVI). Except for the sites with incorrect LSE estimation, a mean bias (RMSE) of the differences between Landsat LST and in situ LST is 1.0 K (2.1 K) over snow-free land surfaces, −1.1 K (1.6 K) over snow surfaces, and −0.3 K (1.1 K) over water surfaces.

Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.

The authors would like to correct an error in the title paper [...]