Functional metabolomics characterizes the contribution of farnesoid X receptor in pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome.

Consumption of herbal products containing pyrrolizidine alkaloids (PAs) is one of the major causes for hepatic sinusoidal obstruction syndrome (HSOS), a deadly liver disease. However, the crucial metabolic variation and biomarkers which can reflect these changes remain amphibious and thus to result in a lack of effective prevention, diagnosis and treatments against this disease. The aim of the study was to determine the impact of HSOS caused by PA exposure, and to translate metabolomics-derived biomarkers to the mechanism. In present study, cholic acid species (namely, cholic acid, taurine conjugated-cholic acid, and glycine conjugated-cholic acid) were identified as the candidate biomarkers (area under the ROC curve 0.968 [95% CI 0.908-0.994], sensitivity 83.87%, specificity 96.55%) for PA-HSOS using two independent cohorts of patients with PA-HSOS. The increased primary bile acid biosynthesis and decreased liver expression of farnesoid X receptor (FXR, which is known to inhibit bile acid biosynthesis in hepatocytes) were highlighted in PA-HSOS patients. Furtherly, a murine PA-HSOS model induced by senecionine (50 mg/kg, p.o.), a hepatotoxic PA, showed increased biosynthesis of cholic acid species via inhibition of hepatic FXR-SHP singling and treatment with the FXR agonist obeticholic acid restored the cholic acid species to the normal levels and protected mice from senecionine-induced HSOS. This work elucidates that increased levels of cholic acid species can serve as diagnostic biomarkers in PA-HSOS and targeting FXR may represent a therapeutic strategy for treating PA-HSOS in clinics.

Study on the prediction method of grasshopper occurrence risk in Inner Mongolia based on the maximum entropy model during the growing period.

Grasshoppers represent a significant biological challenge in Inner Mongolia's grasslands, severely affecting the region's animal husbandry. Thus, dynamic monitoring of grasshopper infestation risk is crucial for sustainable livestock farming. This study employed the Maxent model, along with remote sensing data, to forecast Oedaleus decorus asiaticus occurrence during the growing season, using grasshopper suitability habitats as a base. The Maxent model's predictive accuracy was high, with an AUC of 0.966. The most influential environmental variables for grasshopper distribution were suitable habitat data (34.27%), the temperature-vegetation dryness index during the spawning period (18.81%), and various other meteorological and vegetation factors. The risk index model was applied to calculate the grasshopper distribution across different risk levels for the years 2019-2022. The data indicated that the level 1 risk area primarily spans central, eastern, and southwestern Inner Mongolia. By examining the variable weights, the primary drivers of risk level fluctuation from 2019 to 2022 were identified as accumulated precipitation and land surface temperature anomalies during the overwintering period. This study offers valuable insights for future O. decorus asiaticus monitoring in Inner Mongolia.

Combining the Optimized Maximum Entropy Model to Detect Key Factors in the Occurrence of Oedaleus decorus asiaticus in the Typical Grasslands of Central and Eastern Inner Mongolia.

Grasshoppers pose a significant threat to both natural grassland vegetation and crops. Therefore, comprehending the relationship between environmental factors and grasshopper occurrence is of paramount importance. This study integrated machine learning models (Maxent) using the kuenm package to screen MaxEnt models for grasshopper species selection, while simultaneously fitting remote sensing data of major grasshopper breeding areas in Inner Mongolia, China. It investigated the spatial distribution and key factors influencing the occurrence of typical grasshopper species in grassland ecosystems. The modelling results indicate that a typical steppe has a larger suitable area. The soil type, above biomass, altitude, and temperature, predominantly determine the grasshopper occurrence in typical steppes. This study explicitly delineates the disparate impacts of key environmental factors (meteorology, vegetation, soil, and topography) on grasshopper occurrence in typical steppes. Furthermore, it provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be instrumental in formulating future management measures to guarantee grass ecological environment security and the sustainable development of grassland.