Fosfomycin is now widely used to treat methicillin-resistant S. aureus due to its unique antibacterial activity. However, fosfomycin-resistant S. aureus has rapidly emerged, it is urgent to find new treatments to eliminate fosfomycin-resistant S. aureus infection. The purpose of this study was to analyze the activity of cryptanshinone, a traditional Chinese medicine monomer, in combination with fosfomycin against fosfomycin-sensitive S. aureus (FSSA) and fosfomycin-resistant S. aureus (FRSA). Methods: The MICs of fosfomycin and/or cryptanshinone were determined by agar dilution assay and checkerboard microdilution assay. Furthermore, synergistic effects from fosfomycin and/or cryptanshinone were analyzed by the time-kill assay in vitro. Results: The combination of fosfomycin and cryptotanshinone had a synergistic effect on most (71.43%) of the FRSA and had a partial (28.57%) synergistic effect on a small part. In addition, time sterilization curve verified synergistic activity between cryptanshinone and fosfomycin against FSSA and FRSA, especially when fosfomycin was added for a second time. Conclusion: These data suggest that cryptanshinone combined with fosfomycin could be a novel treatment for FRSA and provide a new direction for the treatment of bacterial infections in the future.
Sudden flood disasters cause serious damage to agricultural production. Rapidly extracting information such as the flooding extent of agricultural land and capturing the influence of flooding on crops provides important guidelines for estimating the flood-affected area, promoting post-disaster farmland restoration, and providing an auxiliary decision-making basis for flood prevention and disaster relief departments. Taking the flood event in Henan and Shanxi Provinces as example, based on the characteristics of the variations in radar data and optical data before and after the disaster, we propose an extent information extraction method for the flood inundation area and the flood-affected area of agricultural land. This method consists of change detection, threshold extraction, and superposition analysis, which weakens the negative impact of the radar data speckle noise and cloud contamination of the optical data on the extraction of the agricultural land flooding to a certain extent. The method was developed based on a flood event in Henan Province and validated in Shanxi Province. The results show that the production of this method have a clear boundary and accurate extent, and the overall precisions of the flood inundation area and flood-affected area extraction are 0.87 and 0.92, respectively. The proposed method combines the advantages of both radar and optical remote sensing data in extracting the specific extents of the flood inundation area and the flood-affected area in large spatial scale. Finally, the impact of time window size to the performance of the method is further analyzed. In the application of the proposed method, the Google Earth Engine (GEE) platform provides a low-cost, fast, and convenient way to extract flood information from remote sensing data. The proposed scheme provides a scientific data basis for restoring production of agricultural land after a flood disaster, as well as for national post-disaster damage assessment and disaster relief decision making.
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