Antimicrobial resistance is a major public health concern.Many drug development strategies focus on metal complexes because they have different inhibitory mechanisms and are less prone to drug resistance than traditio...Antimicrobial resistance is a major public health concern.Many drug development strategies focus on metal complexes because they have different inhibitory mechanisms and are less prone to drug resistance than traditional antibiotics.In this study,we investigated the antibacterial properties of Radix scutellariae(Huangqin),a traditional Chinese medicine.Using acid methanol solutions,we identified metal complexes in aqueous extracts of Huangqin,in which baicalin,oroxindin,and scutellarin function as ligands.Mn^(2+)increased the antibacterial activity of the aqueous extract through metal ion addition.We investigated the mechanism and structure of the baicalin-manganese complex(BCM)obtained by hydrothermal synthesis.Reaction products containing BCM at a ratio of 1:1 and 2:1 were significantly more effective against bacteria than baicalin alone.The antibacterial activity of the BCM against Staphylococcus aureus was eight times higher than that of baicalin.The antibacterial mechanisms included altering the morphological structure of bacteria,disrupting the integrity of their cell membrane and wall,and causing the cells to produce large amounts of reactive oxygen species.Importantly,after continuous cultivation of S.aureus for 20 generations in drug-containing cultures,the minimum inhibitory concentrations(MIC)of amikacin,azithromycin,and clindamycin were 32,eight,and four times greater than those in the first generation,respectively,whereas the MIC of BCM was maintained.BCM could reverse S.aureus resistance to amikacin and azithromycin.In conclusion,during the decoction of Huangqin,the organic components form complexes with metal ions,producing compounds with good antibacterial activity and a low tendency to cause resistance.展开更多
Physics-informed neural networks(PINNs)have emerged as powerful tools for data-driven solutions of partial differential equations.However,when solving complex solutions with a sharp gradient or waveform mutation regio...Physics-informed neural networks(PINNs)have emerged as powerful tools for data-driven solutions of partial differential equations.However,when solving complex solutions with a sharp gradient or waveform mutation region,the traditional PINNs method frequently has significantly higher prediction errors in critical regions than in other areas because of randomly or uniformly distributed sampling points.To overcome the limitations of PINNs in solving complex solutions,we propose a high-residual region resampling PINN(HRR-PINN)method.The HRR-PINN method uses a two-stage paradigm.Pre-training focuses on global modeling to obtain the residual of the network training,which is helpful for achieving a more precise sampling optimization.Secondary training focuses on computational resources of critical regions to specialize in optimizing high-residual regions based on the global results of pretraining,that is,adding new points in high-residual regions and removing low-residual points from the original set.To illustrate the effectiveness of the HRR-PINN method,we applied it to single-periodic solutions,rogue wave solutions on single-periodic backgrounds,and double-periodic solutions of the second-type derivative nonlinear Schr¨odinger equation.Numerical experiments show that the HRR-PINN method significantly optimizes the distribution of sampling points and reduces prediction errors.This confirms the effectiveness for solving complex solutions with abrupt waveform changes or sharp gradients of the HRR-PINN method.展开更多
基金supported by the Natural Science Foundation of Jiangxi Province[grant number:20212BAB206005]the Science and Technology research project of the Education Department of Jiangxi Province[grant number:GJJ211119]the Open Project of Engineering Center of Jiangxi University for Fine Chemicals[grant number:JFCEC-KF-2102].
文摘Antimicrobial resistance is a major public health concern.Many drug development strategies focus on metal complexes because they have different inhibitory mechanisms and are less prone to drug resistance than traditional antibiotics.In this study,we investigated the antibacterial properties of Radix scutellariae(Huangqin),a traditional Chinese medicine.Using acid methanol solutions,we identified metal complexes in aqueous extracts of Huangqin,in which baicalin,oroxindin,and scutellarin function as ligands.Mn^(2+)increased the antibacterial activity of the aqueous extract through metal ion addition.We investigated the mechanism and structure of the baicalin-manganese complex(BCM)obtained by hydrothermal synthesis.Reaction products containing BCM at a ratio of 1:1 and 2:1 were significantly more effective against bacteria than baicalin alone.The antibacterial activity of the BCM against Staphylococcus aureus was eight times higher than that of baicalin.The antibacterial mechanisms included altering the morphological structure of bacteria,disrupting the integrity of their cell membrane and wall,and causing the cells to produce large amounts of reactive oxygen species.Importantly,after continuous cultivation of S.aureus for 20 generations in drug-containing cultures,the minimum inhibitory concentrations(MIC)of amikacin,azithromycin,and clindamycin were 32,eight,and four times greater than those in the first generation,respectively,whereas the MIC of BCM was maintained.BCM could reverse S.aureus resistance to amikacin and azithromycin.In conclusion,during the decoction of Huangqin,the organic components form complexes with metal ions,producing compounds with good antibacterial activity and a low tendency to cause resistance.
基金supported by the National Natural Science Foundation of China(Grant Nos.12547136,12575002,and 12235007).
文摘Physics-informed neural networks(PINNs)have emerged as powerful tools for data-driven solutions of partial differential equations.However,when solving complex solutions with a sharp gradient or waveform mutation region,the traditional PINNs method frequently has significantly higher prediction errors in critical regions than in other areas because of randomly or uniformly distributed sampling points.To overcome the limitations of PINNs in solving complex solutions,we propose a high-residual region resampling PINN(HRR-PINN)method.The HRR-PINN method uses a two-stage paradigm.Pre-training focuses on global modeling to obtain the residual of the network training,which is helpful for achieving a more precise sampling optimization.Secondary training focuses on computational resources of critical regions to specialize in optimizing high-residual regions based on the global results of pretraining,that is,adding new points in high-residual regions and removing low-residual points from the original set.To illustrate the effectiveness of the HRR-PINN method,we applied it to single-periodic solutions,rogue wave solutions on single-periodic backgrounds,and double-periodic solutions of the second-type derivative nonlinear Schr¨odinger equation.Numerical experiments show that the HRR-PINN method significantly optimizes the distribution of sampling points and reduces prediction errors.This confirms the effectiveness for solving complex solutions with abrupt waveform changes or sharp gradients of the HRR-PINN method.
