[Objectives]To study the acute toxicity of total flavonoids in Penthorum chinense Pursh. and the therapeutic effect on AFL( Alcoholic Fatty Liver). [Methods] The liquid of total flavonoids in P. chinense Pursh. was in...[Objectives]To study the acute toxicity of total flavonoids in Penthorum chinense Pursh. and the therapeutic effect on AFL( Alcoholic Fatty Liver). [Methods] The liquid of total flavonoids in P. chinense Pursh. was intragastrically administered to the test group rats in the maximum concentration and the maximum administration volume,an equal volume of solvent was given to the control group,and it was observed continuously for 14 d; 1. 5% ferrous sulfate feed was used for feeding,the alcohol intragastric administration method was used to copy the AFL rats model,and the therapeutic effect of total flavonoids in P. chinense Pursh. on the fatty liver rats was observed. [Results]No rat died in the medication administration group and the control group,there was no acute toxicity reaction,and the maximum tolerance dose of total flavonoids in P. chinense Pursh. for the rats by intragastric administration was 33. 6 g/kg; rats suffered AFL 6 weeks after the alcohol intragastric administration. For the 800 mg/kg P. chinense Pursh. total flavonoids and 2 000 mg/kg P. chinense Pursh. extract with the same dose as that of the P. chinense Pursh. crude drug,P. chinense Pursh. total flavonoids played a more significant role than P. chinense Pursh. extract in lowering oil red O staining area in AFL rats' liver tissue and reducing the ALT,AST,TC,TG content in AFL rats' serum. [Conclusions]The P. chinense Pursh. total flavonoids had low acute toxicity,and had a greater therapeutic effect on the AFL rats than the P. chinense Pursh.extract.展开更多
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv...Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.展开更多
基金Supported by Luzhou Municipal Government-Luzhou Medical College Joint Project(2013LZLY-K78)Project of Sichuan Provincial Department of Education in 2015[2015-Chuan Jiao Han(2014)794)]Key Project of Southwest Medical University in 2015(2015-9)
文摘[Objectives]To study the acute toxicity of total flavonoids in Penthorum chinense Pursh. and the therapeutic effect on AFL( Alcoholic Fatty Liver). [Methods] The liquid of total flavonoids in P. chinense Pursh. was intragastrically administered to the test group rats in the maximum concentration and the maximum administration volume,an equal volume of solvent was given to the control group,and it was observed continuously for 14 d; 1. 5% ferrous sulfate feed was used for feeding,the alcohol intragastric administration method was used to copy the AFL rats model,and the therapeutic effect of total flavonoids in P. chinense Pursh. on the fatty liver rats was observed. [Results]No rat died in the medication administration group and the control group,there was no acute toxicity reaction,and the maximum tolerance dose of total flavonoids in P. chinense Pursh. for the rats by intragastric administration was 33. 6 g/kg; rats suffered AFL 6 weeks after the alcohol intragastric administration. For the 800 mg/kg P. chinense Pursh. total flavonoids and 2 000 mg/kg P. chinense Pursh. extract with the same dose as that of the P. chinense Pursh. crude drug,P. chinense Pursh. total flavonoids played a more significant role than P. chinense Pursh. extract in lowering oil red O staining area in AFL rats' liver tissue and reducing the ALT,AST,TC,TG content in AFL rats' serum. [Conclusions]The P. chinense Pursh. total flavonoids had low acute toxicity,and had a greater therapeutic effect on the AFL rats than the P. chinense Pursh.extract.
文摘Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.
