High-entropy alloys(HEAs)have attracted considerable attention because of their excellent properties and broad compositional design space.However,traditional trial-and-error methods for screening HEAs are costly and i...High-entropy alloys(HEAs)have attracted considerable attention because of their excellent properties and broad compositional design space.However,traditional trial-and-error methods for screening HEAs are costly and inefficient,thereby limiting the development of new materials.Although density functional theory(DFT),molecular dynamics(MD),and thermodynamic modeling have improved the design efficiency,their indirect connection to properties has led to limitations in calculation and prediction.With the awarding of the Nobel Prize in Physics and Chemistry to artificial intelligence(AI)related researchers,there has been a renewed enthusiasm for the application of machine learning(ML)in the field of alloy materials.In this study,common and advanced ML models and strategies in HEA design were introduced,and the mechanism by which ML can play a role in composition optimization and performance prediction was investigated through case studies.The general workflow of ML application in material design was also introduced from the programmer’s point of view,including data preprocessing,feature engineering,model training,evaluation,optimization,and interpretability.Furthermore,data scarcity,multi-model coupling,and other challenges and opportunities at the current stage were analyzed,and an outlook on future research directions was provided.展开更多
BACKGROUND Herba Patriniae and Coix seed(HC)constitute a widely utilized drug combination in the clinical management of colorectal cancer(CRC)that is known for its diuretic,anti-inflammatory,and swelling-reducing prop...BACKGROUND Herba Patriniae and Coix seed(HC)constitute a widely utilized drug combination in the clinical management of colorectal cancer(CRC)that is known for its diuretic,anti-inflammatory,and swelling-reducing properties.Although its efficacy has been demonstrated in a clinical setting,the active compounds and their mechanisms of action in CRC treatment remain to be fully elucidated.AIM To identify the active,CRC-targeting components of HC and to elucidate the mechanisms of action involved.METHODS Active HC components were identified and screened using databases.Targets for each component were predicted.CRC-related targets were obtained from human gene databases.Interaction targets between HC and CRC were identified.A“drug-ingredient-target”network was created to identify the core components and targets involved.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses were conducted to elucidate the key pathways involved.Molecular docking between core targets and key components was executed.In vitro experiments validated core monomers.RESULTS Nineteen active components of HC were identified,with acacetin as the primary active compound.The predictive analysis identified 454 targets of the active compounds in HC.Intersection mapping with 2685 CRC-related targets yielded 171 intervention targets,including 30 core targets.GO and KEGG analyses indicated that HC may influence the phosphoinositide 3-kinase(PI3K)/Akt signaling pathway.Molecular docking showed that acacetin exhibited an optimal interaction with AKT1,identifying PI3K,AKT,and P53 as key genes likely targeted by HC during CRC treatment.Acacetin inhibited HT-29 cell proliferation and migration,as well as promoted apoptosis,in vitro.Western blotting analysis revealed increased p53 and cleaved caspase-3 expression and decreased levels of p-PI3K,p-Akt,and survivin,which likely contributed to CRC apoptosis.CONCLUSION Acacetin,the principal active compound in the HC pair,inhibited the proliferation and migration of HT-29 cells and promoted apoptosis through the PI3K/Akt/p53 signaling pathway.展开更多
基金the National Natural Science Foundation of China(52161011)the Central Guiding Local Science and Technology Development Fund Project(Guike ZY23055005,Guike ZY24212036 and GuikeAB25069457)+5 种基金the Guangxi Science and Technology Project(2023GXNSFDA026046 and Guike AB24010247)the Scientifc Research and Technology Development Program of Guilin(20220110-3 and 20230110-3)the Scientifc Research and Technology Development Program of Nanning Jiangnan district(20230715-02)the Guangxi Key Laboratory of Superhard Material(2022-K-001)the Guangxi Key Laboratory of Information Materials(231003-Z,231033-K and 231013-Z)the Innovation Project of GUET Graduate Education(2025YCXS177)for the fnancial support given to this work.
文摘High-entropy alloys(HEAs)have attracted considerable attention because of their excellent properties and broad compositional design space.However,traditional trial-and-error methods for screening HEAs are costly and inefficient,thereby limiting the development of new materials.Although density functional theory(DFT),molecular dynamics(MD),and thermodynamic modeling have improved the design efficiency,their indirect connection to properties has led to limitations in calculation and prediction.With the awarding of the Nobel Prize in Physics and Chemistry to artificial intelligence(AI)related researchers,there has been a renewed enthusiasm for the application of machine learning(ML)in the field of alloy materials.In this study,common and advanced ML models and strategies in HEA design were introduced,and the mechanism by which ML can play a role in composition optimization and performance prediction was investigated through case studies.The general workflow of ML application in material design was also introduced from the programmer’s point of view,including data preprocessing,feature engineering,model training,evaluation,optimization,and interpretability.Furthermore,data scarcity,multi-model coupling,and other challenges and opportunities at the current stage were analyzed,and an outlook on future research directions was provided.
基金Supported by the National Natural Science Foundation of China,No.82174464China Academy of Traditional Chinese Medicine Science and Technology Innovation Project,No.CI2021A01806Central High Level Traditional Chinese Medicine Hospital Clinical Research and Achievement Transformation Ability Enhancement Project,No.HLCMHPP2023085.
文摘BACKGROUND Herba Patriniae and Coix seed(HC)constitute a widely utilized drug combination in the clinical management of colorectal cancer(CRC)that is known for its diuretic,anti-inflammatory,and swelling-reducing properties.Although its efficacy has been demonstrated in a clinical setting,the active compounds and their mechanisms of action in CRC treatment remain to be fully elucidated.AIM To identify the active,CRC-targeting components of HC and to elucidate the mechanisms of action involved.METHODS Active HC components were identified and screened using databases.Targets for each component were predicted.CRC-related targets were obtained from human gene databases.Interaction targets between HC and CRC were identified.A“drug-ingredient-target”network was created to identify the core components and targets involved.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses were conducted to elucidate the key pathways involved.Molecular docking between core targets and key components was executed.In vitro experiments validated core monomers.RESULTS Nineteen active components of HC were identified,with acacetin as the primary active compound.The predictive analysis identified 454 targets of the active compounds in HC.Intersection mapping with 2685 CRC-related targets yielded 171 intervention targets,including 30 core targets.GO and KEGG analyses indicated that HC may influence the phosphoinositide 3-kinase(PI3K)/Akt signaling pathway.Molecular docking showed that acacetin exhibited an optimal interaction with AKT1,identifying PI3K,AKT,and P53 as key genes likely targeted by HC during CRC treatment.Acacetin inhibited HT-29 cell proliferation and migration,as well as promoted apoptosis,in vitro.Western blotting analysis revealed increased p53 and cleaved caspase-3 expression and decreased levels of p-PI3K,p-Akt,and survivin,which likely contributed to CRC apoptosis.CONCLUSION Acacetin,the principal active compound in the HC pair,inhibited the proliferation and migration of HT-29 cells and promoted apoptosis through the PI3K/Akt/p53 signaling pathway.
