Objective Arsenic trioxide(ATO)is a clinically effective anticancer agent used in the treatment of leukemia.However,it exerts adverse effects on non-tumor cells,including bone marrow mesenchymal stem cells(BMSCs).This...Objective Arsenic trioxide(ATO)is a clinically effective anticancer agent used in the treatment of leukemia.However,it exerts adverse effects on non-tumor cells,including bone marrow mesenchymal stem cells(BMSCs).This study aims to investigate the protective role and molecular mechanism of traditional Chinese medicine Astragalus polysaccharides(APS)in mitigating ATO-induced apoptosis in BMSCs.Methods BMSCs exposed to ATO(0.5μmol/L)were treated with APS(20,40,100,and 200μg/mL).Cell viability,proliferation,and migration were assessed by using MTT,EdU staining,Transwell,and scratch wound healing assays.Apoptosis was evaluated via TUNEL assay,Hoechst 33258 staining,and flow cytometry.Intracellular reactive oxygen species(ROS)and mitochondrial membrane potential were measured by using DCFH-DA and JC-1 staining.Apoptotic protein expression was analyzed by Western blotting.Results ATO exposure significantly inhibited the proliferation and migration of BMSCs and induced apoptosis,while APS markedly attenuated the apoptosis of BMSCs induced by ATO,and significantly improved cell proliferation and migration(P<0.01).Mechanistically,APS effectively reduced ATO-induced ROS(P<0.01),while the protein expression of Bcl-2-associated X protein(Bax)and cleaved Caspase-3 was significantly decreased(P<0.05),and the protein expression of Bcl-2 was significantly increased(P<0.01).In addition,APS markedly decreased the protein expression of c-Jun N-terminal kinase(Jnk)and p38 in ATO-activated BMSCs(P<0.05),and significantly decreased the protein expression of p16 and p53(P<0.01),and increased the protein expression of phosphorylated protein kinase B(p-Akt)and phosphorylated extracellular signal-regulated kinase(p-Erk)(P<0.01,0.05).Conclusion Our study reveals that APS exert significant protective effects against ATO-induced apoptosis in BMSCs.The mechanisms involve suppressing ROS generation,maintaining mitochondrial membrane stability,enhancing cell viability,migration,and proliferation,as well as inhibiting Jnk and p38 mitogen-activated protein kinase(p38 MAPK)signaling pathways.The findings highlight potential molecular targets and novel strategies for the clinical prevention and treatment of ATO-related toxicity.展开更多
In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world da...In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.展开更多
基金supported by the National Natural Science Foundation of China(No.82473925,81800784,81972117,82472477)the Young Elite Scientists Sponsorship Program by CAST(No.2019QNRC001)+8 种基金the Postdoctoral Science Foundation of China(No.2018M630370)the Key Research and Development Program of Heilongjiang Province(No.GA21C006)the Heilongjiang Postdoctoral Fund for Young Talent Program(No.LBH-TZ1020)the Natural Science Foundation of Heilongjiang Province(No.YQ2020H003)the Research Institution’s Scientific Research Expenses Project of Heilongjiang Province(No.CZKYF2022-1-C006)the Excellent Young Talents Funding(No.2019-QD-11)the CAMS Innovation Fund for Medical Sciences(No.CIFMS 2020-I2M-5-003)the Natural Science Foundation of Jiangsu Province(No.BK20210110)the Program of Science and Technology of Suzhou(No.SYG202119).
文摘Objective Arsenic trioxide(ATO)is a clinically effective anticancer agent used in the treatment of leukemia.However,it exerts adverse effects on non-tumor cells,including bone marrow mesenchymal stem cells(BMSCs).This study aims to investigate the protective role and molecular mechanism of traditional Chinese medicine Astragalus polysaccharides(APS)in mitigating ATO-induced apoptosis in BMSCs.Methods BMSCs exposed to ATO(0.5μmol/L)were treated with APS(20,40,100,and 200μg/mL).Cell viability,proliferation,and migration were assessed by using MTT,EdU staining,Transwell,and scratch wound healing assays.Apoptosis was evaluated via TUNEL assay,Hoechst 33258 staining,and flow cytometry.Intracellular reactive oxygen species(ROS)and mitochondrial membrane potential were measured by using DCFH-DA and JC-1 staining.Apoptotic protein expression was analyzed by Western blotting.Results ATO exposure significantly inhibited the proliferation and migration of BMSCs and induced apoptosis,while APS markedly attenuated the apoptosis of BMSCs induced by ATO,and significantly improved cell proliferation and migration(P<0.01).Mechanistically,APS effectively reduced ATO-induced ROS(P<0.01),while the protein expression of Bcl-2-associated X protein(Bax)and cleaved Caspase-3 was significantly decreased(P<0.05),and the protein expression of Bcl-2 was significantly increased(P<0.01).In addition,APS markedly decreased the protein expression of c-Jun N-terminal kinase(Jnk)and p38 in ATO-activated BMSCs(P<0.05),and significantly decreased the protein expression of p16 and p53(P<0.01),and increased the protein expression of phosphorylated protein kinase B(p-Akt)and phosphorylated extracellular signal-regulated kinase(p-Erk)(P<0.01,0.05).Conclusion Our study reveals that APS exert significant protective effects against ATO-induced apoptosis in BMSCs.The mechanisms involve suppressing ROS generation,maintaining mitochondrial membrane stability,enhancing cell viability,migration,and proliferation,as well as inhibiting Jnk and p38 mitogen-activated protein kinase(p38 MAPK)signaling pathways.The findings highlight potential molecular targets and novel strategies for the clinical prevention and treatment of ATO-related toxicity.
基金supported in part by the National Natural Science Foundation of China under Grant 62171203in part by the Jiangsu Province“333 Project”High-Level Talent Cultivation Subsidized Project+2 种基金in part by the SuzhouKey Supporting Subjects for Health Informatics under Grant SZFCXK202147in part by the Changshu Science and Technology Program under Grants CS202015 and CS202246in part by Changshu Key Laboratory of Medical Artificial Intelligence and Big Data under Grants CYZ202301 and CS202314.
文摘In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.
