In the past decade,immunotherapies targeting cytotoxic T-lymphocyte antigen-4(CTLA-4),programmed cell death 1(PD-1),and PD-1 ligand(PD-L1)have been approved for solid tumors.However,some patients demonstrate suboptima...In the past decade,immunotherapies targeting cytotoxic T-lymphocyte antigen-4(CTLA-4),programmed cell death 1(PD-1),and PD-1 ligand(PD-L1)have been approved for solid tumors.However,some patients demonstrate suboptimal clinical outcomes due to resistance.The tumor microenvironment(TME)significantly affects the efficiency of immunotherapy by mediating interactions between tumor and non-tumor cells,including dendritic cells,T cells,B cells,macrophages,neutrophils,NK cells,and myeloid-derived suppressor cells(MDSCs).These non-tumor cells often exhibit two phenotypes with altered functions,and tumor cells drives their transition towards tumor promotion through tumor-education.Tumor-educated cells(TECs)are cells influenced by tumor cells,which acquire immune-suppressive phenotypes and promote tumor progression through resistance to anticancer therapies.These cells undergo modifications in response to signals from the tumor,which can influence their roles in tumor progression.Their dynamic interactions with tumor cells contribute to the reshaping of the TME,facilitating cancer growth and immune modulation.This review summarizes research on TECs in TME,explores mechanisms related to tumor education,and discusses their role in tumor progression and immunotherapy resistance.Additionally,potential therapeutic approaches targeting these cells are also reviewed,which may complement current treatment strategies.展开更多
Physical and chemical processes observed in the mesosphere and thermosphere above the Earth’s low latitudes are complex and highly interrelated to activity in the low-latitude ionosphere.Metallic sodium detected by l...Physical and chemical processes observed in the mesosphere and thermosphere above the Earth’s low latitudes are complex and highly interrelated to activity in the low-latitude ionosphere.Metallic sodium detected by lidar can yield clues to dynamic and chemical processes in these spatial layers above the Earth’s atmosphere.This paper is based on sodium layer data collected at two low-latitude stations,one in the northern hemisphere and one in the southern.The low-latitude sodium layer exhibits conspicuous seasonal variations in shape,density,and altitude;these variations are similar between Earth’s hemispheres:sodium layer density at both stations reaches its seasonal maximum in autumn and minimum in summer.However,maximal Na density over Brazil is greater than that over Hainan.Nocturnal variations of Na density above the two low-latitude stations are also similar;at both,maxima are observed before sunrise.Some variations of the Na layer over Brazil that differ from those observed in the northern hemisphere may be related to the South Atlantic Magnetic Anomaly(SAMA)or fountain effect.We suggest that low-latitude Na layer data may provide useful additional evidence that could significantly improve the low-latitude part of the WACCM-Na model.展开更多
This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task...This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.展开更多
BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise fore...BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this malignancy.AIM To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.METHODS Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020.Least absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient outcomes.The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves.To evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were performed.RESULTS A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte ratio.The area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,respectively.The AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,respectively.The model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding 0.75.Compared with TNM staging,the model demonstrated superior clinical utility.Ultimately,a nomogram was developed via a web-based interface.CONCLUSION This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrated strong predictive ability.Based on these findings,this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.展开更多
基金supported by the NIH/NCI grants(No.P01CA257907)the Educational Department of Hunan Province for Excellent Youth Scholars(No.23B0011)。
文摘In the past decade,immunotherapies targeting cytotoxic T-lymphocyte antigen-4(CTLA-4),programmed cell death 1(PD-1),and PD-1 ligand(PD-L1)have been approved for solid tumors.However,some patients demonstrate suboptimal clinical outcomes due to resistance.The tumor microenvironment(TME)significantly affects the efficiency of immunotherapy by mediating interactions between tumor and non-tumor cells,including dendritic cells,T cells,B cells,macrophages,neutrophils,NK cells,and myeloid-derived suppressor cells(MDSCs).These non-tumor cells often exhibit two phenotypes with altered functions,and tumor cells drives their transition towards tumor promotion through tumor-education.Tumor-educated cells(TECs)are cells influenced by tumor cells,which acquire immune-suppressive phenotypes and promote tumor progression through resistance to anticancer therapies.These cells undergo modifications in response to signals from the tumor,which can influence their roles in tumor progression.Their dynamic interactions with tumor cells contribute to the reshaping of the TME,facilitating cancer growth and immune modulation.This review summarizes research on TECs in TME,explores mechanisms related to tumor education,and discusses their role in tumor progression and immunotherapy resistance.Additionally,potential therapeutic approaches targeting these cells are also reviewed,which may complement current treatment strategies.
基金supported by the NSFC (42374204, 42004143,42364012)the Project of Stable Support for Youth Team in Basic Research Field,Chinese Academy of Sciences (Grant No.YSBR-018)+3 种基金the Scientific Projects of Hainan Province(KJRC2023C05, ZDYF2021GXJS040)the Innovational Fund for Scientific and Technological Personnel of Hainan Provincethe Chinese Meridian ProjectPandeng Program of National Space Science Center,Chinese Academy of Sciences
文摘Physical and chemical processes observed in the mesosphere and thermosphere above the Earth’s low latitudes are complex and highly interrelated to activity in the low-latitude ionosphere.Metallic sodium detected by lidar can yield clues to dynamic and chemical processes in these spatial layers above the Earth’s atmosphere.This paper is based on sodium layer data collected at two low-latitude stations,one in the northern hemisphere and one in the southern.The low-latitude sodium layer exhibits conspicuous seasonal variations in shape,density,and altitude;these variations are similar between Earth’s hemispheres:sodium layer density at both stations reaches its seasonal maximum in autumn and minimum in summer.However,maximal Na density over Brazil is greater than that over Hainan.Nocturnal variations of Na density above the two low-latitude stations are also similar;at both,maxima are observed before sunrise.Some variations of the Na layer over Brazil that differ from those observed in the northern hemisphere may be related to the South Atlantic Magnetic Anomaly(SAMA)or fountain effect.We suggest that low-latitude Na layer data may provide useful additional evidence that could significantly improve the low-latitude part of the WACCM-Na model.
基金The National Natural Science Foundation of China(62203015,62233001,62273351)The Beijing Natural Science Foundation(4242038)。
文摘This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.
文摘BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this malignancy.AIM To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.METHODS Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020.Least absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient outcomes.The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves.To evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were performed.RESULTS A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte ratio.The area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,respectively.The AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,respectively.The model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding 0.75.Compared with TNM staging,the model demonstrated superior clinical utility.Ultimately,a nomogram was developed via a web-based interface.CONCLUSION This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrated strong predictive ability.Based on these findings,this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.
