Host-yeast interactions are fundamental drivers of human microbiome dynamics,spanning a spectrum from mutualistic symbiosis to opportunistic pathogenesis with profound implications for systemic health.This review syst...Host-yeast interactions are fundamental drivers of human microbiome dynamics,spanning a spectrum from mutualistic symbiosis to opportunistic pathogenesis with profound implications for systemic health.This review systematically elucidates the complex molecular mechanisms governing these relationships,with a specific focus on metabolic interdependence and immunomodulation.We analyze how yeast-derived metabolites,particularly short-chain fatty acids(SCFAs),modulate host glucose and lipid homeostasis via signaling pathways such as GPR41/43 and GLP-1 secretion.Furthermore,the review explores the pathophysiological role of fungal dysbiosis in chronic conditions,including obesity,diabetes,and inflammatory bowel disease(IBD),highlighting how a breakdown in host-yeast homeostasis triggers pro-inflammatory cascades.Beyond the fungal-host axis,we introduce the concept of the"mycobiome-virome-bacterial axis,"discussing how commensal yeasts synergize with beneficial bacteria like Bifidobacterium and influence viral infectivity through Interferon-mediated innate immune priming.We critically evaluate how cutting-edge technologies-including transgenic mouse models(specifically Dectin-1^(-/-)and CARD9^(-/-),metabolomics,and single-cell sequencing-have revolutionized our mechanistic understanding of these multi-kingdom dynamics.By integrating current findings,we identify critical knowledge gaps and propose high-resolution research frameworks,such as humanized organ-on-a-chip systems,to simulate intricate host-microbe interactions under physiological flow conditions.This comprehensive synthesis provides a strategic foundation for developing targeted,next-generation microbiome-based interventions to restore host-yeast balance and enhance overall human health.展开更多
Pancreatic ductal adenocarcinoma(PDAC)remains one of the most lethal malignancies,characterized by a highly immunosuppressive tumor microenvironment(TME),dense stromal architecture,and limited response to conventional...Pancreatic ductal adenocarcinoma(PDAC)remains one of the most lethal malignancies,characterized by a highly immunosuppressive tumor microenvironment(TME),dense stromal architecture,and limited response to conventional therapies.This review comprehensively examines the emerging role of chimeric antigen receptor(CAR)-engineered immune cells,including chimeric antigen receptor-T(CAR-T),CAR-macrophages(CAR-M),and CAR-natural killer(CAR-NK)cells,as innovative immunotherapeutic strategies for PDAC.We delve into the mechanistic foundations of these platforms,highlighting their unique abilities to target tumor-associated antigens,overcome stromal barriers,and remodel the immunosuppressive TME.Recent preclinical and clinical advances demonstrate promising antitumor activity,particularly with targets such as mesothelin,claudin18.2,and human epidermal growth factor 2(HER2),though challenges related to antigen heterogeneity,TME suppression,and cell persistence remain.We further discuss synergistic approaches involving genetic engineering,microenvironment modulation,and combination therapies aimed at enhancing efficacy.Finally,we offer perspectives on the future direction of CARbased therapies,including the development of next-generation constructs,allogeneic“off-the-shelf”products,and personalized combination regimens,underscoring their potential in pancreatic cancer.展开更多
The monofloral honey derived from Bauhinia championii(Benth.)Benth.(MH-Bc)possesses significant nutritional and bioactive value,making it highly suitable for commercial exploitation.However,the poorly defined characte...The monofloral honey derived from Bauhinia championii(Benth.)Benth.(MH-Bc)possesses significant nutritional and bioactive value,making it highly suitable for commercial exploitation.However,the poorly defined characteristics and unknown composition have hindered MH-Bc product development.In this study,we employed a combination of untargeted and targeted mass spectrometry analyses to characterize MH-Bc honey.As a result,4,7,8-trimethoxydibenzo[b,d]furan-3-ol(TDBF)was identified as a robust chemical marker for distinguishing MH-Bc from other types of honey.This specific marker was detected in both MH-Bc and the Bc plant but was absent in other honey varieties.Furthermore,a targeted mass spectrometry quantitative method was developed and validated to accurately determine the content of TDBF in honey samples.Overall,the presence of TDBF serves as a discerning indicator for future commercial MH-Bc products.展开更多
In this paper,the dynamics of a Leslie-Gower predator-prey model with weak nonlin-ear harvesting and prey-taxis is discussed.By comparing and analyzing the differences between ordinary differential systems and chemota...In this paper,the dynamics of a Leslie-Gower predator-prey model with weak nonlin-ear harvesting and prey-taxis is discussed.By comparing and analyzing the differences between ordinary differential systems and chemotaxis systems in the stability of equilibrium points,the influence mechanism of chemotaxis on the dynamic behavior of the system is deeply studied.Theoretical analysis shows that chemotaxis significantly changes the stability characteristics of the system,and the reliability of theoretical results is further verified by numerical simulation.展开更多
Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific stai...Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific staining procedures involving carcinogenic chemicals.This study proposes an interdisciplinary approach merging materials science,medicine,and artificial intelligence(AI)to develop a virtual staining technique and intelligent evaluation model based on deep learning for chronic wound tissue pathology.This innovation aims to enhance clinical diagnosis and treatment by offering personalized AI-driven therapeutic strategies.By establishing a mouse model of chronic wounds and using a series of hydrogel wound dressings,tissue pathology sections were periodically collected for manual staining and healing assessment.We focused on leveraging the pix2pix image translation framework within deep learning networks.Through CNN models implemented in Python using PyTorch,our study involves learning and feature extraction for region segmentation of pathological slides.Comparative analysis between virtual staining and manual staining results,along with healing diagnosis conclusions,aims to optimize AI models.Ultimately,this approach integrates new metrics such as image recognition,quantitative analysis,and digital diagnostics to formulate an intelligent wound assessment model,facilitating smart monitoring and personalized treatment of wounds.In blind evaluation by pathologists,minimal disparities were found between virtual and conventional histologically stained images of murine wound tissue.The evaluation used pathologists’average scores on real stained images as a benchmark.The scores for virtual stained images were 71.1%for cellular features,75.4%for tissue structures,and 77.8%for overall assessment.Metrics such as PSNR(20.265)and SSIM(0.634)demonstrated our algorithms’superior performance over existing networks.Eight pathological features such as epidermis,hair follicles,and granulation tissue can be accurately identified,and the images were found to be more faithful to the actual tissue feature distribution when compared to manually annotated data.展开更多
融合子图学习与联邦学习后,联邦子图学习在保护数据隐私的同时可实现多客户端子图信息之间的协同学习.然而,由于不同客户端的数据收集方式存在差异,图数据通常呈现非独立同分布特性.同时,不同客户端局部图数据的结构和特征也存在较大差...融合子图学习与联邦学习后,联邦子图学习在保护数据隐私的同时可实现多客户端子图信息之间的协同学习.然而,由于不同客户端的数据收集方式存在差异,图数据通常呈现非独立同分布特性.同时,不同客户端局部图数据的结构和特征也存在较大差异.这些因素导致联邦子图学习在训练过程中出现收敛困难和泛化能较差等问题.为了解决此问题,文中提出基于嵌入对齐与参数激活的个性化联邦子图学习方法(Personalized Federated Subgraph Learning with Embedding Alignment and Parameter Activation,FSL-EAPA).首先,根据客户端之间的相似性进行个性化模型聚合,降低数据非独立同分布对整体性能的影响.然后,引入参数选择性激活进行模型更新,应对子图结构特征的异质性.最后,利用更新后的客户端为各本地节点嵌入提供正负聚类表示,聚集同类局部节点.因此,FSL-EAPA能充分学习各节点的特征表示,较好地适应不同客户端之间的差异化数据分布.在真实基准图数据集上的实验表明FSL-EAPA的有效性,并且在不同场景下都能获得较高的分类精度.展开更多
文摘作为一种新兴的计算范式,移动边缘计算(Mobile Edge Computing, MEC)为交通流量预测提供了新的解决思路以更好支持智能交通系统.面对实际MEC环境中动态变化的交通流量,现有方法无法有效捕获交通流量中复杂时空依赖关系以实现精准预测.此外,由于边缘服务器资源有限,往往无法及时处理海量交通流量数据,难以满足智能交通系统对实时性的高要求.为解决这些重要挑战,本文提出了一种新颖的边缘环境下基于时空特征融合Transformer的交通流量预测(Traffic Flow Prediction based on Transformer with spatio-temporal feature fusion, TFPformer)方法.首先,对原始交通流量数据进行特征嵌入和编码.接着,设计多头卷积低秩分解注意力机制以捕获长期时间依赖关系和获取局部上下文信息.随后,设计注意力图卷积以捕获空间依赖关系.最后,通过门控单元对时空特征进行自适应融合,进而利用前馈神经网络和线性层实现对未来交通流量的精准预测.基于真实的交通流量数据集,通过大量实验全面评估与验证了所提出TFPformer方法的有效性.相比于基准方法,TFPformer方法在不同数据集上均展现出了更加优越的预测精度和效率.
基金funded by 2023 Chongqing medical scientific research project(Joint project of Chongqing Health Commission and Science and Technology Bureaugrant no.2023GGXM006)+12 种基金oint project of Chongqing Health Commission and Science and Technology Bureau(Joint Key Laboratory Open Project)(No.2026KFXM051)Natural Science Foundation of Chongqing(No.CSTB2025NSCO-GPX1116)2026 Chongqing Municipal Health Commission Traditional Chinese Medicine Research Project(No.2026WSJK158),Technological Innovation Project of Shapingba District,Chongqing(No.2025016)2024 Scientific research project of Chongqing Medical and Pharmaceutical College(No.ygzrc2024101)Chongqing Municipal Education Commission Youth Project(No.KJQN202402821No.KJQN202502819)2024 Chongqing Medical and Pharmaceutical College Innovation Research Group Project(No.ygz2024401)Science and Health Joint Medical Research Project of Shapingba District,Chongqing(No.2024SQKWLHMS051)2025 Scientific research project of Chongqing Medical and Pharmaceutical College(No.YGZZK2025116)2025 Technological Innovation Project of Shapingba District,Chongqing(No.2025031)Chongqing Municipal Education Commission Youth Project(No.KJQN202402821No.KJQN202302811)Joint project of Chongqing Health Commission and Science and Technology Bureau(No.2024MSXM115)respectively.
文摘Host-yeast interactions are fundamental drivers of human microbiome dynamics,spanning a spectrum from mutualistic symbiosis to opportunistic pathogenesis with profound implications for systemic health.This review systematically elucidates the complex molecular mechanisms governing these relationships,with a specific focus on metabolic interdependence and immunomodulation.We analyze how yeast-derived metabolites,particularly short-chain fatty acids(SCFAs),modulate host glucose and lipid homeostasis via signaling pathways such as GPR41/43 and GLP-1 secretion.Furthermore,the review explores the pathophysiological role of fungal dysbiosis in chronic conditions,including obesity,diabetes,and inflammatory bowel disease(IBD),highlighting how a breakdown in host-yeast homeostasis triggers pro-inflammatory cascades.Beyond the fungal-host axis,we introduce the concept of the"mycobiome-virome-bacterial axis,"discussing how commensal yeasts synergize with beneficial bacteria like Bifidobacterium and influence viral infectivity through Interferon-mediated innate immune priming.We critically evaluate how cutting-edge technologies-including transgenic mouse models(specifically Dectin-1^(-/-)and CARD9^(-/-),metabolomics,and single-cell sequencing-have revolutionized our mechanistic understanding of these multi-kingdom dynamics.By integrating current findings,we identify critical knowledge gaps and propose high-resolution research frameworks,such as humanized organ-on-a-chip systems,to simulate intricate host-microbe interactions under physiological flow conditions.This comprehensive synthesis provides a strategic foundation for developing targeted,next-generation microbiome-based interventions to restore host-yeast balance and enhance overall human health.
文摘Pancreatic ductal adenocarcinoma(PDAC)remains one of the most lethal malignancies,characterized by a highly immunosuppressive tumor microenvironment(TME),dense stromal architecture,and limited response to conventional therapies.This review comprehensively examines the emerging role of chimeric antigen receptor(CAR)-engineered immune cells,including chimeric antigen receptor-T(CAR-T),CAR-macrophages(CAR-M),and CAR-natural killer(CAR-NK)cells,as innovative immunotherapeutic strategies for PDAC.We delve into the mechanistic foundations of these platforms,highlighting their unique abilities to target tumor-associated antigens,overcome stromal barriers,and remodel the immunosuppressive TME.Recent preclinical and clinical advances demonstrate promising antitumor activity,particularly with targets such as mesothelin,claudin18.2,and human epidermal growth factor 2(HER2),though challenges related to antigen heterogeneity,TME suppression,and cell persistence remain.We further discuss synergistic approaches involving genetic engineering,microenvironment modulation,and combination therapies aimed at enhancing efficacy.Finally,we offer perspectives on the future direction of CARbased therapies,including the development of next-generation constructs,allogeneic“off-the-shelf”products,and personalized combination regimens,underscoring their potential in pancreatic cancer.
基金supported by the Natural Science Foundation of Beijing Municipality(6252026)the Youth Innovation Program of CAAS(Y2024QC11)the Agricultural Science and Technology Innovation Program under Grant(CAAS-ASTIP-2024-IAR).
文摘The monofloral honey derived from Bauhinia championii(Benth.)Benth.(MH-Bc)possesses significant nutritional and bioactive value,making it highly suitable for commercial exploitation.However,the poorly defined characteristics and unknown composition have hindered MH-Bc product development.In this study,we employed a combination of untargeted and targeted mass spectrometry analyses to characterize MH-Bc honey.As a result,4,7,8-trimethoxydibenzo[b,d]furan-3-ol(TDBF)was identified as a robust chemical marker for distinguishing MH-Bc from other types of honey.This specific marker was detected in both MH-Bc and the Bc plant but was absent in other honey varieties.Furthermore,a targeted mass spectrometry quantitative method was developed and validated to accurately determine the content of TDBF in honey samples.Overall,the presence of TDBF serves as a discerning indicator for future commercial MH-Bc products.
基金Supported by the National Natural Science Foundation of China(Grant No.12161080).
文摘In this paper,the dynamics of a Leslie-Gower predator-prey model with weak nonlin-ear harvesting and prey-taxis is discussed.By comparing and analyzing the differences between ordinary differential systems and chemotaxis systems in the stability of equilibrium points,the influence mechanism of chemotaxis on the dynamic behavior of the system is deeply studied.Theoretical analysis shows that chemotaxis significantly changes the stability characteristics of the system,and the reliability of theoretical results is further verified by numerical simulation.
基金supported by the Fundamental Research Funds for the Central Universities(No.20720230037)the National Natural Science Foundation of China(No.52273305)+2 种基金Natural Science Foundation of Fujian Province of China(No.2023J05012)State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(Nos.2023XAKJ0103071,2023XAKJ0102061)Natural Science Foundation of Xiamen,China(No.3502Z20227010).
文摘Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific staining procedures involving carcinogenic chemicals.This study proposes an interdisciplinary approach merging materials science,medicine,and artificial intelligence(AI)to develop a virtual staining technique and intelligent evaluation model based on deep learning for chronic wound tissue pathology.This innovation aims to enhance clinical diagnosis and treatment by offering personalized AI-driven therapeutic strategies.By establishing a mouse model of chronic wounds and using a series of hydrogel wound dressings,tissue pathology sections were periodically collected for manual staining and healing assessment.We focused on leveraging the pix2pix image translation framework within deep learning networks.Through CNN models implemented in Python using PyTorch,our study involves learning and feature extraction for region segmentation of pathological slides.Comparative analysis between virtual staining and manual staining results,along with healing diagnosis conclusions,aims to optimize AI models.Ultimately,this approach integrates new metrics such as image recognition,quantitative analysis,and digital diagnostics to formulate an intelligent wound assessment model,facilitating smart monitoring and personalized treatment of wounds.In blind evaluation by pathologists,minimal disparities were found between virtual and conventional histologically stained images of murine wound tissue.The evaluation used pathologists’average scores on real stained images as a benchmark.The scores for virtual stained images were 71.1%for cellular features,75.4%for tissue structures,and 77.8%for overall assessment.Metrics such as PSNR(20.265)and SSIM(0.634)demonstrated our algorithms’superior performance over existing networks.Eight pathological features such as epidermis,hair follicles,and granulation tissue can be accurately identified,and the images were found to be more faithful to the actual tissue feature distribution when compared to manually annotated data.
文摘融合子图学习与联邦学习后,联邦子图学习在保护数据隐私的同时可实现多客户端子图信息之间的协同学习.然而,由于不同客户端的数据收集方式存在差异,图数据通常呈现非独立同分布特性.同时,不同客户端局部图数据的结构和特征也存在较大差异.这些因素导致联邦子图学习在训练过程中出现收敛困难和泛化能较差等问题.为了解决此问题,文中提出基于嵌入对齐与参数激活的个性化联邦子图学习方法(Personalized Federated Subgraph Learning with Embedding Alignment and Parameter Activation,FSL-EAPA).首先,根据客户端之间的相似性进行个性化模型聚合,降低数据非独立同分布对整体性能的影响.然后,引入参数选择性激活进行模型更新,应对子图结构特征的异质性.最后,利用更新后的客户端为各本地节点嵌入提供正负聚类表示,聚集同类局部节点.因此,FSL-EAPA能充分学习各节点的特征表示,较好地适应不同客户端之间的差异化数据分布.在真实基准图数据集上的实验表明FSL-EAPA的有效性,并且在不同场景下都能获得较高的分类精度.
