BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision therapies.AIM To investigate whether ...BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision therapies.AIM To investigate whether iCEMIGE(integration of CEll-morphometrics,MIcro-biome,and GEne biomarker signatures)improves risk stratification of breast cancer(BC)patients.METHODS We used our recently developed machine learning technique to identify cellular morphometric biomarkers(CMBs)from the whole histological slide images in The Cancer Genome Atlas(TCGA)breast cancer(TCGA-BRCA)cohort.Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score(CMPS)and our previously reported 12-gene expression prognosis score(GEPS)and 15-microbe abundance prognosis score(MAPS)were independent prognostic factors.iCEMIGE was built upon the sparse representation learning technique.The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS,GEPS,or MAPS alone.Nomogram models were created to predict overall survival(OS)and progress-free survival(PFS)rates at 5-and 10-year in the TCGA-BRCA cohort.RESULTS We identified 39 CMBs that were used to create a CMPS system in BCs.CMPS,GEPS,and MAPS were found to be significantly independently associated with OS.We then established an iCEMIGE scoring system for risk stratification of BC patients.The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors(age,stage,and estrogen and progesterone receptor status)and PAM50-based molecular subtype.Importantly,the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS,GEPS,or MAPS alone.CONCLUSION Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients,which can be extended to other types of cancer.展开更多
Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,rad...Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.展开更多
Joint analysis of multimodalities in spatial mass spectrometry imaging(SMSI)data,including histology,spatial location,and molecule data,allows us to gain novel insights into tissue structures.However,the significant d...Joint analysis of multimodalities in spatial mass spectrometry imaging(SMSI)data,including histology,spatial location,and molecule data,allows us to gain novel insights into tissue structures.However,the significant differences in characteristics such as scale and heterogeneity among the multimodal data,coupled with the high noise levels and uneven quality of MSI data,severely hinder their comprehensive analysis.Here,we introduce a cross-graph cycle attention model,MSCG,to learn efficient joint embeddings for multimodalities of SMSI data by integrating graph attention autoencoders and attention-transfer.Specifically,MSCG enables leveraging one modality(e.g.,histology)to fine-tune the graph neural network trained for another modality(e.g.,MSI).Our study on real datasets from different platforms highlights the superior capacities of MSCG in dissecting cellular heterogeneity,as well as in denoising and aggregating MSI data.Notably,MSCG demonstrates versatile applicability across MSI data from various platforms,showcasing its potential for broad utility in this field.展开更多
Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on t...Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on the application of LLMs to interpret interactions among stress,physical activity,and glucose dynamics across type 1,type 2,gestational,and monogenic diabetes(MODY).We examine methods for capturing contextual data beyond traditional patient diaries,including wearable sensors,lifestyle logs,and digital health tools,and discuss how these data are combined with continuous glucose monitoring.After retrieving 39 relevant studies and finally retaining 25 after screening,we summarize the current capabilities,limitations,and clinical implications of LLM-assisted multimodal approaches in diabetes management.The findings highlight both the promise and challenges of applying LLMs to synthesize heterogeneous data,providing insights for future research on enhancing individualized and evidence-based diabetes care.展开更多
Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellula...Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellular and niche events at the precancerous tipping point of early gastric cancer(EGC),we aimed to develop more precise screening tools and design targeted interventions to prevent malignant transformation at this stage.We utilized our AI models to integrate spatial multimodal data from nine EGC endoscopic submucosal dissection(ESD)samples(covering sequential stages from normal to cancer),construct a spatial-temporal profile of disease progression,and identify a critical tipping point(PMC_P)characterized by an immune-suppressive microenvironment during early cancer development.At this stage,inflammatory pit mucous cells with stemness(PMC_2)interact with fibroblasts via NAMPT→ITGA5/ITGB1 and with macrophages via AREG→EGFR/ERBB2 signaling,fostering cancer initiation.We established gastric precancerous cell lines and organoids to demonstrate that NAMPT and AREG promote cellular proliferation in vitro.Furthermore,in the transgenic CEA-SV40 mouse model,targeting AREG and/or NAMPT disrupted key cell interactions,inhibited the JAK-STAT,MAPK,and NFκB pathways,and reduced PD-L1 expression,which was also confirmed by western blot in vitro.These interventions delayed disease progression,reversed the immunosuppressive microenvironment,and prevented malignant transformation.Clinical validation was conducted using endoscopically resected EGC specimens.Our study provides a precise spatiotemporal depiction of EGC development and identifies novel diagnostic markers and therapeutic targets for early intervention.展开更多
基金Supported by This work was supported by the Department of Defense(DoD)BCRP,No.BC190820the National Cancer Institute(NCI)at the National Institutes of Health(NIH),No.R01CA184476+1 种基金MCIN/AEI/10.13039/501100011039,No.PID2020-118527RB-I00,and No.PDC2021-121735-I00the“European Union Next Generation EU/PRTR.”the Regional Government of Castile and León,No.CSI144P20.Lawrence Berkeley National Laboratory(LBNL)is a multi-program national laboratory operated by the University of California for the DOE under contract DE AC02-05CH11231.
文摘BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision therapies.AIM To investigate whether iCEMIGE(integration of CEll-morphometrics,MIcro-biome,and GEne biomarker signatures)improves risk stratification of breast cancer(BC)patients.METHODS We used our recently developed machine learning technique to identify cellular morphometric biomarkers(CMBs)from the whole histological slide images in The Cancer Genome Atlas(TCGA)breast cancer(TCGA-BRCA)cohort.Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score(CMPS)and our previously reported 12-gene expression prognosis score(GEPS)and 15-microbe abundance prognosis score(MAPS)were independent prognostic factors.iCEMIGE was built upon the sparse representation learning technique.The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS,GEPS,or MAPS alone.Nomogram models were created to predict overall survival(OS)and progress-free survival(PFS)rates at 5-and 10-year in the TCGA-BRCA cohort.RESULTS We identified 39 CMBs that were used to create a CMPS system in BCs.CMPS,GEPS,and MAPS were found to be significantly independently associated with OS.We then established an iCEMIGE scoring system for risk stratification of BC patients.The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors(age,stage,and estrogen and progesterone receptor status)and PAM50-based molecular subtype.Importantly,the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS,GEPS,or MAPS alone.CONCLUSION Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients,which can be extended to other types of cancer.
基金Supported by the Natural Science Foundation of Jilin Province,No.YDZJ202401182ZYTSJilin Provincial Key Laboratory of Precision Infectious Diseases,No.20200601011JCJilin Provincial Engineering Laboratory of Precision Prevention and Control for Common Diseases,Jilin Province Development and Reform Commission,No.2022C036.
文摘Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.
基金supported by the National Natural Science Foundation of China under Grant No.32300523the Shanghai Sailing Program under Grant No.22YF1401700+1 种基金the Fundamental Research Funds for the Central Universities of China under Grant No.2232022Dthe Shanghai Science and Technology Program under Grant No.20DZ2251400.
文摘Joint analysis of multimodalities in spatial mass spectrometry imaging(SMSI)data,including histology,spatial location,and molecule data,allows us to gain novel insights into tissue structures.However,the significant differences in characteristics such as scale and heterogeneity among the multimodal data,coupled with the high noise levels and uneven quality of MSI data,severely hinder their comprehensive analysis.Here,we introduce a cross-graph cycle attention model,MSCG,to learn efficient joint embeddings for multimodalities of SMSI data by integrating graph attention autoencoders and attention-transfer.Specifically,MSCG enables leveraging one modality(e.g.,histology)to fine-tune the graph neural network trained for another modality(e.g.,MSI).Our study on real datasets from different platforms highlights the superior capacities of MSCG in dissecting cellular heterogeneity,as well as in denoising and aggregating MSI data.Notably,MSCG demonstrates versatile applicability across MSI data from various platforms,showcasing its potential for broad utility in this field.
基金supported by the Starting Research Grant ofÓbuda University,the National Research,Development and Innovation Agency(No.2024-1.1.1-KKV_FÓKUSZ-2024-00074)the National Research,Development and Innovation Fund of Hungary(No.TKP2021-NKTA-36)+4 种基金the National Natural Science Foundation of China(No.82372051)the Beijing Natural Science Foundation(No.L222099)the Science and Technology Development Fund,Macao SAR(Nos.0093/2023/RIA2,0145/2023/RIA3,and 0157/2024/RIA2)the Capital’s Funds for Health Improvement and Research(No.2024-2-4015)the Sichuan Key-Area Research and Development Program(No.2024YFHZ0011).
文摘Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on the application of LLMs to interpret interactions among stress,physical activity,and glucose dynamics across type 1,type 2,gestational,and monogenic diabetes(MODY).We examine methods for capturing contextual data beyond traditional patient diaries,including wearable sensors,lifestyle logs,and digital health tools,and discuss how these data are combined with continuous glucose monitoring.After retrieving 39 relevant studies and finally retaining 25 after screening,we summarize the current capabilities,limitations,and clinical implications of LLM-assisted multimodal approaches in diabetes management.The findings highlight both the promise and challenges of applying LLMs to synthesize heterogeneous data,providing insights for future research on enhancing individualized and evidence-based diabetes care.
基金supported by Shanghai Oriental Talent Youth Program(QNKJ2024006)National Natural Science Foundation of China(82170555,32300523,32570769,and 62132015)+1 种基金Shanghai Academic/Technology Research Leader(22XD1422400)Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(22SG06).
文摘Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellular and niche events at the precancerous tipping point of early gastric cancer(EGC),we aimed to develop more precise screening tools and design targeted interventions to prevent malignant transformation at this stage.We utilized our AI models to integrate spatial multimodal data from nine EGC endoscopic submucosal dissection(ESD)samples(covering sequential stages from normal to cancer),construct a spatial-temporal profile of disease progression,and identify a critical tipping point(PMC_P)characterized by an immune-suppressive microenvironment during early cancer development.At this stage,inflammatory pit mucous cells with stemness(PMC_2)interact with fibroblasts via NAMPT→ITGA5/ITGB1 and with macrophages via AREG→EGFR/ERBB2 signaling,fostering cancer initiation.We established gastric precancerous cell lines and organoids to demonstrate that NAMPT and AREG promote cellular proliferation in vitro.Furthermore,in the transgenic CEA-SV40 mouse model,targeting AREG and/or NAMPT disrupted key cell interactions,inhibited the JAK-STAT,MAPK,and NFκB pathways,and reduced PD-L1 expression,which was also confirmed by western blot in vitro.These interventions delayed disease progression,reversed the immunosuppressive microenvironment,and prevented malignant transformation.Clinical validation was conducted using endoscopically resected EGC specimens.Our study provides a precise spatiotemporal depiction of EGC development and identifies novel diagnostic markers and therapeutic targets for early intervention.
