Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has be...Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has been studied using genome-wide association studies(GWAS)and linkage analyses to locate fat-related quantitative trait loci(QTLs),but pinpointing causal variants and genes is hindered by linkage disequilibrium and limited regulatory data.This study aimed to dissect the QTLs affecting BFT on Sus scrofa chromosome 1(SSC1),elucidating regulatory variants,effector genes,and the cell types involved.Results Using whole-genome genotyping data from 3,578 pigs and phenotypic data for five BFT traits,we identified a 630.6 kb QTL on SSC1 significantly associated with these traits via GWAS and fine-mapping,pinpointing 34 candidate causal variants.Using deep convolutional neural networks to predict regulatory activity from sequence data integrated with detailed pig epigenetic profiles,we identified five SNPs potentially affecting enhancer activity in specific tissues.Notably,rs342950505(SSC1:161,123,588)influences weak enhancer activity across multiple tissues,including the brain.High-throughput chromosome conformation capture(Hi-C)analysis identified that rs342950505 interacts with eight genes.Chromatin state annotations confirmed enhancer activity at this QTL in the cerebellum.Leveraging these insights,single-cell ATAC-seq revealed a chromatin accessibility peak encompassing rs342950505 that regulates PMAIP1 expression in inhibitory neurons via enhancer-mediated mechanisms,with an adjacent peak modulating CCBE1 expression in neuroblasts and granule cells.Transcriptome-wide association studies(TWAS)confirmed PMAIP1's role in the hypothalamus,and Mendelian randomization(MR)validated PMAIP1 and CCBE1 as key brain expression quantitative trait locus(eQTL)effectors.We propose that the variant rs342950505,located within a regulatory peak,modulates PMAIP1 expression in inhibitory neurons,potentially influencing energy homeostasis via hypothalamic regulation.Similarly,CCBE1 may contribute to this process.Conclusions Our results,through systematic dissection of pleiotropic BFT-associated loci,provide a framework to elucidate regulatory mechanisms of complex traits,offering insights into polygenic control through lipid metabolism and neural signaling pathways.展开更多
Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through...Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.展开更多
Previous studies have demonstrated that the immunoglobulin G(IgG)N-glycome and transcriptome are potential biochemical signatures of chronological and biological ages,and several aging clocks have been developed.By in...Previous studies have demonstrated that the immunoglobulin G(IgG)N-glycome and transcriptome are potential biochemical signatures of chronological and biological ages,and several aging clocks have been developed.By integrating the IgG N-glycome and transcriptome,we propose a novel aging clock,gtAge.We developed a deep reinforcement learning-based multiomics integration method called AlphaSnake.The results showed that AlphaSnake achieved a predicted coefficient of determination(R^(2))value of0.853,outperforming the concatenation-based integration method(R^(2)=0.820)The gtAge estimated by AlphaSnake explained up to 85.3%of the variance in chronological age,which was higher than that in age predicted from IgG N-glycome solely(gAge;R^(2)=0.290)and age predicted from transcriptome solely(tAge;R^(2)=0.812).We also found that the delta age-the difference between the predicted age and chronological age-was associated with several age-related phenotypes.Both delta gtAge and tAge were negatively associated with high-density lipoprotein(p=0.02 and p=0.022,respectively),whereas delta gAge was positively correlated with cholesterol(p=0.006),triglyceride(p=0.002),fasting plasma glucose(p=0.014),low-density lipoprotein(p=0.006),and glycated hemoglobin(p=0.039).These findings suggest that gtAge,tAge,and gAge are potential biomarkers for biological age.展开更多
Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insi...Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insights needed for this purpose.Methods:To comprehensively capture breast cancer heterogeneity,we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples.Ten distinct clustering algorithms were combined to ensure robust subtype identification,and the identified subtypes were validated in four independent datasets.Subsequently,we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.Results:Five novel subtypes were identified:consensus subtypes 1–5(CS1–CS5).CS2 was an aggressive subtype with elevated TP53 mutation rates,high tumor mutational burden,and strong sensitivity to YM-155 and ispinesib.Conversely,CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses.CS2 and CS4 showed enriched immune checkpoint expression,indicating potential immunotherapy responsiveness,while CS1 and CS5 exhibited immune-cold profiles.The survival support vector machine model effectively predicted survival outcomes across independent datasets.Conclusions:The refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity,enhances risk stratification,and enables the identification of promising therapeutic targets.The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.展开更多
Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid ...Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid and nutrient components between the adipose tissue of Bamei pigs and commercial pigs are still unclear.The study employed UPLC-MS/MS to quantify the composition of lipids and metabolites in the backfat of both Bamei and Large White pigs.A total of 428 lipids and 193 metabolites were significantly different between the 2 groups.Specifically,Bamei pig backfat exhibited altered levels of various lipids and metabolites that may potentially contribute to nutritional and flavor differences,including unsaturated triglycerides,free fatty acids,medium-chain triglycerides,essential amino acids,vitamins and antioxidants,while maintaining reduced cholesterol levels.Furthermore,we delved into the molecular mechanisms underlying these nutritional differences by analyzing significantly different 431 m RNAs and 865 proteins and integrating the regulatory network of protein-metabolite-lipid pathway.Importantly,in the pyruvate metabolic pathway of Bamei pigs,the bioprocess of lactate production was inhibited but the acetyl-Co A production was activated,suggesting the possibility that energy allocation favors the biogenesis of lipid precursors.These findings may contribute to guiding industrial food producers in enhancing the quality of lard at the genetic and molecular levels.展开更多
Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-e...Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-established amyloid precursor protein/presenilin 1 mouse model to carry out an integrated multi-omics study using transcriptomic,proteomic,N^(6)-methyladenosine epitranscriptomic,and phosphoproteomic analyses.The results revealed substantial molecular alterations across multiple biological dimensions and the alteration in the expression of several key genes,such as GFAP,APP,and RTN4,in a mouse model of Alzheimer’s disease.The pronounced elevation of RTN4 in reactive astrocytes is indicative of its involvement in Alzheimer’s disease pathogenesis.Furthermore,we identified dysregulation of pathways related to endocytosis,highlighting the critical role of this process in disease progression.Our findings underscore the significant impact of post-transcriptional(N^(6)-methyladenosine methylation)and post-translational(phosphorylation)protein modifications,which have been underrepresented in Alzheimer’s disease research.The significant contribution made by this study is the integrated,multi-level omics analysis that we carried out to investigate the complex biological changes that occur in Alzheimer’s disease.Our findings provide novel insights into Alzheimer’s disease pathogenesis and suggest potential therapeutic targets,such as RTN4.展开更多
Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging ...Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.展开更多
Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliomet...Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliometric analysis.Methods China National Knowledge Infrastructure(CNKI),Wanfang Data,China Science and Technology Journal Database(VIP),Chaoxing Journal Database,PubMed,and Web of Science were searched to collect literature on the theme of AI in TCM multi-omics research from the inception of each database to December 31,2024.Eligible records were required to simultaneously address AI,TCM,and multi-omics.Quantitative and visual analyses of publication growth,core authorship networks,institutional collaboration patterns,and keyword co-occurrence were performed using Microsoft Excel 2021,NoteExpress v4.0.0,and Cite-Space 6.3.R1.AI application modes in TCM multi-omics research were also categorized and summarized.Results A total of 1106 articles were enrolled(932 Chinese and 174 English).Publication output has increased continuously since 2010 and accelerated after 2016.Region-specific collaboration clusters were identified,dominated by Beijing University of Chinese Medicine,China Academy of Chinese Medical Sciences,Shanghai University of Traditional Chinese Medicine,and Nanjing University of Chinese Medicine.Keyword co-occurrence analysis revealed that current AI applications predominantly centered on metabolomics and algorithms such as cluster analysis and data mining.Research foci mainly ranked as follows:single herbs,herbal formulae,and disease-syndrome differentiation.Conclusion Machine learning methods are the predominant integrative modality of AI in the realm of TCM multi-omics research at present,utilized for processing omics data and uncovering latent patterns therein.The domain of TCM,in addition to investigating omics information procured through high-throughput technologies,also integrates data on traditional Chinese medicinal substances and clinical phenotypes,progressing towards joint analysis of multi-omics,high-dimensionality of data,and multi-modality of information.Deep learning approaches represent an emerging trend in the field.展开更多
Acute myeloid leukemia(AML)is an aggressive hematologic malignancy characterized by poor clinical outcomes,frequently exacerbated by mutations in the FMS-like tyrosine kinase 3(FLT3)gene.Although FLT3 inhibitors(FLT3i...Acute myeloid leukemia(AML)is an aggressive hematologic malignancy characterized by poor clinical outcomes,frequently exacerbated by mutations in the FMS-like tyrosine kinase 3(FLT3)gene.Although FLT3 inhibitors(FLT3i)have emerged as promising therapeutic agents,the absence of molecular biomarkers to predict FLT3i response remains a critical limitation in clinical practice.In this study,we performed a comprehensive multi-omics analysis integrating transcriptomic,proteomic,and pharmacogenomic data from the Beat AML cohort,the Cancer Cell Line Encyclopedia(CCLE),and the PXD023201 repository to elucidate the molecular consequences of FLT3 mutations in AML.Our analysis revealed significant differences in RNA and protein expression profiles between FLT3-mutant and wild-type AML cases,with a particularly striking association between FLT3 mutations and immune suppression.We further evaluated the drug sensitivity of FLT3-mutant patients to 3 FDA-approved FLT3i,gilteritinib,midostaurin,and quizartinib,and observed heightened sensitivity in FLT3-mutant cohorts,accompanied by the activation of immune-related pathways in treatment-responsive groups.These findings suggest a potential synergy between FLT3i efficacy and immune activation.Through rigorous bioinformatic analysis,we identified 3 candidate biomarkers:CD36,SASH1,and NIBAN2,associated with FLT3i sensitivity.These biomarkers were consistently upregulated in favorable prognostic subgroups and demonstrated strong correlations with immune activation pathways.The identification of CD36,SASH1,and NIBAN2 as predictive biomarkers offers a novel toolset for stratifying FLT3i response and prognosis.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
The integration of multi-omic liquid biopsies with artificial intelligence(AI)represents a rapidly evolving frontier in early cancer detection,offering the potential to enhance personalized medicine and improve patien...The integration of multi-omic liquid biopsies with artificial intelligence(AI)represents a rapidly evolving frontier in early cancer detection,offering the potential to enhance personalized medicine and improve patient outcomes.This review explores the current state and emerging directions of this approach,focusing on the synergistic value of combining genomics,epigenomics,transcriptomics,proteomics,and metabolomics with AIdriven analytics.We discuss advances in multi-analyte blood tests such as CancerSEEK,which have demonstrated promising multi-cancer detection capabilities in early studies,as well as efforts to integrate liquid biopsy data with imaging modalities to improve diagnostic performance.The review also highlights ongoing challenges,including the need for greater analytical sensitivity,improved specificity for early-stage disease,standardization of workflows,and harmonization with existing screening modalities.We outline the prospective—but still largely investigational—impact of these technologies on cancer management,including early detection,treatment monitoring,and minimal residual disease assessment,along with their potential economic implications.Ultimately,we envision a future in which multi-omic liquid biopsies integrated with AI may contribute to more effective,noninvasive cancer detection strategies,while recognizing that substantial validation,regulatory approval,and health-system integration are required before widespread clinical adoption can occur.展开更多
Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including no...Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including non-invasive biomarkers have been explored for the diagnosis of viral hepatitis.With the fast development of multi-omics technology,non-invasive biomarkers can be detected from blood,saliva,urine,stool,and other body fluids.The advantages of non-invasive biomarkers are:1)non-invasive;2)convenient to test and 3)repeatable.The application of non-invasive biomarkers significantly improves the diagnostic accuracy of viral hepatitis.The non-invasive biomarkers can be sugars,proteins,nucleic acids,and even microorganisms.In this review,we summarized recent advances in identifying non-invasive biomarkers using multi-omics technology and discussed their potential diagnostic values for viral hepatitis.展开更多
Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancer...Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancerremains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatmentstrategies for patients with gastric cancer. Methods: Single-cell and bulk transcriptomic analyses were integrated tocharacterize gastric cancer fibroblasts. “Seurat”, “Slingshot”, and “CellChat” were used for dimensionality reduction,trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast moduleswere identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct aprognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features.The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays. Results: Eight fibroblast subpopulations associated with gastric cancer metastasisexhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated thatmetastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed markedimmunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-relatedpathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressedgastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis. Conclusion: This studycharacterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis andestablished a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strongpredictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, thefindings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.展开更多
Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi...Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi-omics strategy to characterize the changes and interactions among metabolomic(MB),transcriptomic(TX),and proteomic(PT)profiles in mechanically stressed tea leaves.Mechanical stress initially activated damage-associated molecular patterns(DAMPs),including Ca^(2+)signaling,jasmonic acid signaling,and glutathione metabolism pathways.These processes subsequently induced quality-related metabolic pathways(QRMPs),particularly α-linolenic acid and phenylalanine metabolism.Upregulated expression of LOX,ADH1,and PAR genes,together with the increased abundance of their encoded proteins,respectively promoted the accumulation of jasmine lactone,benzyl alcohol,and 2-phenylethanol.These findings indicate that mechanical stress influences the metabolite biosynthesis in tea leaves through coordinated molecular responses.This study provides new insights into the molecular mechanisms underlying tea leaf responses to mechanical stress and a foundation for future investigations into how early molecular events may contribute to post-harvest metabolic changes during oolong tea processing.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industr...This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industry,the course content has been updated to incorporate AI techniques such as large language models and deep learning.The reform enriches educational resources and introduces innovative instructional approaches.In addition,high-quality practical teaching cases have been developed,and immersive,hands-on learning experiences have been designed based on industrial platforms and real-world applications.These initiatives aim to enhance the practical skills and innovative thinking of professional degree graduate students,fostering high-caliber talent that aligns with industry demands.A survey of 90 graduate students revealed high levels of satisfaction regarding course content,teaching methodology,and skill development.The reform has proven effective in cultivating interdisciplinary professionals with solid foundations in software engineering and AI-driven innovation.展开更多
Ningxia is an ethnic gathering area boasting abundant tourism and cultural resources.Developing the cause of tourism and culture is an important way to encourage all ethnic groups to respect differences,embrace divers...Ningxia is an ethnic gathering area boasting abundant tourism and cultural resources.Developing the cause of tourism and culture is an important way to encourage all ethnic groups to respect differences,embrace diversity,and demonstrate their interactions,exchanges,and integration in tourism activities.As an important preserve of the distinctive cultures of the Chinese nation and a prominent world tourist destination,Ningxia should strive to foster and consolidate the sense of a community with a shared future for the Chinese nation in developing its tourism and culture under the new historical conditions.It is imperative to advance the prosperity and development of tourism and culture in boosting ethnic interactions,exchanges,and integration through the formulation of tourism and cultural policies and plans,as well as the development and design of tourism and cultural projects.展开更多
This paper undertakes a systematic combing of the development of research on integrating Chinese culture into foreign language education in China from the 1980s to 2025,dividing it into three stages:cultural attachmen...This paper undertakes a systematic combing of the development of research on integrating Chinese culture into foreign language education in China from the 1980s to 2025,dividing it into three stages:cultural attachment,cultural compensation,and cultural symbiosis,and reveals the logical shift of the research from the dominance of target language culture to the construction of the subjectivity of Chinese culture.Through quantitative and qualitative analysis of 435 CSSCI papers,three core themes are extracted:what to integrate,why to integrate,and how to integrate.This paper critically analyzes three pairs of contradictions:the imbalance between instrumentality and humanism,the separation of national narrative and individual expression,and the disconnection between traditional inheritance and modern transformation.It is proposed that future research should reconstruct the educational logic based on the Chinese context,integrate the national and individual dimensions,and build a dialogue mechanism between tradition and modernity,so as to provide theoretical and practical reference for the construction of a foreign language education system with Chinese characteristics.展开更多
Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
基金supported by the China Postdoctoral Science Foundation[Grant Number BX20240146 and 2024M761230]Key Project of Research and Development Plan in Jiangxi Province[Grant Number 20243BCC31001].
文摘Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has been studied using genome-wide association studies(GWAS)and linkage analyses to locate fat-related quantitative trait loci(QTLs),but pinpointing causal variants and genes is hindered by linkage disequilibrium and limited regulatory data.This study aimed to dissect the QTLs affecting BFT on Sus scrofa chromosome 1(SSC1),elucidating regulatory variants,effector genes,and the cell types involved.Results Using whole-genome genotyping data from 3,578 pigs and phenotypic data for five BFT traits,we identified a 630.6 kb QTL on SSC1 significantly associated with these traits via GWAS and fine-mapping,pinpointing 34 candidate causal variants.Using deep convolutional neural networks to predict regulatory activity from sequence data integrated with detailed pig epigenetic profiles,we identified five SNPs potentially affecting enhancer activity in specific tissues.Notably,rs342950505(SSC1:161,123,588)influences weak enhancer activity across multiple tissues,including the brain.High-throughput chromosome conformation capture(Hi-C)analysis identified that rs342950505 interacts with eight genes.Chromatin state annotations confirmed enhancer activity at this QTL in the cerebellum.Leveraging these insights,single-cell ATAC-seq revealed a chromatin accessibility peak encompassing rs342950505 that regulates PMAIP1 expression in inhibitory neurons via enhancer-mediated mechanisms,with an adjacent peak modulating CCBE1 expression in neuroblasts and granule cells.Transcriptome-wide association studies(TWAS)confirmed PMAIP1's role in the hypothalamus,and Mendelian randomization(MR)validated PMAIP1 and CCBE1 as key brain expression quantitative trait locus(eQTL)effectors.We propose that the variant rs342950505,located within a regulatory peak,modulates PMAIP1 expression in inhibitory neurons,potentially influencing energy homeostasis via hypothalamic regulation.Similarly,CCBE1 may contribute to this process.Conclusions Our results,through systematic dissection of pleiotropic BFT-associated loci,provide a framework to elucidate regulatory mechanisms of complex traits,offering insights into polygenic control through lipid metabolism and neural signaling pathways.
基金supported by grants from the Fundamental Research Funds for the Central Universities(No.2025ZFJH03)the Central Guidance Fund for Local Science and Technology Development(No.2024ZY01054)the CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-045).
文摘Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.
基金funded by an Australia–China International Collaborative Grant(NHMRC APP1112767-NSFC 81561128020)the European Union’s Horizon 2020 Research and Innovation Program under grant agreement(779238)+2 种基金the Edith Cowan University Higher Degree by Research Scholarship(ECU-HDR 10492768)the Western Australian Future Health Research and Innovation Funds(WANMA/EL2023-24/2 and WANMA/Ideas2024-25/5)the Edith Cowan University Early-Mid Career Researcher Grant Scheme(G1006465)。
文摘Previous studies have demonstrated that the immunoglobulin G(IgG)N-glycome and transcriptome are potential biochemical signatures of chronological and biological ages,and several aging clocks have been developed.By integrating the IgG N-glycome and transcriptome,we propose a novel aging clock,gtAge.We developed a deep reinforcement learning-based multiomics integration method called AlphaSnake.The results showed that AlphaSnake achieved a predicted coefficient of determination(R^(2))value of0.853,outperforming the concatenation-based integration method(R^(2)=0.820)The gtAge estimated by AlphaSnake explained up to 85.3%of the variance in chronological age,which was higher than that in age predicted from IgG N-glycome solely(gAge;R^(2)=0.290)and age predicted from transcriptome solely(tAge;R^(2)=0.812).We also found that the delta age-the difference between the predicted age and chronological age-was associated with several age-related phenotypes.Both delta gtAge and tAge were negatively associated with high-density lipoprotein(p=0.02 and p=0.022,respectively),whereas delta gAge was positively correlated with cholesterol(p=0.006),triglyceride(p=0.002),fasting plasma glucose(p=0.014),low-density lipoprotein(p=0.006),and glycated hemoglobin(p=0.039).These findings suggest that gtAge,tAge,and gAge are potential biomarkers for biological age.
基金supported by the National Natural Science Foundation of China(Grant No.:82560497,82260502,82272656)Guizhou Provincial Basic Research Program(Grant No.:Natural Science,MS[2025]-495)Talent Fund of Guizhou Provincial People’s Hospital(Grant No.:2022-33).
文摘Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insights needed for this purpose.Methods:To comprehensively capture breast cancer heterogeneity,we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples.Ten distinct clustering algorithms were combined to ensure robust subtype identification,and the identified subtypes were validated in four independent datasets.Subsequently,we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.Results:Five novel subtypes were identified:consensus subtypes 1–5(CS1–CS5).CS2 was an aggressive subtype with elevated TP53 mutation rates,high tumor mutational burden,and strong sensitivity to YM-155 and ispinesib.Conversely,CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses.CS2 and CS4 showed enriched immune checkpoint expression,indicating potential immunotherapy responsiveness,while CS1 and CS5 exhibited immune-cold profiles.The survival support vector machine model effectively predicted survival outcomes across independent datasets.Conclusions:The refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity,enhances risk stratification,and enables the identification of promising therapeutic targets.The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.
基金supported by the National Key Research and Development Program of China(2021YFF1000602)the National Natural Science Foundations(32202642)the earmarked fund for CARS-35-PIG.
文摘Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid and nutrient components between the adipose tissue of Bamei pigs and commercial pigs are still unclear.The study employed UPLC-MS/MS to quantify the composition of lipids and metabolites in the backfat of both Bamei and Large White pigs.A total of 428 lipids and 193 metabolites were significantly different between the 2 groups.Specifically,Bamei pig backfat exhibited altered levels of various lipids and metabolites that may potentially contribute to nutritional and flavor differences,including unsaturated triglycerides,free fatty acids,medium-chain triglycerides,essential amino acids,vitamins and antioxidants,while maintaining reduced cholesterol levels.Furthermore,we delved into the molecular mechanisms underlying these nutritional differences by analyzing significantly different 431 m RNAs and 865 proteins and integrating the regulatory network of protein-metabolite-lipid pathway.Importantly,in the pyruvate metabolic pathway of Bamei pigs,the bioprocess of lactate production was inhibited but the acetyl-Co A production was activated,suggesting the possibility that energy allocation favors the biogenesis of lipid precursors.These findings may contribute to guiding industrial food producers in enhancing the quality of lard at the genetic and molecular levels.
基金supported by the National Natural Science Foundation of China,No.82374552(to WP)the Natural Science Foundation of Hunan Province,Nos.2024JJ2086,2024JJ6597(to JK)+1 种基金the Science and Technology Innovation Program of Hunan Province,No.2022RC1220(to WP)Support Plan for High-Level Health and Medical Talents in Hunan Province,No.20240304076(to WP).
文摘Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-established amyloid precursor protein/presenilin 1 mouse model to carry out an integrated multi-omics study using transcriptomic,proteomic,N^(6)-methyladenosine epitranscriptomic,and phosphoproteomic analyses.The results revealed substantial molecular alterations across multiple biological dimensions and the alteration in the expression of several key genes,such as GFAP,APP,and RTN4,in a mouse model of Alzheimer’s disease.The pronounced elevation of RTN4 in reactive astrocytes is indicative of its involvement in Alzheimer’s disease pathogenesis.Furthermore,we identified dysregulation of pathways related to endocytosis,highlighting the critical role of this process in disease progression.Our findings underscore the significant impact of post-transcriptional(N^(6)-methyladenosine methylation)and post-translational(phosphorylation)protein modifications,which have been underrepresented in Alzheimer’s disease research.The significant contribution made by this study is the integrated,multi-level omics analysis that we carried out to investigate the complex biological changes that occur in Alzheimer’s disease.Our findings provide novel insights into Alzheimer’s disease pathogenesis and suggest potential therapeutic targets,such as RTN4.
基金supported by the grants from the Key Research and Development Program of Xinjiang Uygur autonomous region in China(Grant No.2023B02017)the National Key Research and Development Program of China(Grant No.2024YFD2300703)+1 种基金the financial support from the Beijing Rural Revitalization Agricultural Science and Technology Project(Grant No.NY2401080000),BAIC01-2025the 2115 Talent Development Program of China Agricultural University.
文摘Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.
基金General Project of Scientific Research of Hunan Provincial Education Department (22C0191)General Project of University-level Scientific Research of Hunan University of Chinese Medicine (Z2023XJYB21)Hunan Provincial Degree and Graduate Education Reform Research Project(2024JGYB157)。
文摘Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliometric analysis.Methods China National Knowledge Infrastructure(CNKI),Wanfang Data,China Science and Technology Journal Database(VIP),Chaoxing Journal Database,PubMed,and Web of Science were searched to collect literature on the theme of AI in TCM multi-omics research from the inception of each database to December 31,2024.Eligible records were required to simultaneously address AI,TCM,and multi-omics.Quantitative and visual analyses of publication growth,core authorship networks,institutional collaboration patterns,and keyword co-occurrence were performed using Microsoft Excel 2021,NoteExpress v4.0.0,and Cite-Space 6.3.R1.AI application modes in TCM multi-omics research were also categorized and summarized.Results A total of 1106 articles were enrolled(932 Chinese and 174 English).Publication output has increased continuously since 2010 and accelerated after 2016.Region-specific collaboration clusters were identified,dominated by Beijing University of Chinese Medicine,China Academy of Chinese Medical Sciences,Shanghai University of Traditional Chinese Medicine,and Nanjing University of Chinese Medicine.Keyword co-occurrence analysis revealed that current AI applications predominantly centered on metabolomics and algorithms such as cluster analysis and data mining.Research foci mainly ranked as follows:single herbs,herbal formulae,and disease-syndrome differentiation.Conclusion Machine learning methods are the predominant integrative modality of AI in the realm of TCM multi-omics research at present,utilized for processing omics data and uncovering latent patterns therein.The domain of TCM,in addition to investigating omics information procured through high-throughput technologies,also integrates data on traditional Chinese medicinal substances and clinical phenotypes,progressing towards joint analysis of multi-omics,high-dimensionality of data,and multi-modality of information.Deep learning approaches represent an emerging trend in the field.
基金supported by grants from the CAMS Innovation Fund for Medical Science(2023-I2M-3-014 to H.W.,2023-I2M-2-007 to H.W.,2021-I2M-1-073 to H.W.)the National Natural Science Foundation of China(82270236 to H.W.,82341080 to H.W.,82400271 to M.N.,82273217 to L.S.)+2 种基金the Fundamental Research Funds for the Central Universities,Peking Union Medical College(3332024200 to M.N.)the distinguished Young Scholars of Tianjin(22JCJQJC00090 to H.W.)the Tianjin Municipal Science and Technology Commission Grant(22JCQNJC00040 to L.S.).
文摘Acute myeloid leukemia(AML)is an aggressive hematologic malignancy characterized by poor clinical outcomes,frequently exacerbated by mutations in the FMS-like tyrosine kinase 3(FLT3)gene.Although FLT3 inhibitors(FLT3i)have emerged as promising therapeutic agents,the absence of molecular biomarkers to predict FLT3i response remains a critical limitation in clinical practice.In this study,we performed a comprehensive multi-omics analysis integrating transcriptomic,proteomic,and pharmacogenomic data from the Beat AML cohort,the Cancer Cell Line Encyclopedia(CCLE),and the PXD023201 repository to elucidate the molecular consequences of FLT3 mutations in AML.Our analysis revealed significant differences in RNA and protein expression profiles between FLT3-mutant and wild-type AML cases,with a particularly striking association between FLT3 mutations and immune suppression.We further evaluated the drug sensitivity of FLT3-mutant patients to 3 FDA-approved FLT3i,gilteritinib,midostaurin,and quizartinib,and observed heightened sensitivity in FLT3-mutant cohorts,accompanied by the activation of immune-related pathways in treatment-responsive groups.These findings suggest a potential synergy between FLT3i efficacy and immune activation.Through rigorous bioinformatic analysis,we identified 3 candidate biomarkers:CD36,SASH1,and NIBAN2,associated with FLT3i sensitivity.These biomarkers were consistently upregulated in favorable prognostic subgroups and demonstrated strong correlations with immune activation pathways.The identification of CD36,SASH1,and NIBAN2 as predictive biomarkers offers a novel toolset for stratifying FLT3i response and prognosis.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
文摘The integration of multi-omic liquid biopsies with artificial intelligence(AI)represents a rapidly evolving frontier in early cancer detection,offering the potential to enhance personalized medicine and improve patient outcomes.This review explores the current state and emerging directions of this approach,focusing on the synergistic value of combining genomics,epigenomics,transcriptomics,proteomics,and metabolomics with AIdriven analytics.We discuss advances in multi-analyte blood tests such as CancerSEEK,which have demonstrated promising multi-cancer detection capabilities in early studies,as well as efforts to integrate liquid biopsy data with imaging modalities to improve diagnostic performance.The review also highlights ongoing challenges,including the need for greater analytical sensitivity,improved specificity for early-stage disease,standardization of workflows,and harmonization with existing screening modalities.We outline the prospective—but still largely investigational—impact of these technologies on cancer management,including early detection,treatment monitoring,and minimal residual disease assessment,along with their potential economic implications.Ultimately,we envision a future in which multi-omic liquid biopsies integrated with AI may contribute to more effective,noninvasive cancer detection strategies,while recognizing that substantial validation,regulatory approval,and health-system integration are required before widespread clinical adoption can occur.
文摘Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including non-invasive biomarkers have been explored for the diagnosis of viral hepatitis.With the fast development of multi-omics technology,non-invasive biomarkers can be detected from blood,saliva,urine,stool,and other body fluids.The advantages of non-invasive biomarkers are:1)non-invasive;2)convenient to test and 3)repeatable.The application of non-invasive biomarkers significantly improves the diagnostic accuracy of viral hepatitis.The non-invasive biomarkers can be sugars,proteins,nucleic acids,and even microorganisms.In this review,we summarized recent advances in identifying non-invasive biomarkers using multi-omics technology and discussed their potential diagnostic values for viral hepatitis.
基金funded by the Key Research and Development and Promotion Project of Henan Province(Grant No.232102310130)。
文摘Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancerremains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatmentstrategies for patients with gastric cancer. Methods: Single-cell and bulk transcriptomic analyses were integrated tocharacterize gastric cancer fibroblasts. “Seurat”, “Slingshot”, and “CellChat” were used for dimensionality reduction,trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast moduleswere identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct aprognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features.The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays. Results: Eight fibroblast subpopulations associated with gastric cancer metastasisexhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated thatmetastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed markedimmunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-relatedpathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressedgastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis. Conclusion: This studycharacterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis andestablished a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strongpredictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, thefindings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.
基金supported by the National Key Research and Development Program of China(2022YFD2101101)the Earmarked Fund for CARS-19+2 种基金the National Natural Science Foundation of China(32402634)the Modern Agricultural(Tea)Industry Technology System of Fujian Province,China(2025 No.593)the Special Fund for Science and Technology Innovation of Fujian Zhang Tianfu Tea Development Foundation,China(FJZTF01)。
文摘Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi-omics strategy to characterize the changes and interactions among metabolomic(MB),transcriptomic(TX),and proteomic(PT)profiles in mechanically stressed tea leaves.Mechanical stress initially activated damage-associated molecular patterns(DAMPs),including Ca^(2+)signaling,jasmonic acid signaling,and glutathione metabolism pathways.These processes subsequently induced quality-related metabolic pathways(QRMPs),particularly α-linolenic acid and phenylalanine metabolism.Upregulated expression of LOX,ADH1,and PAR genes,together with the increased abundance of their encoded proteins,respectively promoted the accumulation of jasmine lactone,benzyl alcohol,and 2-phenylethanol.These findings indicate that mechanical stress influences the metabolite biosynthesis in tea leaves through coordinated molecular responses.This study provides new insights into the molecular mechanisms underlying tea leaf responses to mechanical stress and a foundation for future investigations into how early molecular events may contribute to post-harvest metabolic changes during oolong tea processing.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
基金supported by Project of Higher Education Teaching Reform Research in Heilongjiang Province(Graduate Education)(Grant No.SJGYY2024030).
文摘This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industry,the course content has been updated to incorporate AI techniques such as large language models and deep learning.The reform enriches educational resources and introduces innovative instructional approaches.In addition,high-quality practical teaching cases have been developed,and immersive,hands-on learning experiences have been designed based on industrial platforms and real-world applications.These initiatives aim to enhance the practical skills and innovative thinking of professional degree graduate students,fostering high-caliber talent that aligns with industry demands.A survey of 90 graduate students revealed high levels of satisfaction regarding course content,teaching methodology,and skill development.The reform has proven effective in cultivating interdisciplinary professionals with solid foundations in software engineering and AI-driven innovation.
文摘Ningxia is an ethnic gathering area boasting abundant tourism and cultural resources.Developing the cause of tourism and culture is an important way to encourage all ethnic groups to respect differences,embrace diversity,and demonstrate their interactions,exchanges,and integration in tourism activities.As an important preserve of the distinctive cultures of the Chinese nation and a prominent world tourist destination,Ningxia should strive to foster and consolidate the sense of a community with a shared future for the Chinese nation in developing its tourism and culture under the new historical conditions.It is imperative to advance the prosperity and development of tourism and culture in boosting ethnic interactions,exchanges,and integration through the formulation of tourism and cultural policies and plans,as well as the development and design of tourism and cultural projects.
基金“A Study on the Value and Path of Integrating Excellent Traditional Chinese Culture Into Intercultural Communication Courses”(ZD2024)a project by the Beijing Higher Education Association,as well as“A Study on the Path of Empowering the Integration of Excellent Traditional Chinese Culture Into Intercultural Communication Courses With Generative AI”(2024),an institutional project of Beijing International Studies University.
文摘This paper undertakes a systematic combing of the development of research on integrating Chinese culture into foreign language education in China from the 1980s to 2025,dividing it into three stages:cultural attachment,cultural compensation,and cultural symbiosis,and reveals the logical shift of the research from the dominance of target language culture to the construction of the subjectivity of Chinese culture.Through quantitative and qualitative analysis of 435 CSSCI papers,three core themes are extracted:what to integrate,why to integrate,and how to integrate.This paper critically analyzes three pairs of contradictions:the imbalance between instrumentality and humanism,the separation of national narrative and individual expression,and the disconnection between traditional inheritance and modern transformation.It is proposed that future research should reconstruct the educational logic based on the Chinese context,integrate the national and individual dimensions,and build a dialogue mechanism between tradition and modernity,so as to provide theoretical and practical reference for the construction of a foreign language education system with Chinese characteristics.
基金supported by the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
