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
Background Cardiovascular disease (CVD) is mainly caused by atherosclerosis,for which dyslipidemia (i.e. high (V)LDL-cholesterol (C),high triglycerides and low HDLC is a major risk factor. To reduce the risk to develo...Background Cardiovascular disease (CVD) is mainly caused by atherosclerosis,for which dyslipidemia (i.e. high (V)LDL-cholesterol (C),high triglycerides and low HDLC is a major risk factor. To reduce the risk to develop CVD,patients with dyslipidemia are usually treated with lipid-lowering drugs including statins and fibrates.展开更多
Purpose： To get a better understanding of the way in which university rankings are used.Design/methodology/approach： Detailed analysis of the activities of visitors of the website of the CWTS Leiden Ranking.Findings...Purpose： To get a better understanding of the way in which university rankings are used.Design/methodology/approach： Detailed analysis of the activities of visitors of the website of the CWTS Leiden Ranking.Findings： Visitors of the Leiden Ranking website originate disproportionally from specific countries. They are more interested in impact indicators than in collaboration indicators, while they are about equally interested in size-dependent indicators and size-independent indicators. Many visitors do not seem to realize that they should decide themselves which criterion they consider most appropriate for ranking universities.Research limitations： The analysis is restricted to the website of a single university ranking. Moreover, the analysis does not provide any detailed insights into the motivations of visitors of university ranking websites.Practical implications： The Leiden Ranking website may need to be improved in order to make more clear to visitors that they should decide themselves which criterion they want to use for ranking universities.Originality/value： This is the first analysis of the activities of visitors of a university ranking website.展开更多
Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have in...Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.展开更多
Purpose:This study explores how academic mobility influenced research productivity and citation impactamong Ukrainian scholars from 2005 to 2023.It examines the effects of evolving research assessmentpolicies on schol...Purpose:This study explores how academic mobility influenced research productivity and citation impactamong Ukrainian scholars from 2005 to 2023.It examines the effects of evolving research assessmentpolicies on scholarly publishing,particularly in the context of institutional affiliations and national versusinternational mobility.Design/methodology/approach:Using publication and citation data from the Dimensions database,thisstudy analyses two cohorts of Ukrainian scholars who began publishing during 2005–2013 and 2014–2023.The analysis categorises scholars by affiliation type(universities vs.National Academy of Sciences ofUkraine),and mobility status(non-mobile,nationally mobile,and internationally mobile),enabling acomparative evaluation of research output and impact.Findings:The findings reveal a structural shift in affiliations,with a sharp increase in university-affiliatedscholars and a decline in the National Academy of Sciences of Ukraine scholars.Although research outputgrew,citation impact remained low,particularly among non-mobile scholars.International mobilityconsistently correlates with higher productivity and impact,while national mobility yields modest gains,likely due to uniform constraints across domestic institutions.The post-2013 policy changes increasedpublication volume but not citation impact,as they focused primarily on quantity,not quality.Furthermore,Russo-Ukrainian war has severely constrained academic work.Research limitations:The analysis is limited by the bibliometric database coverage and the reliance oninitial and final affiliations as a proxy for mobility,which may overlook interim mobility events.Practical implications:The findings suggest that research policy in Ukraine should go beyond incentivisingpublication counts and instead foster quality through enhanced infrastructure,sustained funding,andexpanded international collaboration opportunities.Originality/value:This study offers new insights into how mobility patterns interact with institutionalstructures and national policy in a war-affected research system.展开更多
Photodynamic therapy(PDT)is an emerging minimally invasive therapeutic modality that relies on the activation of a photosensitizing agent by light of a specific wavelength in the presence of molecular oxygen,leading t...Photodynamic therapy(PDT)is an emerging minimally invasive therapeutic modality that relies on the activation of a photosensitizing agent by light of a specific wavelength in the presence of molecular oxygen,leading to the generation of reactive oxygen species(ROS).This mechanism facilitates selective cytotoxic effects within pathological tissues and has demonstrated therapeutic potential across diverse disease contexts.However,the broader clinical applications remain limited by photosensitizer selectivity,shallow light penetration,and the risk of off-target cytotoxicity.Recent advancements in PDT have focused on the development of next-generation photosensitizers,the integration of nanotechnology for enhanced delivery and targeting,and the strategic combination of PDT with complementary therapeutic approaches.Experimental animal models play a crucial role in validating the efficacy and safety of PDT,optimizing its therapeutic parameters,and determining its mechanisms of action.This review provides a comprehensive overview of PDT applications in various disease models,including oncological,infectious,and nonconventional indications.Special emphasis is placed on the importance of large animal models in PDT research,such as rabbits,pigs,dogs,and non-human primates,which provide experimental platforms that more closely resemble human physiological and pathological states.The use of these models for understanding the mechanisms of PDT,optimizing therapeutic regimens,and evaluating clinical outcomes is also discussed.This review aims to inform future directions in PDT research and emphasizes the importance of selecting appropriate preclinical animal models to facilitate successful clinical translation.展开更多
We propose that the core mass function(CMF)can be driven by filament fragmentation.To model a star-forming system of filaments and fibers,we develop a fractal and turbulent tree with a fractal dimension of 2 and a Lar...We propose that the core mass function(CMF)can be driven by filament fragmentation.To model a star-forming system of filaments and fibers,we develop a fractal and turbulent tree with a fractal dimension of 2 and a Larson's law exponent(β)of 0.5.The fragmentation driven by convergent flows along the splines of the fractal tree yields a Kroupa-IMF-like CMF that can be divided into three power-law segments with exponentsα=-0.5,-1.5,and-2,respectively.The turnover masses of the derived CMF are approximately four times those of the Kroupa IMF,corresponding to a star formation efficiency of 0.25.Adoptingβ=1/3,which leads to fractional Brownian motion along the filament,may explain a steeper CMF at the high-mass end,withα=-3.33 close to that of the Salpeter IMF.We suggest that the fibers of the tree are basic building blocks of star formation,with similar properties across different clouds,establishing a common density threshold for star formation and leading to a universal CMF.展开更多
目的对比研究三维定量冠状动脉造影(3D QCA)、二维定量冠状动脉造影(2D QCA)与目测法在评估冠脉X射线造影靶病变血管的差异性。方法回顾性随机抽取2009年5月～2009年11月于我院接受冠状动脉造影并行介入治疗的60位患者65处靶病变血管段...目的对比研究三维定量冠状动脉造影(3D QCA)、二维定量冠状动脉造影(2D QCA)与目测法在评估冠脉X射线造影靶病变血管的差异性。方法回顾性随机抽取2009年5月～2009年11月于我院接受冠状动脉造影并行介入治疗的60位患者65处靶病变血管段的影像资料。分析比较Medis 3D QCA、西门子2D QCA、专家目测对靶病变管腔面积狭窄率、病变血管长度、参考血管直径的测量值,分析比较3D QCA、2DQCA的直径狭窄率测量值。结果冠脉X射线造影三维定量分析、二维定量分析、目测定量分析在成功三维重建62处(3例因靶血管少一个投照体位无法实现三维重建)靶病变中最窄处管腔面积狭窄率(%)(73.87±8.98 vs 79.10±8.06 vs 83.53±8.19,P<0.001)、长度(mm)(28.95±17.31 vs 26.20±16.04vs 27.21±16.58,P<0.001)、参考血管直径(mm)(2.67±0.29 vs 2.64±0.26 vs 2.76±0.29,P<0.001)有显著性差异,三维与二维对靶血管病变最窄处直径狭窄率(%)(54.21±9.48 vs 57.84±10.17,P=0.016)有显著性差异。结论 Medis 3D QCA对冠状动脉造影能成功实现三维重建,与专家目测和二维定量分析相比,三维定量分析系统能够恢复三维血管形态从而更准确地分析冠状动脉病变。展开更多
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Background Cardiovascular disease (CVD) is mainly caused by atherosclerosis,for which dyslipidemia (i.e. high (V)LDL-cholesterol (C),high triglycerides and low HDLC is a major risk factor. To reduce the risk to develop CVD,patients with dyslipidemia are usually treated with lipid-lowering drugs including statins and fibrates.
文摘Purpose： To get a better understanding of the way in which university rankings are used.Design/methodology/approach： Detailed analysis of the activities of visitors of the website of the CWTS Leiden Ranking.Findings： Visitors of the Leiden Ranking website originate disproportionally from specific countries. They are more interested in impact indicators than in collaboration indicators, while they are about equally interested in size-dependent indicators and size-independent indicators. Many visitors do not seem to realize that they should decide themselves which criterion they consider most appropriate for ranking universities.Research limitations： The analysis is restricted to the website of a single university ranking. Moreover, the analysis does not provide any detailed insights into the motivations of visitors of university ranking websites.Practical implications： The Leiden Ranking website may need to be improved in order to make more clear to visitors that they should decide themselves which criterion they want to use for ranking universities.Originality/value： This is the first analysis of the activities of visitors of a university ranking website.
基金supported in part by National Natural Science Foundation of China(62441605)。
文摘Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.
基金This project received funding through the MSCA4Ukraine project,which is funded by the European Union.
文摘Purpose:This study explores how academic mobility influenced research productivity and citation impactamong Ukrainian scholars from 2005 to 2023.It examines the effects of evolving research assessmentpolicies on scholarly publishing,particularly in the context of institutional affiliations and national versusinternational mobility.Design/methodology/approach:Using publication and citation data from the Dimensions database,thisstudy analyses two cohorts of Ukrainian scholars who began publishing during 2005–2013 and 2014–2023.The analysis categorises scholars by affiliation type(universities vs.National Academy of Sciences ofUkraine),and mobility status(non-mobile,nationally mobile,and internationally mobile),enabling acomparative evaluation of research output and impact.Findings:The findings reveal a structural shift in affiliations,with a sharp increase in university-affiliatedscholars and a decline in the National Academy of Sciences of Ukraine scholars.Although research outputgrew,citation impact remained low,particularly among non-mobile scholars.International mobilityconsistently correlates with higher productivity and impact,while national mobility yields modest gains,likely due to uniform constraints across domestic institutions.The post-2013 policy changes increasedpublication volume but not citation impact,as they focused primarily on quantity,not quality.Furthermore,Russo-Ukrainian war has severely constrained academic work.Research limitations:The analysis is limited by the bibliometric database coverage and the reliance oninitial and final affiliations as a proxy for mobility,which may overlook interim mobility events.Practical implications:The findings suggest that research policy in Ukraine should go beyond incentivisingpublication counts and instead foster quality through enhanced infrastructure,sustained funding,andexpanded international collaboration opportunities.Originality/value:This study offers new insights into how mobility patterns interact with institutionalstructures and national policy in a war-affected research system.
基金supported by the China Postdoctoral Science Foundation(2024M751098,2024M761134)Jilin Province Development and Reform Commission Program(ZKJCFGW2023015)+1 种基金Wenzhou Science&Technology Bureau Basic Public Welfare Research Program(Y20240006)Jilin University Young Teachers and Students Cross-disciplinary Training Project(2023-JCXK-08)。
文摘Photodynamic therapy(PDT)is an emerging minimally invasive therapeutic modality that relies on the activation of a photosensitizing agent by light of a specific wavelength in the presence of molecular oxygen,leading to the generation of reactive oxygen species(ROS).This mechanism facilitates selective cytotoxic effects within pathological tissues and has demonstrated therapeutic potential across diverse disease contexts.However,the broader clinical applications remain limited by photosensitizer selectivity,shallow light penetration,and the risk of off-target cytotoxicity.Recent advancements in PDT have focused on the development of next-generation photosensitizers,the integration of nanotechnology for enhanced delivery and targeting,and the strategic combination of PDT with complementary therapeutic approaches.Experimental animal models play a crucial role in validating the efficacy and safety of PDT,optimizing its therapeutic parameters,and determining its mechanisms of action.This review provides a comprehensive overview of PDT applications in various disease models,including oncological,infectious,and nonconventional indications.Special emphasis is placed on the importance of large animal models in PDT research,such as rabbits,pigs,dogs,and non-human primates,which provide experimental platforms that more closely resemble human physiological and pathological states.The use of these models for understanding the mechanisms of PDT,optimizing therapeutic regimens,and evaluating clinical outcomes is also discussed.This review aims to inform future directions in PDT research and emphasizes the importance of selecting appropriate preclinical animal models to facilitate successful clinical translation.
基金support of the Strategic Priority Research Program of the Chinese Academy of Sciences under grant No.XDB0800303the National Key R&D Program of China under grant No.2022YFA1603100the National Natural Science Foundation of China(NSFC,Grant No.12203086)。
文摘We propose that the core mass function(CMF)can be driven by filament fragmentation.To model a star-forming system of filaments and fibers,we develop a fractal and turbulent tree with a fractal dimension of 2 and a Larson's law exponent(β)of 0.5.The fragmentation driven by convergent flows along the splines of the fractal tree yields a Kroupa-IMF-like CMF that can be divided into three power-law segments with exponentsα=-0.5,-1.5,and-2,respectively.The turnover masses of the derived CMF are approximately four times those of the Kroupa IMF,corresponding to a star formation efficiency of 0.25.Adoptingβ=1/3,which leads to fractional Brownian motion along the filament,may explain a steeper CMF at the high-mass end,withα=-3.33 close to that of the Salpeter IMF.We suggest that the fibers of the tree are basic building blocks of star formation,with similar properties across different clouds,establishing a common density threshold for star formation and leading to a universal CMF.
文摘目的对比研究三维定量冠状动脉造影(3D QCA)、二维定量冠状动脉造影(2D QCA)与目测法在评估冠脉X射线造影靶病变血管的差异性。方法回顾性随机抽取2009年5月～2009年11月于我院接受冠状动脉造影并行介入治疗的60位患者65处靶病变血管段的影像资料。分析比较Medis 3D QCA、西门子2D QCA、专家目测对靶病变管腔面积狭窄率、病变血管长度、参考血管直径的测量值,分析比较3D QCA、2DQCA的直径狭窄率测量值。结果冠脉X射线造影三维定量分析、二维定量分析、目测定量分析在成功三维重建62处(3例因靶血管少一个投照体位无法实现三维重建)靶病变中最窄处管腔面积狭窄率(%)(73.87±8.98 vs 79.10±8.06 vs 83.53±8.19,P<0.001)、长度(mm)(28.95±17.31 vs 26.20±16.04vs 27.21±16.58,P<0.001)、参考血管直径(mm)(2.67±0.29 vs 2.64±0.26 vs 2.76±0.29,P<0.001)有显著性差异,三维与二维对靶血管病变最窄处直径狭窄率(%)(54.21±9.48 vs 57.84±10.17,P=0.016)有显著性差异。结论 Medis 3D QCA对冠状动脉造影能成功实现三维重建,与专家目测和二维定量分析相比,三维定量分析系统能够恢复三维血管形态从而更准确地分析冠状动脉病变。
