Background There is insufficient evidence to provide recommendations for leisure-time physical activity among workers across various occupational physical activity levels.This study aimed to assess the association of ...Background There is insufficient evidence to provide recommendations for leisure-time physical activity among workers across various occupational physical activity levels.This study aimed to assess the association of leisure-time physical activity with cardiovascular and all-cause mortality across occupational physical activity levels.Methods This study utilized individual participant data from 21 cohort studies,comprising both published and unpublished data.Eligibility criteria included individual-level data on leisure-time and occupational physical activity(categorized as sedentary,low,moderate,and high)along with data on all-cause and/or cardiovascular mortality.A 2-stage individual participant data meta-analysis was conducted,with separate analysis of each study using Cox proportional hazards models(Stage 1).These results were combined using random-effects models(Stage 2).Results Higher leisure-time physical activity levels were associated with lower all-cause and cardiovascular mortality risk across most occupational physical activity levels,for both males and females.Among males with sedentary work,high compared to sedentary leisure-time physical activity was associated with lower all-cause(hazard ratios(HR)=0.77,95%confidence interval(95%CI):0.70-0.85)and cardiovascular mortality(HR=0.76,95%CI:0.66-0.87)risk.Among males with high levels of occupational physical activity,high compared to sedentary leisure-time physical activity was associated with lower all-cause(HR=0.84,95%CI:0.74-0.97)and cardiovascular mortality(HR=0.79,95%CI:0.60-1.04)risk,while HRs for low and moderate levels of leisure-time physical activity ranged between 0.87 and 0.97 and were not statistically significant.Among females,most effects were similar but more imprecise,especially in the higher occupational physical activity levels.Conclusion Higher levels of leisure-time physical activity were generally associated with lower mortality risks.However,results for workers with moderate and high occupational physical activity levels,especially women,were more imprecise.Our findings suggests that workers may benefit from engaging in high levels of leisure-time physical activity,irrespective of their level of occupational physical activity.展开更多
Background:Despite the well-established health benefits of physical activity(PA)for young people(aged 419 years),most do not meet PA guidelines.Policies that support PA in schools may be promising,but their impact on ...Background:Despite the well-established health benefits of physical activity(PA)for young people(aged 419 years),most do not meet PA guidelines.Policies that support PA in schools may be promising,but their impact on PA behavior is poorly understood.The aim of this systematic review was to ascertain the level and type of evidence reported in the international scientific literature for policies within the school setting that contribute directly or indirectly to increasing PA.Methods:This systematic review is compliant with Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines.Six databases were searched using key concepts of policy,school,evaluation,and PA.Following title and abstract screening of 2323 studies,25 progressed to data synthesis.Methodological quality was assessed using standardized tools,and the strength of the evidence of policy impact was described based on pre-determined codes:positive,negative,inconclusive,or untested statistically.Results:Evidence emerged for 9 policy areas that had a direct or indirect effect on PA within the school setting.These were whole school PA policy,physical education,sport/extracurricular PA,classroom-based PA,active breaks/recess,physical environment,shared use agreements,active school transport,and surveillance.The bulk of the evidence was significantly positive(54%),27%was inconclusive,9%was significantly negative,and 11%was untested(due to rounding,some numbers add to 99%or 101%).Frequency of evidence was highest in the primary setting(41%),34%in the secondary setting,and 24%in primary/secondary combined school settings.By policy area,frequency of evidence was highest for sport/extracurricular PA(35%),17%for physical education,and 12%for whole school PA policy,with evidence for shared use agreements between schools and local communities rarely reported(2%).Comparing relative strength of evidence,the evidence for shared use agreements,though sparse,was 100%positive,while 60%of the evidence for whole school PA policy,59%of the evidence for sport/extracurricular PA,57%of the evidence for physical education,50%of the evidence for PA in classroom,and 50%of the evidence for active breaks/recess were positive.Conclusion:The current evidence base supports the effectiveness of PA policy actions within the school setting but cautions against a“one-sizefits-all”approach and emphasizes the need to examine policy implementation to maximize translation into practice.Greater clarity regarding terminology,measurement,and methods for evaluation of policy interventions is needed.展开更多
BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highli...BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highlighting the need for non-invasive alternatives.AIM To investigate extracellular vesicles(EVs)as potential biomarkers for diagnosing and staging steatosis in patients with MASLD using machine learning(ML)and explainable artificial intelligence(XAI).METHODS In this single-center observational study,798 patients with metabolic dysfunction were enrolled.Of these,194 met the eligibility criteria,and 76 successfully completed all study procedures.Transient elastography was used for steatosis and fibrosis staging,and circulating plasma EV characteristics were analyzed through nanoparticle tracking.Twenty ML models were developed:Six to differentiate non-steatosis(S0)from steatosis(S1-S3);and fourteen to identify severe steatosis(S3).Models utilized EV features(size and concentration),clinical(advanced fibrosis and presence of type 2 diabetes mellitus),and anthropomorphic(sex,age,height,weight,body mass index)data.Their performance was assessed using receiver operating characteristic(ROC)-area under the curve(AUC),specificity,and sensitivity,while correlation and XAI analysis were also conducted.RESULTS The CatBoost C1a model achieved an ROC-AUC of 0.71/0.86(train/test)on average across ten random five-fold cross-validations,using EV features alone to distinguish S0 from S1-S3.The CatBoost C2h-21 model achieved an ROC-AUC of 0.81/1.00(train/test)on average across ten random three-fold cross-validations,using engineered features including EVs,clinical features like diabetes and advanced fibrosis,and anthropomorphic data like body mass index and weight for identifying severe steatosis(S3).Key predictors included EV mean size and concentration.Correlation,XAI,and SHapley Additive exPlanations analysis revealed non-linear feature relationships with steatosis stages.CONCLUSION The EV-based ML models demonstrated that the mean size and concentration of circulating plasma EVs constituted key predictors for distinguishing the absence of significant steatosis(S0)in patients with metabolic dysfunction,while the combination of EV,clinical,and anthropomorphic features improved the diagnostic accuracy for the identification of severe steatosis.The algorithmic approach using ML and XAI captured non-linear patterns between disease features and provided interpretable MASLD staging insights.However,further large multicenter studies,comparisons,and validation with histopathology and advanced imaging methods are needed.展开更多
Ovo-like transcriptional repressor 1(OVOL1)is a key mediator of epithelial lineage determination and mesenchymal–epithelial transition(MET).The cytokines transforming growth factor-β(TGF-β)and bone morphogenetic pr...Ovo-like transcriptional repressor 1(OVOL1)is a key mediator of epithelial lineage determination and mesenchymal–epithelial transition(MET).The cytokines transforming growth factor-β(TGF-β)and bone morphogenetic proteins(BMP)control the epithelial–mesenchymal plasticity(EMP)of cancer cells,but whether this occurs through interplay with OVOL1 is not known.Here,we show that OVOL1 is inversely correlated with the epithelial–mesenchymal transition(EMT)signature,and is an indicator of a favorable prognosis for breast cancer patients.OVOL1 suppresses EMT.展开更多
基金The Trùndelag Health Study (HUNT) is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology), Trùndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public HealthThe coordination of European Prospective Investigation into Cancer and Nutrition - Spain study (EPIC) is financially supported by the International Agency for Research on Cancer (IARC)+7 种基金by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC)supported by Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andaluc 1a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain)funded by The Netherlands Organisation for Health Research and DevelopmentZon Mw (Grant No.: 531-00141-3)Funding for the SHIP study has been provided by the Federal Ministry for Education and Research (BMBFidentification codes 01 ZZ96030, 01 ZZ0103, and 01 ZZ0701)support from the Swedish Research Council (2018-02527 and 2019-00193)financed by the Helmholtz Zentrum München - German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria.
文摘Background There is insufficient evidence to provide recommendations for leisure-time physical activity among workers across various occupational physical activity levels.This study aimed to assess the association of leisure-time physical activity with cardiovascular and all-cause mortality across occupational physical activity levels.Methods This study utilized individual participant data from 21 cohort studies,comprising both published and unpublished data.Eligibility criteria included individual-level data on leisure-time and occupational physical activity(categorized as sedentary,low,moderate,and high)along with data on all-cause and/or cardiovascular mortality.A 2-stage individual participant data meta-analysis was conducted,with separate analysis of each study using Cox proportional hazards models(Stage 1).These results were combined using random-effects models(Stage 2).Results Higher leisure-time physical activity levels were associated with lower all-cause and cardiovascular mortality risk across most occupational physical activity levels,for both males and females.Among males with sedentary work,high compared to sedentary leisure-time physical activity was associated with lower all-cause(hazard ratios(HR)=0.77,95%confidence interval(95%CI):0.70-0.85)and cardiovascular mortality(HR=0.76,95%CI:0.66-0.87)risk.Among males with high levels of occupational physical activity,high compared to sedentary leisure-time physical activity was associated with lower all-cause(HR=0.84,95%CI:0.74-0.97)and cardiovascular mortality(HR=0.79,95%CI:0.60-1.04)risk,while HRs for low and moderate levels of leisure-time physical activity ranged between 0.87 and 0.97 and were not statistically significant.Among females,most effects were similar but more imprecise,especially in the higher occupational physical activity levels.Conclusion Higher levels of leisure-time physical activity were generally associated with lower mortality risks.However,results for workers with moderate and high occupational physical activity levels,especially women,were more imprecise.Our findings suggests that workers may benefit from engaging in high levels of leisure-time physical activity,irrespective of their level of occupational physical activity.
文摘Background:Despite the well-established health benefits of physical activity(PA)for young people(aged 419 years),most do not meet PA guidelines.Policies that support PA in schools may be promising,but their impact on PA behavior is poorly understood.The aim of this systematic review was to ascertain the level and type of evidence reported in the international scientific literature for policies within the school setting that contribute directly or indirectly to increasing PA.Methods:This systematic review is compliant with Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines.Six databases were searched using key concepts of policy,school,evaluation,and PA.Following title and abstract screening of 2323 studies,25 progressed to data synthesis.Methodological quality was assessed using standardized tools,and the strength of the evidence of policy impact was described based on pre-determined codes:positive,negative,inconclusive,or untested statistically.Results:Evidence emerged for 9 policy areas that had a direct or indirect effect on PA within the school setting.These were whole school PA policy,physical education,sport/extracurricular PA,classroom-based PA,active breaks/recess,physical environment,shared use agreements,active school transport,and surveillance.The bulk of the evidence was significantly positive(54%),27%was inconclusive,9%was significantly negative,and 11%was untested(due to rounding,some numbers add to 99%or 101%).Frequency of evidence was highest in the primary setting(41%),34%in the secondary setting,and 24%in primary/secondary combined school settings.By policy area,frequency of evidence was highest for sport/extracurricular PA(35%),17%for physical education,and 12%for whole school PA policy,with evidence for shared use agreements between schools and local communities rarely reported(2%).Comparing relative strength of evidence,the evidence for shared use agreements,though sparse,was 100%positive,while 60%of the evidence for whole school PA policy,59%of the evidence for sport/extracurricular PA,57%of the evidence for physical education,50%of the evidence for PA in classroom,and 50%of the evidence for active breaks/recess were positive.Conclusion:The current evidence base supports the effectiveness of PA policy actions within the school setting but cautions against a“one-sizefits-all”approach and emphasizes the need to examine policy implementation to maximize translation into practice.Greater clarity regarding terminology,measurement,and methods for evaluation of policy interventions is needed.
文摘BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highlighting the need for non-invasive alternatives.AIM To investigate extracellular vesicles(EVs)as potential biomarkers for diagnosing and staging steatosis in patients with MASLD using machine learning(ML)and explainable artificial intelligence(XAI).METHODS In this single-center observational study,798 patients with metabolic dysfunction were enrolled.Of these,194 met the eligibility criteria,and 76 successfully completed all study procedures.Transient elastography was used for steatosis and fibrosis staging,and circulating plasma EV characteristics were analyzed through nanoparticle tracking.Twenty ML models were developed:Six to differentiate non-steatosis(S0)from steatosis(S1-S3);and fourteen to identify severe steatosis(S3).Models utilized EV features(size and concentration),clinical(advanced fibrosis and presence of type 2 diabetes mellitus),and anthropomorphic(sex,age,height,weight,body mass index)data.Their performance was assessed using receiver operating characteristic(ROC)-area under the curve(AUC),specificity,and sensitivity,while correlation and XAI analysis were also conducted.RESULTS The CatBoost C1a model achieved an ROC-AUC of 0.71/0.86(train/test)on average across ten random five-fold cross-validations,using EV features alone to distinguish S0 from S1-S3.The CatBoost C2h-21 model achieved an ROC-AUC of 0.81/1.00(train/test)on average across ten random three-fold cross-validations,using engineered features including EVs,clinical features like diabetes and advanced fibrosis,and anthropomorphic data like body mass index and weight for identifying severe steatosis(S3).Key predictors included EV mean size and concentration.Correlation,XAI,and SHapley Additive exPlanations analysis revealed non-linear feature relationships with steatosis stages.CONCLUSION The EV-based ML models demonstrated that the mean size and concentration of circulating plasma EVs constituted key predictors for distinguishing the absence of significant steatosis(S0)in patients with metabolic dysfunction,while the combination of EV,clinical,and anthropomorphic features improved the diagnostic accuracy for the identification of severe steatosis.The algorithmic approach using ML and XAI captured non-linear patterns between disease features and provided interpretable MASLD staging insights.However,further large multicenter studies,comparisons,and validation with histopathology and advanced imaging methods are needed.
基金We acknowledge the support of the Chinese Scholarship Council(CSC)to Chuannan Fan and Qian Wang,and the Cancer Genomics Centre in the Netherlands(CGC,NL)and the ZonMW grant(09120012010061)to Peter ten Dijke.We thank Maarten van Dinther and Martijn Rabelink for excellent technical assistance and Slobodan Vukicevic(University of Zagreb,Croatia),and Andrew Hinck(University of Pittsburgh,USA)for generously providing human recombinant BMP6 and TGF-β3,respectively.We acknowledge A.G.Jochemsen for providing the FH1tUTG vector.We are grateful to Midory Thorikay for help with testing effect of compounds on OVOL1 expression and checking the expression of OVOL1 in breast cancer cell lines,Jing Zhang for the help with GSEA,Sijia Liu for instructions on how to perform the zebrafish xenograft assay,and all other members in ten Dijke’s laboratory and Yuva Oz for their kind support.
文摘Ovo-like transcriptional repressor 1(OVOL1)is a key mediator of epithelial lineage determination and mesenchymal–epithelial transition(MET).The cytokines transforming growth factor-β(TGF-β)and bone morphogenetic proteins(BMP)control the epithelial–mesenchymal plasticity(EMP)of cancer cells,but whether this occurs through interplay with OVOL1 is not known.Here,we show that OVOL1 is inversely correlated with the epithelial–mesenchymal transition(EMT)signature,and is an indicator of a favorable prognosis for breast cancer patients.OVOL1 suppresses EMT.
