BACKGROUND The burden of mental disorders(MD)in the Western Pacific Region(WPR)re-mains a critical public health concern,with substantial variations across demogra-phics and countries.AIM To analyze the burden of MD i...BACKGROUND The burden of mental disorders(MD)in the Western Pacific Region(WPR)re-mains a critical public health concern,with substantial variations across demogra-phics and countries.AIM To analyze the burden of MD in the WPR from 1990 to 2021,along with associated risk factors,to reveal changing trends and emerging challenges.METHODS We used data from the Global Burden of Disease 2021,analyzing prevalence,incidence,and disability-adjusted life years(DALYs)of MD from 1990 to 2021.Statistical methods included age-standardisation and uncertainty analysis to address variations in population structure and data completeness.RESULTS Between 1990 and 2021,the prevalence of MD rose from 174.40 million cases[95%uncertainty interval(UI):160.17-189.84]to 234.90 million cases(95%UI:219.04-252.50),with corresponding DALYs increasing from 22.8 million(95%UI:17.22-28.79)to 32.07 million(95%UI:24.50-40.68).During this period,the burden of MD shifted towards older age groups.Depressive and anxiety disorders were predominant,with females showing higher DALYs for depressive and anxiety disorders,and males more affected by conduct disorders,attention-deficit hyperactivity disorder,and autism spectrum disorders.Australia,New Zealand,and Malaysia reported the highest burdens,whereas Vietnam,China,and Brunei Darussalam reported the lowest.Additionally,childhood sexual abuse and bullying,and intimate partner violence emerged as significant risk factors.CONCLUSION This study highlights the significant burden of MD in the WPR,with variations by age,gender,and nation.The coronavirus disease 2019 pandemic has exacerbated the situation,emphasizing the need for a coordinated response.展开更多
Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that in...Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that integrates artificial intelligence(AI)with quantitative pathology image analysis,aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens.This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images.Pathomics integrates deep learning with histopathological features to enable precise tumour classification(e.g.,HCC vs cholangiocarcinoma),microvascular invasion(MVI)detection,recurrence risk stratification,and survival prediction.Advanced frameworks such as MVI-AI diagnostic model and CHOWDER demonstrate high accuracy in identifying prognostic biomarkers,whereas multiomics integration links morphometric patterns to molecular signatures(e.g.,EZH2 expression and immune infiltration).Despite these breakthroughs,critical bottlenecks persist,including limited multicentre validation studies,"black box"model interpretability,and clinical workflow integration.Future studies should emphasize AI-enhanced multimodal fusion(radiogenomics and liquid biopsy)and standardized platforms to bridge computational pathology and precision oncology,ultimately improving personalized therapeutic strategies for liver malignancies.This synthesis aims to guide research translation and advance personalized therapeutic strategies for liver malignancies.展开更多
基金Supported by National Key Research and Development Program of China,No.2022YFC3600903Key Discipline Project under Shanghai's Three-Year Action Plan for Strengthening the Public Health System(2023-2025),No.GWVI-11.1-44.
文摘BACKGROUND The burden of mental disorders(MD)in the Western Pacific Region(WPR)re-mains a critical public health concern,with substantial variations across demogra-phics and countries.AIM To analyze the burden of MD in the WPR from 1990 to 2021,along with associated risk factors,to reveal changing trends and emerging challenges.METHODS We used data from the Global Burden of Disease 2021,analyzing prevalence,incidence,and disability-adjusted life years(DALYs)of MD from 1990 to 2021.Statistical methods included age-standardisation and uncertainty analysis to address variations in population structure and data completeness.RESULTS Between 1990 and 2021,the prevalence of MD rose from 174.40 million cases[95%uncertainty interval(UI):160.17-189.84]to 234.90 million cases(95%UI:219.04-252.50),with corresponding DALYs increasing from 22.8 million(95%UI:17.22-28.79)to 32.07 million(95%UI:24.50-40.68).During this period,the burden of MD shifted towards older age groups.Depressive and anxiety disorders were predominant,with females showing higher DALYs for depressive and anxiety disorders,and males more affected by conduct disorders,attention-deficit hyperactivity disorder,and autism spectrum disorders.Australia,New Zealand,and Malaysia reported the highest burdens,whereas Vietnam,China,and Brunei Darussalam reported the lowest.Additionally,childhood sexual abuse and bullying,and intimate partner violence emerged as significant risk factors.CONCLUSION This study highlights the significant burden of MD in the WPR,with variations by age,gender,and nation.The coronavirus disease 2019 pandemic has exacerbated the situation,emphasizing the need for a coordinated response.
基金Supported by Wenzhou Municipal Science and Technology Bureau,No.Y20240109.
文摘Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that integrates artificial intelligence(AI)with quantitative pathology image analysis,aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens.This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images.Pathomics integrates deep learning with histopathological features to enable precise tumour classification(e.g.,HCC vs cholangiocarcinoma),microvascular invasion(MVI)detection,recurrence risk stratification,and survival prediction.Advanced frameworks such as MVI-AI diagnostic model and CHOWDER demonstrate high accuracy in identifying prognostic biomarkers,whereas multiomics integration links morphometric patterns to molecular signatures(e.g.,EZH2 expression and immune infiltration).Despite these breakthroughs,critical bottlenecks persist,including limited multicentre validation studies,"black box"model interpretability,and clinical workflow integration.Future studies should emphasize AI-enhanced multimodal fusion(radiogenomics and liquid biopsy)and standardized platforms to bridge computational pathology and precision oncology,ultimately improving personalized therapeutic strategies for liver malignancies.This synthesis aims to guide research translation and advance personalized therapeutic strategies for liver malignancies.
