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Association Between Body Mass Index and Brain Health in Adults: A 16-Year Population-Based Cohort and Mendelian Randomization Study 成人体重指数与脑健康之间的关系:一项为期 16 年的基于人群的队列和孟德尔随机研究
Health data science Pub Date : 2024-03-01 DOI: 10.34133/hds.0087
Han Lv, Na Zeng, Mengyi Li, Jing Sun, Ning Wu, Mingze Xu, Qian Chen, Xinyu Zhao, Shuohua Chen, Wenjuan Liu, Xiaoshuai Li, Pengfei Zhao, Max Wintermark, Ying Hui, Jing Li, Shouling Wu, Zhenchang Wang
{"title":"Association Between Body Mass Index and Brain Health in Adults: A 16-Year Population-Based Cohort and Mendelian Randomization Study","authors":"Han Lv, Na Zeng, Mengyi Li, Jing Sun, Ning Wu, Mingze Xu, Qian Chen, Xinyu Zhao, Shuohua Chen, Wenjuan Liu, Xiaoshuai Li, Pengfei Zhao, Max Wintermark, Ying Hui, Jing Li, Shouling Wu, Zhenchang Wang","doi":"10.34133/hds.0087","DOIUrl":"https://doi.org/10.34133/hds.0087","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"82 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data. 学者在讨论 COVID-19 问题时的反应速度是否快于谷歌趋势?文本大数据的一种方法。
Health data science Pub Date : 2024-02-26 eCollection Date: 2024-01-01 DOI: 10.34133/hds.0116
Benson Shu Yan Lam, Amanda Man Ying Chu, Jacky Ngai Lam Chan, Mike Ka Pui So
{"title":"Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data.","authors":"Benson Shu Yan Lam, Amanda Man Ying Chu, Jacky Ngai Lam Chan, Mike Ka Pui So","doi":"10.34133/hds.0116","DOIUrl":"10.34133/hds.0116","url":null,"abstract":"<p><p><b>Background:</b> The COVID-19 pandemic has posed various difficulties for policymakers, such as the identification of health issues, establishment of policy priorities, formulation of regulations, and promotion of economic competitiveness. Evidence-based practices and data-driven decision-making have been recognized as valuable tools for improving the policymaking process. Nevertheless, due to the abundance of data, there is a need to develop sophisticated analytical techniques and tools to efficiently extract and analyze the data. <b>Methods:</b> Using Oxford COVID-19 Government Response Tracker, we categorize the policy responses into 6 different categories: (a) containment and closure, (b) health systems, (c) vaccines, (d) economic, (e) country, and (f) others. We proposed a novel research framework to compare the response times of the scholars and the general public. To achieve this, we analyzed more than 400,000 research abstracts published over the past 2.5 years, along with text information from Google Trends as a proxy for topics of public concern. We introduced an innovative text-mining method: coherent topic clustering to analyze the huge number of abstracts. <b>Results:</b> Our results show that the research abstracts not only discussed almost all of the COVID-19 issues earlier than Google Trends did, but they also provided more in-depth coverage. This should help policymakers identify core COVID-19 issues and act earlier. Besides, our clustering method can better reflect the main messages of the abstracts than a recent advanced deep learning-based topic modeling tool. <b>Conclusion:</b> Scholars generally have a faster response in discussing COVID-19 issues than Google Trends.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0116"},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10895931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Toward Unified AI Drug Discovery with Multimodal Knowledge. 利用多模态知识实现统一的人工智能药物发现。
Health data science Pub Date : 2024-02-23 eCollection Date: 2024-01-01 DOI: 10.34133/hds.0113
Yizhen Luo, Xing Yi Liu, Kai Yang, Kui Huang, Massimo Hong, Jiahuan Zhang, Yushuai Wu, Zaiqing Nie
{"title":"Toward Unified AI Drug Discovery with Multimodal Knowledge.","authors":"Yizhen Luo, Xing Yi Liu, Kai Yang, Kui Huang, Massimo Hong, Jiahuan Zhang, Yushuai Wu, Zaiqing Nie","doi":"10.34133/hds.0113","DOIUrl":"10.34133/hds.0113","url":null,"abstract":"<p><p><b>Background:</b> In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. <b>Methods:</b> In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. <b>Results:</b> Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. <b>Conclusions:</b> By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0113"},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10886071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Identification and analysis of sex-biased copy number alterations 性别差异拷贝数改变的鉴定和分析
Health data science Pub Date : 2024-02-21 DOI: 10.34133/hds.0121
Chenhao Zhang, Yang Yang, Qinghua Cui, Dongyu Zhao, Chunmei Cui
{"title":"Identification and analysis of sex-biased copy number alterations","authors":"Chenhao Zhang, Yang Yang, Qinghua Cui, Dongyu Zhao, Chunmei Cui","doi":"10.34133/hds.0121","DOIUrl":"https://doi.org/10.34133/hds.0121","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Digital Exclusion and Depressive Symptoms among Older People: Findings from Five Aging Cohort Studies across 24 Countries. 老年人的数字排斥和抑郁症状:来自24个国家的5项老年队列研究的结果
Health data science Pub Date : 2024-01-10 eCollection Date: 2025-01-01 DOI: 10.34133/hds.0218
Jingjing Wang, Xinran Lu, Sing Bik Cindy Ngai, Lili Xie, Xiaoyun Liu, Yao Yao, Yinzi Jin
{"title":"Digital Exclusion and Depressive Symptoms among Older People: Findings from Five Aging Cohort Studies across 24 Countries.","authors":"Jingjing Wang, Xinran Lu, Sing Bik Cindy Ngai, Lili Xie, Xiaoyun Liu, Yao Yao, Yinzi Jin","doi":"10.34133/hds.0218","DOIUrl":"10.34133/hds.0218","url":null,"abstract":"<p><p><b>Background:</b> Digital exclusion is a global issue that disproportionately affects older individuals especially in low- and middle-income nations. However, there is a wide gap in current research regarding the impact of digital exclusion on the mental health of older adults in both high-income and low- and middle-income countries. <b>Methods:</b> We analyzed data from 5 longitudinal cohorts: the Health and Retirement Study (HRS), the English Longitudinal Study of Aging (ELSA), the Survey of Health, Ageing and Retirement in Europe (SHARE), the China Health and Retirement Longitudinal Study (CHARLS), and the Mexican Health and Aging Study (MHAS). These cohorts consisted of nationwide samples from 24 countries. Digital exclusion was defined as the self-reported lack of access to the internet. Depressive symptoms were assessed using comparable scales across all cohorts. We used generalized estimating equation models, fitting a Poisson model, to investigate the association between the digital exclusion and depressive symptoms. We adjusted for the causal directed acyclic graph (DAG) minimal sufficient adjustment set (MSAS), which includes gender, age, retirement status, education, household wealth, social activities, and weekly contact with their children. <b>Results:</b> During the study period (2010-2018), 122,242 participants underwent up to 5 rounds of follow-up. Digital exclusion varied greatly across countries, ranging from 21.1% in Denmark to 96.9% in China. The crude model revealed a significant association between digital exclusion and depressive symptoms. This association remained statistically significant in the MSAS-adjusted model across all cohorts: HRS [incidence rate ratio (IRR), 1.37; 95% confidence interval (CI), 1.28 to 1.47], ELSA (IRR, 1.32; 95% CI, 1.23 to 1.41), SHARE (IRR, 1.30; 95% CI, 1.27 to 1.33), CHARLS (IRR, 1.62; 95% CI, 1.38 to 1.91), and MHAS (IRR, 1.31; 95% CI, 1.26 to 1.37); all <i>P</i>s < 0.001. Notably, this association was consistently stronger in individuals living in lower wealth quintile households across all 5 cohorts and among those who do not regularly interact with their children, except for ELSA. <b>Conclusions:</b> Digital exclusion is globally widespread among older adults. Older individuals who are digitally excluded are at a higher risk of developing depressive symptoms, particularly those with limited communication with their offspring and individuals living in lower wealth quintile households. Prioritizing the provision of internet access to older populations may help reduce the risks of depression symptoms, especially among vulnerable groups with limited familial support and with lower income.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"5 ","pages":"0218"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Loneliness and Social Isolation with Risk of Incident Non-alcoholic Fatty Liver Disease, UK Biobank 2006 to 2022. 孤独和社会隔离与非酒精性脂肪肝事件的风险,英国生物银行2006年至2022年。
Health data science Pub Date : 2024-01-07 eCollection Date: 2025-01-01 DOI: 10.34133/hds.0220
Ya Miao, Xiaoke Kong, Bin Zhao, Fang Fang, Jin Chai, Jiaqi Huang
{"title":"Loneliness and Social Isolation with Risk of Incident Non-alcoholic Fatty Liver Disease, UK Biobank 2006 to 2022.","authors":"Ya Miao, Xiaoke Kong, Bin Zhao, Fang Fang, Jin Chai, Jiaqi Huang","doi":"10.34133/hds.0220","DOIUrl":"10.34133/hds.0220","url":null,"abstract":"<p><p><b>Background:</b> Although loneliness and social isolation are proposed as important risk factors for metabolic diseases, their associations with the risk of non-alcoholic fatty liver disease (NAFLD) have not been elucidated. The aims of this study were to determine whether loneliness and social isolation are independently associated with the risk of NAFLD and to explore potential mediators for the observed associations. <b>Methods:</b> In this large prospective cohort analysis with 405,073 participants of the UK Biobank, the status of loneliness and social isolation was assessed through self-administrated questionnaires at study recruitment. The primary endpoint of interest was incident NAFLD. Multivariable-adjusted Cox proportional hazard regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals for the associations between loneliness, social isolation, and risk of NAFLD. <b>Results:</b> During a median follow-up of 13.6 years, there were 5,570 cases of NAFLD identified. In the multivariable-adjusted model, loneliness and social isolation were both statistically significantly associated with an increased risk of NAFLD (HR = 1.22 and 1.13, respectively). No significant multiplicative or additive interaction was found between loneliness and social isolation on the risk of NAFLD. The mediation analysis estimated that 30.4%, 16.2%, 5.3%, 4.1%, 10.5%, and 33.2% of the loneliness-NAFLD association was mediated by unhealthy lifestyle score, obesity, current smoking, irregular physical activity, suboptimal sleep duration, and depression, respectively. On the other hand, 25.6%, 10.1%, 15.5%, 10.1%, 8.1%, 11.6%, 9.6%, 4.8%, and 3.0% of the social isolation-NAFLD association was mediated by unhealthy lifestyle score, obesity, current smoking, irregular physical activity, suboptimal sleep duration, depression, C-reactive protein, count of white blood cells, and count of neutrophils, respectively. <b>Conclusions:</b> Our study demonstrated that loneliness and social isolation were associated with an elevated risk of NAFLD, independent of other important risk factors. These associations were partially mediated by lifestyle, depression, and inflammatory factors. Our findings substantiate the importance of loneliness and social isolation in the development of NAFLD.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"5 ","pages":"0220"},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Erratum to "Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study". 对“使用图神经网络检测有多重耐药肠杆菌科感染风险的患者:一项回顾性研究”的勘误。
Health data science Pub Date : 2023-12-16 eCollection Date: 2024-01-01 DOI: 10.34133/hds.0216
Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, Douglas Teodoro
{"title":"Erratum to \"Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study\".","authors":"Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, Douglas Teodoro","doi":"10.34133/hds.0216","DOIUrl":"10.34133/hds.0216","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.34133/hds.0099.].</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0216"},"PeriodicalIF":0.0,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11649199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Large-scale machine learning analysis reveals DNA-methylation and gene-expression response signatures for gemcitabine-treated pancreatic cancer 大规模机器学习分析揭示了吉西他滨治疗胰腺癌的DNA甲基化和基因表达反应特征
Health data science Pub Date : 2023-12-12 DOI: 10.34133/hds.0108
Adeolu Z Ogunleye, Chayanit Piyawajanusorn, G. Ghislat, Pedro Ballester
{"title":"Large-scale machine learning analysis reveals DNA-methylation and gene-expression response signatures for gemcitabine-treated pancreatic cancer","authors":"Adeolu Z Ogunleye, Chayanit Piyawajanusorn, G. Ghislat, Pedro Ballester","doi":"10.34133/hds.0108","DOIUrl":"https://doi.org/10.34133/hds.0108","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
See your stories: Visualisation for Narrative Medicine 看看你的故事叙事医学的可视化
Health data science Pub Date : 2023-12-04 DOI: 10.34133/hds.0103
Hua Ma, Xiaoru Yuan, Xu Sun, Glyn Lawson, Qingfeng Wang
{"title":"See your stories: Visualisation for Narrative Medicine","authors":"Hua Ma, Xiaoru Yuan, Xu Sun, Glyn Lawson, Qingfeng Wang","doi":"10.34133/hds.0103","DOIUrl":"https://doi.org/10.34133/hds.0103","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"12 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138603135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Using mobile-phone data to assess socio-economic disparities in unhealthy food reliance during the COVID-19 pandemic 利用手机数据评估 COVID-19 大流行期间不健康食品依赖的社会经济差异
Health data science Pub Date : 2023-11-30 DOI: 10.34133/hds.0101
Charles Alba, Ruopeng An
{"title":"Using mobile-phone data to assess socio-economic disparities in unhealthy food reliance during the COVID-19 pandemic","authors":"Charles Alba, Ruopeng An","doi":"10.34133/hds.0101","DOIUrl":"https://doi.org/10.34133/hds.0101","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139208949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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