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公共数据库临床研究Nature medicine

Phenomic profiles and disease patterns of 1.9 million participants from Our Future Health.

1 次引用趋势数据不足

“我们的未来健康”是一项旨在招募500万英国成年居民的前瞻性研究,以推动疾病预防、检测和治疗方法的发现与转化。目前已入组超过250万人,逾190万参与者具备基线表型数据。研究评估了自报健康相关行为、地理位置、诊断与用药、门急诊就诊、癌症登记及死因等表型,并将疾病模式与国家估计值及英国生物样本库队列进行比较。社会人口学、生活方式和健康相关特征总体反映英国人群模式,但除一个少数族裔群体外,其他少数族裔及社会经济最 deprived 群体代表性不足。多种主要自报疾病患病率高于国家估计值,其中心理健康问题(如抑郁和焦虑)尤为突出,且与英国生物样本库方向一致(r=0.78);与已知临床相关因素的关联在两个队列中均得到重复(r=0.80)。用药模式和癌症患病率呈现预期年龄梯度,肺癌率低于国家数据。随着招募推进,电子健康记录有助于明确疾病模式并系统评估偏倚。

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完整作者

Vincent J Straub、Stefania Benonisdottir、Giovanni Scotti Bentivoglio、Neil Wary、Robert Campbell、Augustine Kong、Melinda C Mills

作者机构

  • Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford and Nuffield College, Oxford, UK. vincent.straub@ndph.ox.ac.uk.
  • Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford and Nuffield College, Oxford, UK.
  • Institute of Physical Sciences, University of Iceland, Reykjavík, Iceland.
  • Department of Economics, Econometrics and Finance, University of Groningen, Groningen, the Netherlands.
  • Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford and Nuffield College, Oxford, UK. melinda.mills@demography.ox.ac.uk.
  • Department of Economics, Econometrics and Finance, University of Groningen, Groningen, the Netherlands. melinda.mills@demography.ox.ac.uk.
  • Department of Genetics, University Medical Centre Groningen, Groningen, the Netherlands. melinda.mills@demography.ox.ac.uk.

技术路线

数据/样本:Our Future Health前瞻性研究中>190万参与者的基线表型数据(自报健康行为、地理位置、诊断与用药、住院/门诊记录、癌症登记及死因) → 分析:描述性统计并与英国国家估计及UK Biobank队列比较疾病模式、计算相关性

最近 5 个完整年度引用趋势

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不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/30 06:05
公共数据库临床研究Journal of translational medicine

Comparative proteomics of human anterior lens capsules reveals ATP6V1A-associated lysosomal dysregulation in age-related cataract with primary angle-closure glaucoma.

0 次引用趋势数据不足

本研究比较了单纯年龄相关性白内障(ARC)与合并原发性闭角型青光眼(PACG)患者前囊膜的蛋白质组。采用数据非依赖采集蛋白质组学分析人前囊膜标本,最终纳入13例ARC和10例PACG样本,通过富集分析、网络分析和ROC分析筛选差异蛋白,并在人前囊膜铺片、细胞与动物应激模型、外周血单个核细胞及UK Biobank血浆蛋白质组数据中进一步验证ATP6V1A。结果共鉴定出1262个差异表达蛋白;PACG中表达降低的蛋白富集于蛋白稳态相关伴侣及neddylation网络,并汇聚于溶酶体功能障碍,ATP6V1A及其他V-ATP酶亚基、LAMP2和Rag GTP酶家族成员协同降低;表达升高的蛋白富集于细胞外基质重塑及脂质/聚糖代谢通路。ATP6V1A降低在人前囊膜铺片、压力应激晶状体上皮细胞、视网膜应激模型及PACG患者外周血单个核细胞中得到证实,药理学抑制V-ATP酶可在细胞活力明显丧失前破坏溶酶体稳态。结论认为,PACG相关前囊膜具有蛋白稳态受损及ATP6V1A相关溶酶体失调的分子特征。

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技术路线

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最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:24
临床队列临床研究Nature medicine

Prediction of maternal and infant outcomes from longitudinal electronic health records with a mother-child AI agent.

0 次引用趋势数据不足

研究开发了基于大语言模型的母婴人工智能助手MoChiAgent,其核心预测引擎MoChiFormer利用常规实验室检查等纵向电子健康记录数据,通过重建缺失检验值、减少批次效应和学习健康轨迹表征,预测母婴疾病风险。该引擎基于4,401,599次纵向临床就诊数据开发并内部评估,并在包含263,452次和23,192次就诊的独立母婴队列中外部验证。结果显示,MoChiFormer识别关键妊娠状况的曲线下面积分别为胎盘早剥0.89、胎膜早破0.89、早产0.91。配对母婴数据分析揭示了跨代风险关联,特定聚类母亲所生婴儿的新生儿黄疸风险比为2.81(95%置信区间2.60-3.03),血液系统疾病风险比为2.83(95%置信区间2.62-3.05)。将母亲妊娠期电子健康记录与婴儿记录整合,可改善对染色体异常和呼吸系统疾病等婴儿疾病的预测。研究提示MoChiAgent可提供具有临床相关性和可操作性的决策支持信息,以促进母婴风险分层照护。

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编辑短评

传统母婴预测模型多母子分离、缺少干预指引;MoChiAgent 依托 MoChiFormer 处理稀疏常规 EHR,联合母子配对数据捕捉跨代风险,同步预测多种不良妊娠与新生儿结局,并自动匹配循证干预方案,支撑全周期一体化母婴风险管理。

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完整作者

Sian Liu、Wenxin Zheng、Jin Kang、Tianyi Xu、Siming Chen、Gen Li、Junlong Li、Hang Wong、Meihao Wang、Xiaokai Bai、Changxi Hu、Cheng Tang、Shengwei Jin、Zixing Zou、Ieng Chong、Yuxing Lu、Io Nam Wong、Hui Xu、Charlotte L Zhang、Jingman Shi、Erhu Feng、Jinyu Gu、Zhuo Sun、Haibo Chen、Li Yang、Yuan Zhang、Xian Zhu、Huanhuan Huang、Xiuyuan Xu、Xue Li、Zhenhui Zhao、Hongbo Qi、Xinyu Lu、Ngaman Cheng、Sicheng Pan、Ning Sun、Yun Yin、Michelle Williams、Eric Oermann、John E J Rasko、Jin Li、Kai Wang、Kang Zhang、Hao Wu、Yubin Xia、Fanxin Zeng、International Consortium of Digital Twins in Healthcare and Medicine

作者机构

  • Center for Big Data and Intelligent Medicine, The First Affiliated Hospital of Chongqing Medical University; Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission and Chongqing Translational Medicine Center, Chongqing, China.
  • State Key Lab of Eye Health and Clinical Data Science Institute, Institute for Advanced Study on Eye Health and Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China.
  • Artificial Intelligence Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Macau, China.
  • Department of Radiology, Department of Anesthesia and Critical Care, Key Laboratory of Pediatric Anesthesiology, Ministry of Education, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
  • Guangzhou National Laboratory, Guangzhou, China.
  • Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing, China.
  • College of Computer Science, Sichuan University, Chengdu, China.
  • Department of Clinical Research Center, Dazhou Central Hospital and Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College, Dazhou, China.
  • Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Baichuan Inc., Beijing, China.
  • Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
  • Departments of Neurosurgery, Radiology and Data Science, Neuroscience Institute, NYU Langone Medical Center, New York University, New York, NY, USA.
  • Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.
  • College of Life Science, Fudan University, Shanghai, China. li_jin_lifescience@fudan.edu.cn.
  • Artificial Intelligence Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Macau, China. kaiwang314@gmail.com.
  • State Key Lab of Eye Health and Clinical Data Science Institute, Institute for Advanced Study on Eye Health and Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China. kang.zhang@gmail.com.
  • Artificial Intelligence Cross Disciplinary Research Institute and Faculty of Medicine, Macau University of Science and Technology, Macau, China. kang.zhang@gmail.com.
  • Guangzhou National Laboratory, Guangzhou, China. kang.zhang@gmail.com.
  • Center for Big Data and Intelligent Medicine, The First Affiliated Hospital of Chongqing Medical University; Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission and Chongqing Translational Medicine Center, Chongqing, China. wuhao@cqmu.edu.cn.
  • Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China. xiayubin@sjtu.edu.cn.
  • Department of Clinical Research Center, Dazhou Central Hospital and Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College, Dazhou, China. zengfx@pku.edu.cn.

技术路线

纵向EHR数据(4,401,599次临床就诊用于开发与内部评估;独立母婴队列263,452与23,192次就诊用于外部验证)→MoChiFormer重建缺失实验室值、减少批次效应并学习EHR表征,支持孕周/胎儿/婴儿年龄估计、健康轨迹建模与当前及未来疾病风险分层→外部独立母婴队列验证预测性能与跨代风险关联

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 12:05
临床队列临床研究Journal of veterinary internal medicine

Effects of a gluten-free hydrolyzed diet in dogs with paroxysmal dyskinesia: a prospective cohort study.

0 次引用趋势数据不足

23只客户饲养的阵发性运动障碍(PD)患犬接受无麸质水解饮食(GFHD)的前瞻性队列研究,于基线、90天和180天评估发作频率、胃肠道症状及血清抗麦胶蛋白肽IgG和抗转谷氨酰胺酶-2 IgA滴度。结果显示,发作中位数从基线6次降至180天时2次(P=0.0003);180天时17/23为应答者(5例优、12例良);胃肠道症状从15/23降至6/23,呕吐和腹泻显著减少(P=0.01)。各时间点发作频率与两种抗体滴度均无显著相关性(P>0.1),抗体结果前后无显著变化。结论认为,GFHD与多数患犬PD发作频率及胃肠道症状显著减少相关,但抗体滴度不能预测饮食反应,作为生物标志物价值有限。

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技术路线

暂未整理

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 07:00
临床队列临床研究JAMA

Electrocardiogram-Based Deep Learning to Prioritize Testing for Transthyretin Amyloid Cardiomyopathy.

0 次引用趋势数据不足

一项诊断准确性研究开发并验证了一种可在本地部署的人工智能系统,通过常规心电图图像识别需进一步评估转甲状腺素蛋白淀粉样心肌病(ATTR-CM)的患者。研究使用耶鲁纽黑文健康系统2015年8月至2023年6月的心电图数据建模,并进行时间验证及外部验证,外部验证纳入5个跨国队列和3个筛查队列。主要结局为心脏淀粉样核素显像或活检确诊ATTR-CM的受试者工作特征曲线下面积(AUROC)、敏感性、特异性及阳性/阴性预测值。开发队列纳入11 291例患者的28 174份心电图,其中293例ATTR-CM。内部验证AUROC为0.84,预设阈值下敏感性0.72、特异性0.86;在模拟ATTR-CM特征患者中AUROC为0.81,在转诊行核素显像者中为0.78。5个外部队列AUROC为0.78~0.89;筛查队列中SCAN-MP研究AUROC为0.76,两个CACTUS队列分别为0.79和0.91。探索性分析显示,AI-ECG序贯AI超声心动图使阳性预测值由0.24升至0.66,敏感性由0.84降至0.68。结论认为,该AI-ECG方法在跨国回顾性和筛查队列中可区分ATTR-CM,可能作为优先安排超声心动图和确诊影像检查的可及初筛步骤,但预期用途和校准仍需前瞻性评估。

为什么值得关注

编辑短评

ATTR-CM 漏诊严重、确诊检查稀缺昂贵;该深度学习模型基于常规 ECG 图像做风险评分,用于优先筛选高风险患者安排专项确诊检查,适合在富集高危人群中使用,而非全人群普筛。

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完整作者

Philip M Croon、Bruno Batinica、Lovedeep S Dhingra、Ryan B Choi、Evangelos K Oikonomou、Sumukh V Shankar、Veer Sangha、Robert M A van der Boon、Michelle Michels、Maarten van Ettinger、Peter-Paul Zwetsloot、Sergio Teruya、Cesia Gallegos Kattan、Edward J Miller、Navid Noory、Oscar M Westin、Sie K Fensman、Steen Hvitfeldt Poulsen、Avneet Singh、Sudarshan Balla、Anouk Achten、Charalambos Vlachopoulos、Alexios S Antonopoulos、Nico Bruining、Julian D Gillmore、Mathew S Maurer、Frederick L Ruberg、Marianna Fontana、Rohan Khera

作者机构

  • Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
  • Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, Connecticut.
  • Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
  • Thoraxcenter, Cardiovascular Institute, Erasmus Medical Center, Rotterdam, the Netherlands.
  • Division of Cardiology, Columbia University Irving Medical Center, New York, New York.
  • Heart Center, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.
  • Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
  • Department of Cardiology, Northwell Health, New York, New York.
  • Department of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown.
  • Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, the Netherlands.
  • National Amyloidosis Centre, University College London, Royal Free Hospital, London, United Kingdom.
  • Cardiogenetics Unit, First Department of Cardiology, National and Kapodistrian University of Athens, Hippokration Hospital, Athens, Greece.
  • Section of Cardiovascular Medicine and Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, Massachusetts.
  • Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut.
  • Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut.
  • Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut.
  • Associate Editor, JAMA.

技术路线

常规心电图图像或原始12导联信号→AI-ECG模型检测ATTR-CM(以心脏淀粉样变核素显像或活检确诊为参照)→内部时间验证、模拟特征压力测试及5个跨国外部队列与3个筛查队列验证;探索性串联AI-ECG与AI超声心动图筛查

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:54
AI 靶点发现临床研究Nature genetics

Prediction of human missense variant effects from functional evidence.

3 次引用趋势数据不足

研究旨在解决错义变异效应预测在研究与诊断遗传学中的关键瓶颈。现有预测器多依赖临床结局或群体模式,而非功能影响的直接测量,导致泛化性有限和数据循环性。为此,研究者提出FuncVEP系列变异效应预测器,基于多样化功能数据训练,用于预测错义变异的功能影响。FuncVEP可跨数据集泛化,在广泛基准测试中优于48种现有预测器,功能基准准确率由78.8%提升至84.6%,临床基准由90.1%提升至92.4%。在发现层面,研究在英国生物银行和西奈山百万健康发现计划中识别出210个新基因-表型关联,涉及494个与先天性免疫错误相关的基因;相比最先进预测器,FuncVEP显著提高了发现率。结论认为,FuncVEP为变异解读提供了稳健、可扩展的解决方案,可提升诊断精度并推进基因发现。

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编辑短评

针对错义变异预测依赖临床或群体模式导致泛化受限与数据循环的问题,本文构建基于多样功能数据训练的FuncVEP预测器。其在多基准上优于48个现有工具并提升基因-表型关联发现率,为变异解读提供更稳健可扩展的方案。

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完整作者

Barış Kayaalp、Kerem Çil、Clément Conil、Aurélie Cobat、Meltem Ece Kars、Yuval Itan、Jean-Laurent Casanova、Tayfun Özçelik

作者机构

  • Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey.
  • Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, France.
  • Imagine Institute, University of Paris, Paris, France.
  • St Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, Rockefeller University, New York, NY, USA.
  • Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Pediatric Immunology-Hematology Unit, Necker Hospital for Sick Children, Paris, France.
  • HHMI, Rockefeller University, New York, NY, USA.
  • Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey. tozcelik@bilkent.edu.tr.

技术路线

功能数据训练集 → FuncVEP模型构建

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:54
公共数据库临床研究World journal of surgical oncology

Association of different adiposity indices with breast cancer incidence and all-cause mortality: a nationwide cohort study.

0 次引用趋势数据不足

美国一项基于国家健康与营养调查(NHANES)2005—2016年周期数据的全国性队列研究,纳入12,829名20~85岁女性,评估相对脂肪量(RFM)、腰高比(WHtR)、体型指数(ABSI)、倒数体重指数(RPI)和锥度指数(CI)5项肥胖指标与乳腺癌发病及全因死亡的关系。中位随访101.4个月,期间发生367例乳腺癌、1,132例死亡。多因素校正后,ABSI与乳腺癌发病(OR=1.21,95%CI:1.02~1.44)及全因死亡(HR=1.22,95%CI:1.12~1.34)均呈显著正相关,在≥50岁女性中关联更强;CI亦与死亡正相关(HR=1.12);RFM、WHtR和RPI在≥50岁亚组中与乳腺癌呈负相关。ABSI对乳腺癌预测的AUC最高(0.606),剂量反应关系最一致。结论认为,5项肥胖指标中ABSI与乳腺癌发病和全因死亡的关联最强且最一致,在各年龄组中预测价值稳定,未来仍需更大规模前瞻性队列研究进一步探讨。

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最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:24
公共数据库临床研究Human genomics

Dietary-fibre intake, genetic factors, and the risk of nonalcoholic fatty liver disease: a prospective study in the UK Biobank.

0 次引用趋势数据不足

英国生物银行前瞻性研究纳入424008名基线无肝病参与者,采用经验证的食物频率问卷评估膳食纤维摄入,以肝脏脂肪含量遗传风险评分评价遗传风险,用Cox比例风险模型分析。平均随访12.1年发生4662例非酒精性脂肪性肝病。膳食纤维摄入最高五分位者风险低于最低五分位(HR 0.70,95%CI 0.61~0.80,趋势P<0.001),面包和谷物来源纤维关联较强。高遗传风险者发病风险升高(HR 1.82,95%CI 1.65~2.01),但各遗传风险组中较高纤维摄入均与较低风险相关;高遗传风险合并低纤维摄入者发病风险最高(HR 2.85,95%CI 2.20~3.70)。结论认为,较高膳食纤维摄入与非酒精性脂肪性肝病风险降低相关,该关联见于所有遗传风险组。

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最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 02:32
公共数据库临床研究Cell

Advancing cancer detection and treatment using longitudinal routine clinical data.

2 次引用趋势数据不足

肿瘤管理在诊疗连续过程中仍较分散,从晚期诊断、挽救治疗到非个体化监测均存在碎片化问题。为此,研究提出统一多模态Transformer模型Oncoformer,基于中国肿瘤多模态预测与监测研究(COMPASS)队列(367万人、1770万次就诊)训练,并在包括英国生物样本库在内的独立外部队列中验证。该模型整合纵向电子健康记录与胸部X线影像,可完成泛癌诊断(AUROC=0.956)、诊断前1年内未来癌症预测(AUROC=0.869)、肿瘤分期推断(平均AUROC>0.90)、患者特异性治疗反应预测及10种癌症的无复发生存分层(均p<0.01)。分期预测经术后病理终点独立验证,并显示集中于核心癌症基因组通路。Oncoformer通过将常规临床数据转化为癌症动态演化视图,为基于常规临床数据的风险知情癌症预测与治疗分层提供了框架。

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编辑短评

癌症是动态演化疾病,碎片化、单任务模型不匹配临床需求;Oncoformer 证明整合纵向常规临床 + 影像时序信息,能用一套模型实现提前预警、诊断、分期、疗效与复发风险分层,把静态筛查升级成动态个体化肿瘤全程监测。

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完整作者

Fei Liu、Kai Wang、Hui Xu、Cheng Tang、Xian Shen、Meihao Wang、Lei Yang、Li Yang、Li Liu、Changxi Hu、Gen Li、Wei Wu、Zixing Zou、Bingzhou Li、Sian Liu、Jin Kang、Jungho Kong、Ting Li、Io Nam Wong、Xiaoying Huang、Gang Chen、Wenyang Lu、Ian Ziyar、Charlotte L Zhang、Yiwen Sun、Weihong Lin、Caiwen Ou、Manson Fok、Taiwa Hou、Winston Wang、Kanmin Xue、Yun Yin、Hao Zhu、Jonathan Gootenberg、Omar O Abudayyeh、Michael Karin、Alexandre Loupy、John E J Rasko、Trey Ideker、Huiyan Luo、Eric Oermann、Kang Zhang、International Consortium of Digital Twins in Healthcare and Medicine

作者机构

  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Department of Urology, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Department of General Surgery, Department of Hepatobiliary Surgery, Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Department of Radiology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Key Laboratory of Intelligent Medical Imaging of Wenzhou, Healthcare Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Department of Orthopedics, Key Laboratory of Hepatosplenic Surgery of Ministry of Education, the First Affiliated Hospital, Harbin Medical University, Harbin, China.
  • Department of Respiratory and Critical Care Medicine, Center for Big Data and Intelligent Medicine, the First Affiliated Hospital of Chongqing Medical University, Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission, Chongqing Translational Medicine Center, Chongqing, China.
  • Department of Health Management, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Peking-Tsinghua Center for Life Sciences, Beijing, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
  • Departments of Medicine and Bioengineering, Moores Cancer Center, School of Medicine, UC San Diego, CA, USA.
  • Division of Pulmonary Medicine, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou Key Laboratory of Interdisciplinary and Translational Medicine, Wenzhou Key Laboratory of Heart and Lung, Wenzhou, Zhejiang, China.
  • Nepean Hospital, Sydney, Australia.
  • Key Laboratory of Intelligent Medical Imaging of Wenzhou, Healthcare Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Dongguan Hospital, Southern Medical University, Guangzhou, China.
  • Conde S. Januario Hospital, Macau, China.
  • Department of Internal Medicine, Mayo Clinic, Arizona, USA.
  • Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Faculty of Health and Wellness, Faculty of Business, City University of Macau, Taipa, Macau SAR, China.
  • Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Gene and Cell Therapy Institute, Mass General Brigham, Cambridge, MA, USA.
  • Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, F-75015 Paris, France; Department of Kidney Transplantation, Assistance Publique - Hôpitaux de Paris, Paris, France.
  • Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China.
  • Departments of Medicine and Bioengineering, Moores Cancer Center, School of Medicine, UC San Diego, CA, USA; Big Data Institute, Oxford University, Oxford, UK.
  • Department of Medical Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
  • Departments of Neurosurgery, Radiology, and Data Science, Neuroscience Institute, NYU Langone Medical Center, New York University, New York, NY, USA.
  • Clinical Data Science Institute, State Key Laboratory of Macromolecular Drugs and Large-scale Manufacturing, and The Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Artificial Intelligence Cross Disciplinary Research Institute, Faculty of Medicine, Macau University of Science and Technology, Macau, China; Guangzhou National Laboratory, Guangzhou, China; State Key Laboratory of Eye Health, Eye Hospital, Wenzhou Medical University, Wenzhou, China. Electronic address: kang.zhang@gmail.com.

技术路线

3.67 million individuals/17.7 million clinical visits (COMPASS cohort, longitudinal EHR + chest X-ray) → Oncoformer unified multimodal transformer analysis → validation on independent external cohorts including UK Biobank, with staging independently validated against postoperative pathological endpoints and convergence on core cancer genomic pathways

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 06:32
临床队列临床研究Nature medicine

Longitudinal plasma proteomics predict phenoconversion to clinically manifest ALS.

5 次引用趋势数据不足

一项纵向Olink Explore高通量血浆蛋白质组研究纳入33例表型转化者、35例ALS患者、10例症状前致病性变异携带者和59例对照的516份系列血浆样本,识别出92种在表型转化前浓度发生变化的蛋白质,并确定由19种蛋白质组成的核心组合;该组合可预测0.5至5年时间范围内的表型转化(交叉验证曲线下面积0.80-0.89),估算转化时间的平均绝对误差为1.6年。上述发现在英国生物银行数据中得到部分重复,证实NEFL、EDA2R和CA3等多种蛋白质在症状前升高,且多蛋白组合在估算表型转化时间方面优于单独使用NEFL。研究揭示症状前ALS的生物学特征,并基于纵向实证数据识别出一组新的易感性/风险生物标志物组合,有助于推进ALS预防目标。

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编辑短评

本研究针对ALS症状前表型转化难以预测的瓶颈,基于纵向血浆Olink蛋白质组学识别转化前变化蛋白并构建19蛋白组合,可预测0.5-5年转化时间,并在UK Biobank中部分重复。其为症状前ALS生物学及预防试验富集提供了候选风险标志物依据。

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完整作者

Ximing Ran、Joanne Wuu、Zhaohui S Qin、Michael P McDermott、Johnathan Cooper-Knock、Yindi Li、Volkan Granit、Anne-Laure Grignon、Elizabeth Lin、Maria Catalina Fernandez、Danielle Colato、Nathan Carberry、Christina M Lill、Paolo Piazza、Andrea Malaspina、Michael Benatar

作者机构

  • Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
  • Department of Computer Science, Emory University, Atlanta, GA, USA.
  • Department of Neurology and ALS Center, University of Miami Miller School of Medicine, Miami, FL, USA.
  • Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
  • Department of Neurology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
  • Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK.
  • NIHR Sheffield Biomedical Research Centre, Sheffield, UK.
  • Department of Neuromuscular Diseases, UCL Queen Square Motor Neuron Disease Center, UCL Queen Square Institute of Neurology, University College London, London, UK.
  • Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
  • Ageing and Epidemiology Unit (AGE), School of Public Health, Imperial College London, London, UK.
  • Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK.
  • Department of Neurology and ALS Center, University of Miami Miller School of Medicine, Miami, FL, USA. mbenatar@med.miami.edu.

技术路线

纵向血浆样本(33例表型转化者、35例ALS患者、10例症状前携带者、59例对照,共516份系列血浆)→Olink Explore高通量蛋白质组学分析→识别92个表型转化前浓度变化蛋白并刻画纵向轨迹→构建19蛋白核心组合,交叉验证AUC 0.80-0.89预测0.5-5年表型转化、平均绝对误差1.6年→UK Biobank数据部分重复验证(NEFL、EDA2R、CA3等症状前升高,多蛋白组合优于单用NEFL)

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/11/01 03:56
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