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临床队列共 58 篇
临床队列临床研究BMJ (Clinical research ed.)

Simultaneous derivation, validation, and comparison of predictor hazard ratios for cardiovascular risk prediction equations in patients with diabetes from high versus non-high income countries: cohort study.

0 次引用趋势数据不足

一项基于人群的队列研究,比较新西兰(高收入国家)与中国(非高收入国家)30~74岁、无心血管疾病(CVD)的糖尿病患者,采用标准化方法分别建立两国5年CVD风险预测方程,并比较预测因子风险比。2004—2018年新西兰47 958名参与者中发生5 622例(11.7%)首次CVD事件;2010—2018年中国46 558名参与者中发生3 650例(7.8%)首次CVD事件。性别特异性方程包含16个预测因子,多数预测因子风险比相近,但年龄风险比差异明显(女性每增加10岁:新西兰1.61,95%CI 1.51~1.71;中国2.51,95%CI 2.32~2.72)。将新西兰方程应用于中国队列,标准重校准未改善校准(期望/观察比:男性0.938,女性0.809);以中国年龄系数替换新西兰年龄系数后校准明显改善(期望/观察比:男性1.027,女性1.025)。结论认为,将高收入国家CVD风险方程应用于非高收入国家糖尿病人群可能引入系统性误差,主要由年龄效应差异驱动;除简单重校准外,应考虑更新关键预测因子系数(尤其年龄),以提高风险分层准确性。

为什么值得关注

编辑短评

高收入国家开发的糖尿病心血管风险评分直接在其他国家使用时,仅靠简单重校准不够;年龄的风险效应在人群间差异很大,更新年龄这类关键预测因子的 HR,才能显著改善风险标定。

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完整作者

Jingyuan Liang、Yeunhyang Choi、Peng Shen、Susan Wells、Katrina Poppe、Zhangping Fu、Matire Harwood、Xiaofei Liu、Sue Crengle、Ting Wang、Corina Grey、Hongbo Lin、Suneela Mehta、Xun Tang、Rod Jackson、Pei Gao

作者机构

  • Section of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
  • Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
  • Department of Chronic Diseases and Health Promotion, Yinzhou District Centre for Disease Control and Prevention, Ningbo, China.
  • Department of General Practice and Primary Health Care, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
  • Department of Medicine, School of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
  • Ngāi Tahu Māori Health Research Unit, Division of Health Sciences, University of Otago, Dunedin, New Zealand.
  • Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
  • Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China peigao@bjmu.edu.cn.
  • Peking University Clinical Research Institute, Peking University, Beijing, China.

技术路线

电子健康记录队列数据(新西兰47,958人、中国46,558人)→按标准化方法分别推导两国性别特异性5年心血管风险预测方程并比较预测因子风险比→将新西兰方程应用于中国队列,评估标准化再校准前后的性能,再更新风险比存在显著差异的预测因子(如年龄系数)并验证校准改善

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 03:53
临床队列临床研究Nature medicine

A clinically-oriented foundation model for intraoperative pathology.

0 次引用趋势数据不足

研究基于十家医疗中心逾10万张冰冻切片构建了面向临床的术中病理基础模型CRISP,并在近100项回顾性诊断任务、逾1.5万张术中切片上完成系统评估,涵盖良恶性判别、术中关键决策及泛癌检测等。模型在6家机构、14种肿瘤类型和24个解剖部位(含既往未见部位及罕见癌)表现出稳健泛化能力。在逾3000例患者的前瞻性队列中,CRISP在真实条件下保持较高诊断准确性,直接为92.6%病例的手术决策提供依据;人机协作使诊断工作量减少35%,避免105项辅助检查,并将微转移灶检出准确率提升至87.5%。研究提示,CRISP为AI驱动的术中病理提供了面向临床的路径,有望支持手术决策并促进计算方法向临床实践转化。

为什么值得关注

编辑短评

通用病理基础模型多基于石蜡切片,难以适配冰冻切片伪影与术中场景;CRISP 以大规模冰冻切片预训练,经多中心前瞻性验证,可跨器官、跨肿瘤类型辅助术中病理判读,减轻病理医师负担、提升微转移检出能力,支撑外科实时决策。

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完整作者

Zihan Zhao、Fengtao Zhou、Ronggang Li、Bing Chu、Xinke Zhang、Xueyi Zheng、Ke Zheng、Xiaobo Wen、Jiabo Ma、Yihui Wang、Jiewei Chen、Chengyou Zheng、Jiangyu Zhang、Yongqin Wen、Jiajia Meng、Ziqi Zeng、Xiaoqing Li、Jing Li、Dan Xie、Yaping Ye、Yu Wang、Hao Chen、Muyan Cai

作者机构

  • Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Department of Gastric Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China.
  • Department of Pathology, Jiangmen Central Hospital, Jiangmen, China.
  • Department of Pathology, Zhongshan People's Hospital, Zhongshan, China.
  • Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
  • Department of Pathology, The Tenth Affiliated Hospital, Southern Medical University, Dongguan People's Hospital, Dongguan, China.
  • Department of Pathology, Huizhou Central People's Hospital, Huizhou, China.
  • Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
  • Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China. jhc@ust.hk.
  • State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • SIAT-HKUST Joint Laboratory for Brain Science, The Hong Kong University of Science and Technology, Hong Kong, China. jhc@ust.hk.
  • Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. caimy@sysucc.org.cn.

技术路线

数据/样本:来自10个医疗中心的逾10万张冰冻切片构建CRISP基础模型;分析:在近100项回顾性诊断任务、逾1.5万张术中切片上评估,并观察跨6家机构、14种肿瘤类型、24个解剖部位的泛化性;验证:3000余例患者的前瞻性队列验证,评估真实条件下诊断准确性、对手术决策的影响及人机协作效果

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 03:53
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。

为什么值得关注

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。

为什么值得关注

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完整作者

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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 07:00
临床队列综述Briefings in bioinformatics

Advances in computational biology approaches for digital modeling of tumor microenvironment.

0 次引用趋势数据不足

肿瘤微环境(TME)是异质性多尺度系统,仅靠实验难以完整刻画。计算建模可整合细胞、血管、代谢、免疫与力学机制,但方法多样,给模型选择、参数化、验证和临床转化带来困难。本文以问题为导向综述TME建模的计算方法,比较连续、离散及离散-连续混合范式在生物学分辨率、数据需求、可解释性和计算成本上的差异,并将其关联至肿瘤细胞动力学、血管运输、细胞外基质与力学、代谢与缺氧、免疫互作五个机制领域;从参数可识别性、不确定性量化、数值有效性、生物学保真度和临床相关性考察模型可信度,强调按预期用途和验证情境解读性能。文章还分析机器学习在机制-ML整合中的功能,包括代理模型加速前向模拟、反演方法估计潜参数与状态、患者特异性工作流整合多模态临床数据与机制模型,以及物理信息神经网络嵌入控制方程;并讨论纵向个性化、物理感知多尺度整合、临床可部署数字孪生、外部泛化和合成数据到临床迁移,提出从机制假设生成到自适应数字孪生决策支持的转化就绪路径。可信的TME建模应优先采用最小充分模型,明确预期用途,透明呈现不确定性,并使证据与生物学或临床问题相匹配。

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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 03:57
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
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
临床队列临床研究Paediatrics & child health

A prospective cohort study of serum sickness-like reactions in the pediatric population.

0 次引用趋势数据不足

一项前瞻性队列研究于2022年10月至2024年9月在加拿大伦敦儿童医院开展,纳入0至18岁表现为皮疹伴关节痛或水肿的患儿,旨在描述儿童血清病样反应(SSLR)的临床与实验室特征。27例患者中男性占59%,就诊时平均年龄4岁;阿莫西林是最常见诱因(85.2%),皮疹以环状形态最常见,超声排除了真性关节炎。皮疹约5天消退,关节痛约4天缓解;全身性激素可加速关节痛缓解,但未缩短病程。淋巴细胞毒性试验在58.3%的病例中确认药物因果关系,并与环状皮疹及T细胞活化标志物升高相关。研究认为,上述发现对儿童SSLR的诊断、管理和预后具有重要意义。

为什么值得关注

暂未整理

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完整作者

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作者机构

暂未整理

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:2026/10/31 11:42
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