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全部共 171 篇
合成生物学基础研究Nature methods

Decoding mechanoregulation in immunological synapses using biomimetic artificial cells.

0 次引用趋势数据不足

研究人员开发出一种基于生物材料的仿生人工细胞平台kpiCells,其采用仿生膜—内质网结构,可通过模块化的力学、化学和形貌输入,精细复制细胞状态。研究表明,kpiCells能够参与生理性细胞间相互作用并再现关键亚细胞特征。在T细胞系统中,该平台可整合研究传入的力学感知通路和传出的力学施加通路,支持在单个T细胞抗原受体上测量皮牛顿级力,并获得界定激活阈值的单细胞间力学指纹。该工作确立了kpiCells作为一种仿生模型,使合成材料设计的功能复杂性接近活细胞系统。

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编辑短评

该文针对现有生化与合成材料方法难以动态调控细胞间相互作用力学的问题,构建kpiCells仿生人工细胞平台,模拟细胞状态并参与生理性相互作用。其价值在于将机械、化学与形貌输入模块化整合,为解码免疫突触机械调控提供可控实验体系。

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完整作者

Xiaolei Yu、Vincent Mukwaya、Minxing Yue、Shuo Yang、Qian Li、Weili Zhao、Chunhai Fan、Lin Wang、Yingxi Zhao、Hongkai Yang、Jiacan Su、Li Wang、Hongjing Dou

作者机构

  • State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. mukvince@sjtu.edu.cn.
  • Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhang Jiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. liqian2018@sjtu.edu.cn.
  • State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhang Jiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Hebei Senlang Biotechnology, Shijiazhuang, China.
  • Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. dr_wangli@126.com.
  • State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. hjdou@sjtu.edu.cn.

技术路线

数据/样本:基于仿生膜-内质网架构构建的kpiCells人工细胞平台,作为可调机械、化学和形貌输入的仿生体系 → 分析:使kpiCells参与生理性细胞间相互作用,复现亚细胞特征;在T细胞系统中整合 interrogate 传入机械感知通路与传出力施加通路

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:2026/11/01 03:55
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
公共数据库基础研究Journal of translational medicine

Novel androgen receptor N-terminal mutation drives gastric cancer metastasis via YWHAZ-mediated FAK pathway activation.

0 次引用趋势数据不足

研究对象为胃癌及AR N端A32C(p.Y11S)突变。研究分析TCGA数据库33种肿瘤并在65例胃癌中验证,采用增殖、凋亡、迁移实验及转录组分析、免疫共沉淀、FAE226药物抑制和肝转移模型研究机制。结果显示,与野生型AR相比,A32C突变显著增强增殖、抗凋亡能力及G1/S期转换;Y11S位点替换增强AR与YWHAZ结合,形成细胞质支架,自发激活FAK-PXN轴及下游PI3K-AKT/ERK通路;敲低YWHAZ或抑制FAK可逆转恶性表型。体内实验中,MT-AR细胞肝脏定植能力高于野生型细胞,转移灶中FAK活性持续较高。结论认为,AR A32C突变是此前未被识别的致癌驱动因素,通过YWHAZ介导的配体非依赖性FAK激活机制促进胃癌转移,拓展了AR突变生物学研究范围,并提示基于AR突变状态的FAK抑制剂可作为AR突变胃癌的精准治疗。

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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:2026/11/01 03:24
公共数据库基础研究Journal of translational medicine

Chromogranin B promotes EMT-associated stem-like properties and chemoresistance in HNSCC.

0 次引用趋势数据不足

头颈部鳞状细胞癌(HNSCC)中,上皮-间质转化(EMT)相关干性与治疗耐药是推动疾病进展的重要因素,但其上游调控因子尚不明确。研究整合TCGA-HNSC、GEO及单细胞RNA测序数据,结合EMT评分、基因集富集分析、加权基因共表达网络分析和Cox回归,评估嗜铬粒蛋白B(CHGB)的表达、预后价值及其与EMT的关联,并通过功能获得/缺失、异种移植、肿瘤球形成、顺铂敏感性及挽救实验探究其生物学功能与机制。结果显示,CHGB在HNSCC组织中显著上调并与总生存期不良相关,其表达与EMT相关转录程序密切相关。敲低CHGB可抑制增殖、迁移、细胞周期进展、原代及继代肿瘤球形成、体外顺铂耐药和体内肿瘤生长,过表达则呈相反效应。机制上,CHGB通过PI3K/AKT信号维持ZEB1表达,促进EMT相关干性和化疗耐药;恢复ZEB1可部分挽救CHGB敲低所致的自我更新能力受损和顺铂敏感性增强,而沉默ZEB1可减弱CHGB驱动的恶性表型。单细胞转录组分析进一步证实CHGB富集于恶性细胞,并与EMT和干性程序正相关。结论认为,CHGB通过PI3K/AKT/ZEB1轴调控EMT相关干性和化疗耐药,是HNSCC潜在的预后生物标志物和治疗靶点。

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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
合成生物学综述Molecular cancer

Trace elements and tumors: homeostatic networks, molecular mechanisms and new horizons for precision intervention.

0 次引用趋势数据不足

痕量元素铁、铜、锌、硒、锰是生命必需物质,作为酶辅因子、结构组分和信号分子参与能量代谢、氧化还原平衡及基因组稳定性调控,其生理功能呈剂量-效应双相性,稳态失衡既可维护健康也可促进恶性转化。本综述系统阐述上述五种核心痕量元素在生理条件下的完整生命周期及其功能互联,并阐释肿瘤细胞通过元素成瘾机制劫持该调控网络。文章详述铁依赖性铁死亡与铜依赖性铜死亡的新兴作用,以及痕量元素对凋亡和自噬的情境依赖性调控,揭示肿瘤特异性代谢脆弱性。在肿瘤免疫微环境中,锌和硒通常支持抗肿瘤免疫,铁和铜失调驱动免疫抑制极化,锰则为具免疫治疗潜力的cGAS-STING激动剂。在血管生成与转移方面,铜和铁在细胞外基质重塑、上皮-间质转化及血管生成信号中发挥核心作用。临床证据表明,全身及局部元素失衡可作为多种癌症的风险生物标志物。治疗策略包括传统营养与药理干预、基于元素代谢的放化疗及免疫治疗协同增敏,以及元素响应纳米平台和合成生物学工程免疫细胞等交叉工程方法。多数治疗证据来自临床前模型,需严谨基础研究支撑临床转化。

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AI 辅助短评,基于标题和摘要整理

本文系统梳理铁、铜、锌、硒、锰的稳态网络与肿瘤代谢劫持机制,涵盖铁死亡、铜死亡及免疫微环境调控,并整合元素失衡作为风险标志物的临床证据。治疗策略分类清晰,但作者明确多数证据源于临床前模型,转化前需严谨基础研究。综述框架具系统生物学视角,知识缺口梳理有助于假设驱动探索,适合作为该领域参考。

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完整作者

Shengxin Zhang、Keqin Tan、Qin Zhang、Yuzhe Zhao、Ziyang Cheng、Xinggang Yang、Jingsi Dong、Jiaming Liu、Xuelei Ma

作者机构

  • Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Department of Postgraduate Students, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • Department of Hematology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
  • Lung Cancer Center/Lung Cancer Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China. dongjingsi@wchscu.edu.cn.
  • Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China. dongjingsi@wchscu.edu.cn.
  • Department of Urology, West China Hospital, Sichuan University, Chengdu, China. JM3099@163.com.
  • Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China. maxuelei0726@wchscu.cn.

技术路线

文献综述:整合基础与临床数据 → 归纳五种微量元素(铁、铜、锌、硒、锰)的稳态网络、分子机制及肿瘤特异性代谢脆弱性

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:2026/10/29 03:49
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
公共数据库临床研究Biology of sport

Associations of exercise snacks with sarcopenia and all-cause mortality: A national cohort study.

暂无引用数据

本研究基于2011—2014年美国国家健康与营养调查(NHANES)数据,纳入2969名参与者,探讨“运动零食”(即日常累积的短时中高强度身体活动)与肌少症及全因死亡率的关联。研究采用MIMS-ES(运动零食期间日均MIMS单位)和Time-ES(每日运动零食总分钟数)两个指标进行量化,运用调查加权回归模型、限制性立方样条分析及生存分析。结果显示,在完全校正模型中,MIMS-ES每增加1单位、Time-ES每增加1分钟,肌少症发生风险分别降低8%和9%;较高运动零食水平与更强握力相关。限制性立方样条分析呈L形关联,MIMS-ES约2050单位/日、Time-ES约80分钟/日后关联趋于平缓。在肌少症患者中,较高运动零食水平与较低全因死亡风险相关(MIMS-ES:HR=0.83,95%CI:0.74~0.91;Time-ES:HR=0.80,95%CI:0.71~0.91)。亚组及敏感性分析结果基本一致。研究表明,较高运动零食水平与较好的肌肉健康及生存结局相关,仍需进一步前瞻性和干预性研究加以明确。

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暂未整理

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完整作者

暂未整理

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

暂无引用数据

数据来源:暂未标注更新时间:暂无更新时间
合成生物学基础研究Science translational medicine

Tumor-specific antibodies elicited by engineered bacteria promote bladder cancer immunotherapy in preclinical mouse models.

0 次引用趋势数据不足

研究者利用合成生物学改造益生菌大肠杆菌Nissle 1917(EcN),使其表达人趋化因子CXCL13,并在膀胱癌原位小鼠模型中经膀胱内给药。结果显示,工程菌可定植于膀胱肿瘤,并在肿瘤引流淋巴结中诱发生发中心反应;与PD-1阻断联合后,在两种侵袭性强、生长快且免疫“冷”的原位小鼠模型中增强抗肿瘤活性。该效应依赖CD8+ T细胞和CD4+滤泡辅助性T细胞,联合治疗提高肿瘤特异性抗体应答,促进长期生存及肿瘤再挑战后的保护性免疫。结论认为,表达CXCL13的工程化EcN可通过增强肿瘤特异性体液免疫提升PD-1阻断免疫治疗疗效。

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编辑短评

免疫冷膀胱癌对 PD‑1 抑制剂应答差;该研究用表达 CXCL13 的工程益生菌 EcN 膀胱灌注,在引流淋巴结诱导生发中心反应、产生肿瘤特异性抗体,与 PD‑1 阻断协同,实现肿瘤控制并建立长期抗肿瘤记忆Science

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完整作者

Mathieu Rouanne、Noah Chen、Dylan L Mariuzza、Zaofeng Yang、Fangda Li、Kenia de Los Santos-Alexis、Thomas M Savage、Rosa L Vincent、Cathy L Mendelsohn、Tal Danino、Nicholas Arpaia

作者机构

  • Department of Microbiology & Immunology, Columbia University, New York, NY 10032, USA.
  • Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, USA.
  • Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
  • Department of Urology, Columbia University, New York, NY 10032, USA.
  • Data Science Institute, Columbia University, New York, NY 10027, USA.

技术路线

工程化CXCL13表达EcN经膀胱内递送定植膀胱肿瘤 → 膀胱肿瘤引流淋巴结生发中心应答分析

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 03:49
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