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临床队列共 58 篇
临床队列临床研究Nature

Sugar-rich foods exacerbate antibiotic-induced microbiome disruption.

Anqi Dai、Annamaria Ballweg、William Jogia 等DOI: 10.1038/s41586-026-11077-3
多组学风险模型队列研究
1 次引用趋势数据不足

一项研究通过精确追踪173名因造血细胞移植住院患者摄入的9 419餐饮食并分析随后微生物组变化，评估饮食与肠道微生物组动态的关系。对158名有配对纵向微生物组样本患者数据应用贝叶斯推断显示，抗生素暴露期间摄入甜食和糖类可预测微生物失调加重，表现为α多样性降低和致病共生菌肠球菌更大程度扩增。小鼠实验亦表明，补充蔗糖可增加并延长抗生素诱导的肠球菌扩增。研究提示，抗生素治疗期间避免富含单糖的饮食或可减轻微生物群破坏。

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编辑短评

评估了饮食（特别是甜食和糖类）与肠道微生物组动态的关系，抗生素治疗期间避免富含单糖的饮食或可减轻微生物群破坏。

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完整作者

Anqi Dai、Annamaria Ballweg、William Jogia、Madhumitha Rangesa、Peter A Adintori、Mirae Baichoo、Tyler Funnell、Teng Fei、Nicholas R Waters、Abrielle Swartz、Sukanya Sahu、Brianna Gipson、Sandeep S Raj、Eiko Hayase、Zoe Pierce、Kenton Wu、Natalie Smith、Justin Neuberger、Adam Warren、Marina Burgos da Silva、Oriana Miltiadous、Corrado Zuanelli Brambilla、Marissa Lubin Buchan、Tatanisha K Peets、Ana Gradissimo、Luigi A Amoretti、Caichen Duan、Chenzhen Zhang、Fanny Matheis、Alexis P Sullivan、John B Slingerland、Annelie G Clurman、Daniel G Brereton、Paul A Giardina、Antonio L C Gomes、Abigail J Johnson、Dan Knights、Robert R Jenq、Kate A Markey、Miguel-Angel Perales、Sergio A Giralt、Marcel R M van den Brink、Jonas Schluter、Jonathan U Peled

作者机构

  • Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Institute for Systems Genetics, NYU Langone Health Grossman School of Medicine, New York, NY, USA.
  • Department of Microbiology, NYU Langone Health Grossman School of Medicine, New York, NY, USA.
  • Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA, USA.
  • Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Departments of Genomic Medicine and Stem Cell Transplantation Cellular Therapy, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Human Oncology & Pathogenesis Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Haematology Unit, Department of Cellular Therapy, Hematology and Laboratory Medicine, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
  • Food and Nutrition Services, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Department of Biomedical Engineering, NYU Tandon School of Engineering, New York, NY, USA.
  • Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
  • Riddet Institute, Massey University, Palmerston North, New Zealand.
  • Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.
  • Biotechnology Institute, University of Minnesota, Saint Paul, MN, USA.
  • Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
  • Division of Medicine, University of Washington, Seattle, WA, USA.
  • Weill Cornell Medical College, New York, NY, USA.
  • City of Hope, Duarte, CA, USA. mvandenbrink@coh.org.
  • Institute for Systems Genetics, NYU Langone Health Grossman School of Medicine, New York, NY, USA. jonas.schluter@nyulangone.org.
  • Department of Microbiology, NYU Langone Health Grossman School of Medicine, New York, NY, USA. jonas.schluter@nyulangone.org.
  • Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA. jonas.schluter@nyulangone.org.
  • Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. peledj@mskcc.org.
  • Weill Cornell Medical College, New York, NY, USA. peledj@mskcc.org.

技术路线

数据/样本：173例造血干细胞移植住院患者的9,419餐饮食记录及纵向肠道菌群样本（其中158例有配对纵向菌群样本用于贝叶斯推断）；分析：贝叶斯推断评估饮食与菌群动态关系；验证：小鼠实验验证蔗糖补充对抗生素诱导肠球菌扩张的影响。

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：2026/10/30 06:30
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临床队列方法学Nature methods

CytoVI: deep generative modeling of antibody-based single cell data.

Florian Ingelfinger、Nathan Levy、Can Ergen 等DOI: 10.1038/s41592-026-03224-5
单细胞队列研究方法学
0 次引用趋势数据不足

基于抗体的单细胞技术（如流式细胞术、质谱流式细胞术和CITE-seq）已广泛应用于临床诊断和基础研究，但其分析受到技术噪声、批次效应、平台差异和抗体组合受限等因素的影响。为此，研究提出CytoVI——一种概率生成模型，用于对基于抗体的单细胞数据进行统计严谨的统一分析。CytoVI可在单一概率模型中生成信息丰富的细胞嵌入、填补缺失测量值、进行差异蛋白表达检验并自动注释细胞。研究应用CytoVI构建了涵盖350种蛋白的B细胞成熟整合图谱，识别出与免疫球蛋白类别转换相关的蛋白；在经流式细胞术和CITE-seq分析的B细胞非霍奇金淋巴瘤患者队列中，CytoVI揭示了疾病相关的T细胞状态。CytoVI已作为开源软件发布于scvi-tools.org。

为什么值得关注

暂未整理

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完整作者

Florian Ingelfinger、Nathan Levy、Can Ergen、Artemy Bakulin、Alexander Becker、Pierre Boyeau、Martin Kim、Diana Ditz、Jan Dirks、Jonas Maaskola、Tobias Wertheimer、Robert Zeiser、Corinne C Widmer、Ido Amit、Nir Yosef

作者机构

  • Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel. florian.ingelfinger@uniklinik-freiburg.de.
  • Department of Internal Medicine I, Medical Center-University of Freiburg, Freiburg, Germany. florian.ingelfinger@uniklinik-freiburg.de.
  • German Cancer Consortium (DKTK) Partner Site Freiburg, a partnership between DKFZ and Medical Center-University of Freiburg, Heidelberg, Germany. florian.ingelfinger@uniklinik-freiburg.de.
  • Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel.
  • Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA.
  • Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
  • Division of Hematology and Laboratory Medicine, University Hospital Basel, Basel, Switzerland.
  • Department of Internal Medicine I, Medical Center-University of Freiburg, Freiburg, Germany.
  • Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel. ido.amit@weizmann.ac.il.
  • Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel. nir.yosef@weizmann.ac.il.
  • Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA. nir.yosef@weizmann.ac.il.

技术路线

抗体单细胞数据（流式细胞术、质谱流式、CITE-seq） → 统一概率生成模型分析（细胞嵌入生成、缺失测量插补、差异蛋白表达检验、自动细胞注释）

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：2026/10/30 06:02
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临床队列临床研究Cell

Microbiome signatures linked to cancer and treatment adverse events in a real-world cohort.

Lu Yang、Vaidhvi Singh、Brent J Gawey 等DOI: 10.1016/j.cell.2026.09.009
肿瘤与癌症多组学风险模型多中心队列研究
0 次引用趋势数据不足

一项真实世界混合癌症队列研究（Mayo Clinic Cancer Microbiome）纳入1,364例癌症患者和287名健康对照，采用可校正癌症、合并症及人口学和临床变量相关非特异性微生物组关联的分析框架，在五类癌症中鉴定出341种最可能参与癌症发病的癌症相关菌种。在癌种内部，早期乳腺癌患者粪便胆汁酸及C. scindens水平较低，早期结直肠癌患者乳酸和Veillonella parvula升高；携带二氢嘧啶脱氢酶的Anaerostipes hadrus对5-氟尿嘧啶所致腹泻具有保护作用。研究提示该队列可用于发现癌症特异性微生物组特征和预测性生物标志物。

为什么值得关注

暂未整理

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完整作者

Lu Yang、Vaidhvi Singh、Brent J Gawey、Jason P Sinnwell、Stephen Johnson、Elle C Billings、Trena M Van Gorp、Jonathan J Harrington、Michael Q Slama、Lisa M Till、Manavjot Singh、Thoshik R Samineni、Mojun Zhu、Krishna R Kalari、Gianrico Farrugia、Jun Chen、Ruben A T Mars、Purna C Kashyap

作者机构

  • Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; The Benioff Center for Microbiome Medicine, University of California San Francisco, San Francisco, CA, USA.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Division of Gastroenterology, Mount Sinai, New York, NY, USA.
  • Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
  • Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
  • Division of Gastroenterology, Mount Sinai, New York, NY, USA.
  • Department of Oncology, Mayo Clinic, Rochester, MN, USA.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.
  • Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: chen.jun2@mayo.edu.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: mars.ruben@mayo.edu.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA. Electronic address: kashyap.purna@mayo.edu.

技术路线

真实世界混合癌症队列（1364例癌症患者和287例健康对照）的粪便微生物组数据 → 构建校正框架以排除与合并症、人口学和临床变量的非特异性关联，鉴定出五类癌症相关的341个菌种

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：2026/10/30 12:53
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临床队列临床研究Nature medicine

Werner helicase inhibitor for advanced microsatellite instability solid tumors: a phase 1 trial.

Timothy A Yap、Elisa Fontana、Natalie Cook 等DOI: 10.1038/s41591-026-04657-3
肿瘤与癌症风险模型成人队列研究
2 次引用趋势数据不足

一项正在进行的首次人体1期试验第1部分评估了WRN共价抑制剂RO7589831（VVD-133214）在88例微卫星不稳定（MSI）和/或DNA错配修复缺陷晚期实体瘤患者中的递增剂量。给药方案为每日一次150 mg、每日两次150~1,000 mg或每日三次200~600 mg，主要目标为安全性和耐受性及推荐2期剂量与方案（RP2D）。600 mg每日三次组发生1例剂量限制性2级恶心，但未确定最大耐受剂量。发生率≥10%的治疗中出现不良事件多为1~2级；≥30%者为恶心（54.5%）、腹泻（43.2%）、乏力（39.8%）、贫血（37.5%）和呕吐（30.7%）。导致停药的不良事件少见（3.4%），无5级事件。MSI可评估患者疾病控制率为74.2%（49/66），7例确认为部分缓解（客观缓解率10.6%），中位缓解持续时间10.2+个月；中位无进展生存期6.7个月，中位总生存期17.6个月。选择150 mg和600 mg每日两次作为RP2D。

为什么值得关注

编辑短评

WRN共价抑制剂RO7589831（VVD-133214）的首次人体1期试验第1部分，评估了患者中的递增剂量。

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完整作者

Timothy A Yap、Elisa Fontana、Natalie Cook、Marwan Fakih、Oliver Bechter、Erica S Tsang、Emiliano Calvo、Kristoffer S Rohrberg、Javier Ros、Ignacio Matos、John H Strickler、Malaka Ameratunga、Meredith Pelster、Adam Sharp、Jaspreet Grewal、Susanna Ulahannan、Rajesh Belani、Matthew P Patricelli、Sophia M Blake、David Dejardin、Stephen Fowler、Christina Godfried Sie、Astrid Heller、Nina Henkel、Michael Hettich、Angelika Lahr、David Lamparter、Charlotte Lane、Ting Liu、Hélène Meistermann、Christophe Meille、Neil Parrott、Sotirios K Sotiriou、Denise Reeves、Barbara Romagnoli、Piergiorgio Pettazzoni、Daniel Waterkamp、Matthias Wittwer、Yali Fu、Sophie Postel-Vinay

作者机构

  • University of Texas MD Anderson Cancer Center, Houston, TX, USA. tyap@mdanderson.org.
  • Sarah Cannon Research Institute, London, UK.
  • The Christie NHS Foundation Trust and The University of Manchester, Manchester, UK.
  • City of Hope National Medical Center, Duarte, CA, USA.
  • Universitair Ziekenhuis (UZ) Leuven, Leuven, Belgium.
  • Princess Margaret Cancer Centre, Toronto, Ontario, Canada.
  • START Madrid-CIOCC, Centro Integral Oncológico Clara Campal, Madrid, Spain.
  • Rigshospitalet, Copenhagen, Denmark.
  • Vall d´Hebron Institut d'Oncologia (VHIO), Barcelona, Spain.
  • Clinica Universidad de Navarra, Madrid, Spain.
  • Duke University, Durham, NC, USA.
  • The Alfred, Melbourne, Victoria, Australia.
  • Sarah Cannon Research Institute, Nashville, TN, USA.
  • Royal Marsden Hospital, London, Institute of Cancer Research, London, UK.
  • Norton Cancer Institute, Louisville, KY, USA.
  • University of Oklahoma, Norman, OK, USA.
  • Vividion Therapeutics, San Diego, CA, USA.
  • Roche Pharma Research and Early Development, Roche Innovation Center Munich, Munich, Germany.
  • Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
  • Roche Products Ltd., Welwyn Garden City, UK.
  • Roche Pharma Research and Early Development, Roche Innovation Center New York, New York, NY, USA.
  • Gustave Roussy, Villejuif and University College of London Cancer Institute, London, UK.

技术路线

88例MSI和/或dMMR晚期实体瘤患者→剂量递增（每日一次150 mg、每日两次150-1,000 mg、每日三次200-600 mg）→安全性/耐受性分析及RP2D确定→抗肿瘤活性分析（DCR、ORR、DOR、PFS、OS）→探索性ctDNA分子缓解和FDG-PET代谢缓解分析支持生物学活性（肿瘤组织直接靶点结合未能证实）

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：2026/10/30 06:03
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临床队列临床研究Cell

Opposing functions of gut immunomodulatory metabolites on CAR-T therapy.

Markus Perl、Severin Guetter、Dhyani Shah 等DOI: 10.1016/j.cell.2026.09.004
肿瘤与癌症多组学风险模型队列研究
0 次引用趋势数据不足

一项研究纳入德国三个中心的129例患者，采用鸟枪法宏基因组学和靶向质谱分析发现，CAR-T细胞治疗前短链脂肪酸（尤其是戊酸）水平降低与疾病进展风险增加相关；而吲哚类代谢物（包括吲哚-3-甲醛和吲哚-3-乙酸）及支链脂肪酸异戊酸水平升高与不良结局相关。在人源和小鼠CAR-T细胞模型中的功能验证表明，补充戊酸可增强CAR-T细胞疗效，而吲哚-3-甲醛和异戊酸则损害其疗效。研究揭示了免疫调节代谢物对CAR-T细胞治疗的对立作用，对设计代谢物引导的微生物组治疗策略具有重要意义。

为什么值得关注

暂未整理

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完整作者

Markus Perl、Severin Guetter、Dhyani Shah、Simon Holzinger、Luisa Becker、Misbah Tariq、Philipp Menauer、Simon Delahais、Gloria Lutzny-Geier、Julian N Scherer、Sascha Göttert、Konstantin Herfeld、Paul Heinrich、Klaus Georg Kreitmeier、Claudia Gebhard、Wolfgang Herr、Marcel P Trefny、Janina Doerr、Saba Sameri、Dennis C Harrer、Kai Ziegler-Martin、Sarah Staudt、Leo Hansmann、Matthias Edinger、Daniel Wolff、Daniela Weber、Elisabeth Meedt、Alexander Denk、Andre Gessner、Ernst Holler、Tobias Wertheimer、Klaus Neuhaus、Tingting Zheng、David M Cordas Dos Santos、Sebastian Theurich、Melanie Schirmer、Karin Kleigrewe、Jonas Schluter、Marcel van den Brink、Michael Schmitt、Markus Feuerer、Christoph Heuser-Loy、Jeremy Baldwin、Luca Gattinoni、Michael Rehli、Michael Hudecek、Camille Bigenwald、Laurence Zitvogel、Marion Subklewe、Viktoria Blumenberg、Maria-Luisa Schubert、Christoph Stein-Thoeringer、Maik Luu、Sebastian Kobold、Matthias A Fante、Erik Thiele Orberg、Hendrik Poeck

作者机构

  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Bavarian Cancer Research Center (BZKF), Regensburg, Germany. Electronic address: markus.perl.ukr@gmail.com.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany.
  • Division of Clinical Pharmacology, Department of Medicine IV, University Hospital, Ludwig Maximilian University (LMU) of Munich, Munich, Germany.
  • Department of Internal Medicine 5, Hematology and Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany.
  • Lehrstuhl für Zelluläre Immuntherapie, Medizinische Klinik und Poliklinik II, Universitätsklinikum Würzburg, Würzburg, Germany.
  • Institute of Clinical Microbiology and Hygiene, University Hospital Regensburg, Regensburg, Germany.
  • Division of Hematology and Oncology, Department of Medicine, Freiburg University, Freising, Germany.
  • Core Facility Microbiome, ZIEL - Institute for Food & Health, Technische Universität München, Freising, Germany.
  • Translational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany; ZIEL - Institute for Food & Health, Technische Universität München, Freising, Germany.
  • Gene Center and Department of Biochemistry, LMU Munich, Munich, Germany.
  • Department of Medicine III, Hematology/Oncology, LMU University Hospital and Gene Center, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, a partnership between the DKFZ and BZKF at the University Hospital of the LMU, Germany.
  • Translational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany; ZIEL - Institute for Food & Health, Technische Universität München, Freising, Germany; Center for Organoid Systems, Technical University Munich, Garching, Germany.
  • Bavarian Center for Biomolecular Mass Spectrometry, TUM School of Life Sciences, Technical University Munich, Freising, Germany.
  • Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA; Institute for Systems Genetics, NYU Langone Health Grossman School of Medicine, New York, NY, USA.
  • Beckman Research Institute, City of Hope, Duarte, CA, USA.
  • Department of Medicine V, Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany.
  • Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany; Department of Internal Medicine III, Haematology and Oncology, University Hospital Bonn, Bonn, Germany.
  • Lehrstuhl für Zelluläre Immuntherapie, Medizinische Klinik und Poliklinik II, Universitätsklinikum Würzburg, Würzburg, Germany; Branch Cellular Immunotherapy, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Würzburg, Germany.
  • Gustave Roussy Cancer Campus, Villejuif, France.
  • Department of Medicine III, Hematology/Oncology, LMU University Hospital and Gene Center, LMU Munich, Munich, Germany; Laboratory for Translational Cancer Immunology, Gene Center LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany; Bavarian Cancer Research Center (BZKF), Munich, Germany.
  • Department of Internal Medicine I, University Hospital Tuebingen, Tuebingen, Germany.
  • Division of Clinical Pharmacology, Department of Medicine IV, University Hospital, Ludwig Maximilian University (LMU) of Munich, Munich, Germany; Laboratory for Translational Cancer Immunology, Gene Center LMU Munich, Munich, Germany; Bavarian Cancer Research Center (BZKF), Munich, Germany; German Center for Lung Research (DZL), Partner Site Munich, Munich, Germany.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Bavarian Cancer Research Center (BZKF), Regensburg, Germany. Electronic address: erik.orberg@ukr.de.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Bavarian Cancer Research Center (BZKF), Regensburg, Germany; Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany. Electronic address: hendrik.poeck@ukr.de.

技术路线

粪便宏基因组与靶向质谱（129例三中心队列）→代谢物与疾病进展关联分析→人及小鼠CAR-T细胞模型功能验证

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：2026/10/30 06:03
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临床队列临床研究Nature medicine

Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence.

Jian Zhou、Guangyu Guo、Jiawen Yao 等DOI: 10.1038/s41591-026-04656-4
肿瘤与癌症风险模型多中心队列研究
0 次引用趋势数据不足

研究开发了食管人工智能引导恶性病变评估（EAGLE）模型，可从胸部非增强CT中检测癌前病变和食管癌。模型在2个中心6813例患者中训练，并在3个国家12个中心80612例患者中验证，涵盖机会性筛查和人群筛查场景。多中心外部测试（8个中心，n=11466）特异性达98.5%，癌症敏感性90.0%，癌前病变敏感性52.5%；低剂量CT验证（2个中心，n=1607）表现相当。真实世界队列校准（3个中心，n=35402）使假阳性减少72.7%且保持敏感性；前瞻性医院验证（n=17446）阳性预测值为42.2%，真实世界低剂量筛查（n=10959）特异性达99.94%。配对CT-内镜队列（2个中心，n=702）中，较高敏感性操作点下癌前病变和Ⅰ期食管癌敏感性分别为65.0%和78.4%。前瞻性队列探索性分析提示，转诊高危个体接受内镜检查可提高筛查效率。EAGLE有望成为可扩展的早期食管癌筛查工具。

为什么值得关注

AI 辅助短评，基于标题和摘要整理

该研究针对食管癌缺乏无创可扩展筛查工具的问题，构建EAGLE模型从胸部平扫CT识别癌前病变与早期癌，并在多国多中心及多种筛查场景中验证。其价值在于展示了利用现有CT资源开展机会性筛查的可行性，为扩大食管癌早筛覆盖提供了新思路。

查看扩展信息

完整作者

Jian Zhou、Guangyu Guo、Jiawen Yao、Qinji Yu、Chunpeng Zheng、Xiaochen Feng、Xianghua Ye、Yongjin Zhou、Pei Yang、Lukas Lambert、Shaojun Zheng、Qiong Li、Yingda Xia、Wenchao Guo、Yi Chen、Mengdi Zhang、Yu Jiang、Dandan Zheng、Jia Ge、Haomiao Qing、Wusong Liu、Peng Zhou、Mei Lan、Lei Wu、Yong Li、Haifeng Wang、Yong Zhou、Junqiang Chen、Liyan Xu、Chenying Lu、Lin Chen、Junwei Han、Chengwei Shao、Wenqiang Wei、Kai Zhang、Jianfeng Zhang、Dingwen Zhang、Jiansong Ji、Na Li、Chuanmiao Xie、Kai Cao、Yuqian Zhao、Ling Zhang、Qifeng Wang

作者机构

  • Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • DAMO Academy, Alibaba Group, Hangzhou, China.
  • Hupan Lab, Hangzhou, China.
  • Department of Surgical Oncology, Shantou Central Hospital, Shantou, China.
  • Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China.
  • Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Zhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China.
  • Department of Radiotherapy, Cancer Hospital Affiliated to Xiangya Medical College, Central South University, Hunan Cancer Hospital, Changsha, China.
  • Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
  • Department of radio-oncology, People's Hospital of Chenghai Shantou, Shantou, China.
  • DAMO Academy, Alibaba Group, Washington, DC, USA.
  • Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
  • Department of Endoscopy Center, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
  • Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
  • Department of Radiological Imaging, Suining Central Hospital, Suining, China.
  • Department of Radiotherapy, The Third Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital), Urumqi, China.
  • Medical imaging center, The Third Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital), Urumqi, China.
  • Department of Radiation Oncology, The Teaching Hospital of Fujian Medical University, Fujian Provincial Cancer Hospital, Fuzhou, China.
  • Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, China.
  • International Health Management Center, Shanghai Institution of Pancreatic Disease, Shanghai, China.
  • School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.
  • National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Department of Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. zhangdingwen2006yyy@gmail.com.
  • Zhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China. jijiansong@zju.edu.cn.
  • Department of oncology, Suining Central Hospital, Suining, China. 1500921245@qq.com.
  • Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. xchuanm@sysucc.org.cn.
  • Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China. mdkaicao163@163.com.
  • Sichuan Provincial Engineering Research Center of Tumor Organoids and Clinical Transformation, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China. zhaoyuqian1211@scszlyy.org.cn.
  • Hupan Lab, Hangzhou, China. ling.z@alibaba-inc.com.
  • DAMO Academy, Alibaba Group, Washington, DC, USA. ling.z@alibaba-inc.com.
  • Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China. wangqifeng@scszlyy.org.cn.

技术路线

数据/样本（两中心6813例训练，三国家12中心80612例多场景验证，含机会性筛查11466例、低剂量CT 1607例、真实世界校准35402例、前瞻性医院验证17446例、真实世界低剂量筛查10959例、CT-内镜配对702例）→分析（构建EAGLE模型从胸部平扫CT检测癌前病变与食管癌，评估敏感性、特异性、PPV及校准后假阳性变化）

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 03:52
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临床队列临床研究Cancer cell

Chemotherapy-free phase 1/2 trial of HRS-4642 plus adebrelimab in previously treated metastatic KRAS G12D-mutant pancreatic cancer.

Si Shi、Miaoyan Wei、Nan Du 等DOI: 10.1016/j.ccell.2026.08.012
肿瘤与癌症风险模型队列研究
1 次引用趋势数据不足

一项1/2期试验（NCT06427239）评估选择性KRAS G12D抑制剂HRS-4642联合PD-L1阻断剂adebrelimab，用于48例既往接受过治疗的转移性KRAS G12D突变胰腺导管腺癌患者。剂量递增未出现剂量限制性毒性，确定了2期推荐剂量，未发生治疗相关死亡；最常见治疗相关不良事件为高胆固醇血症和贫血（各50%）。在2期推荐剂量队列（n=37）中，确认的客观缓解率为43.2%（95%CI 27.1-60.5），疾病控制率为81.1%（95%CI 64.8-92.0），中位缓解持续时间为6.9个月（95%CI 4.8-8.6），中位无进展生存期为5.7个月（95%CI 4.0-7.5），中位总生存期为11.5个月（95%CI 9.2-16.9）。该无化疗方案耐受性良好，在难治性KRAS G12D突变PDAC中显示出令人鼓舞的抗肿瘤活性，值得进一步研究。

为什么值得关注

编辑短评

经治 KRAS G12D 突变转移性胰腺癌后线治疗选择匮乏；该 Ⅰ/Ⅱ 期研究证实 HRS‑4642 联合 adebrelimab 的无化疗方案安全性可控、应答率高，为这类患者提供去化疗的靶向免疫联合新方向。

查看扩展信息

完整作者

Si Shi、Miaoyan Wei、Nan Du、Jialin Li、Boyue Han、Wei Wang、Xin Hu、Fei Luo、Jin Xu、Xianjun Yu

作者机构

  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: shisi@fudanpci.org.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China.
  • Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Clinical Research & Development, Jiangsu Hengrui Pharmaceuticals Co., Ltd, Shanghai, China.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: xujin@fudanpci.org.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: yuxianjun@fudanpci.org.

技术路线

48例经治转移性KRAS G12D突变PDAC患者入组→剂量递增确定RP2D并汇总治疗相关不良事件→RP2D队列（n=37）疗效分析（确认ORR、DCR、DoR、PFS、OS及95%CI）

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 03:52
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临床队列基础研究Nature medicine

EBV reactivation priming of the peripheral immune system in multiple sclerosis relapse.

Devin A King、Shrishti Saxena、Danielle Caefer 等DOI: 10.1038/s41591-026-04665-3
单细胞多组学队列研究
1 次引用趋势数据不足

尽管研究多年，多发性硬化（MS）复发前的细胞与分子事件仍不完全清楚。该观察性研究对复发缓解型MS患者的纵向血样采用单细胞RNA测序、批量转录组学、多参数流式细胞术和靶向病毒逆转录定量PCR，构建了复发前后免疫扰动的时间分辨图谱。单核细胞和B细胞中出现可重复的复发前特征，最早在临床发作前3个月出现，并富集对EB病毒（EBV）裂解再激活因子响应的宿主基因。RT-qPCR证实复发前B细胞中EBV LMP-1转录本升高，流式细胞术显示携带EBV表面蛋白gp350的CD11c+非典型B细胞群体扩增。复发前转录模块与MS全基因组关联研究风险位点及EBNA-2结合增强子重叠，提示遗传易感性与EBV响应程序通过共享调控元件发挥作用。外周激活与中枢神经系统病灶形成的关系尚待明确。

为什么值得关注

编辑短评

EBV 感染是 MS 发病基础，但复发启动时序不明；该研究在纵向样本中发现 EBV 裂解再激活提前 2–3 个月出现在外周 B 细胞，与 MS 遗传风险位点协同激活炎症程序，对外周免疫系统预致敏，作为 MS 临床复发的近端前置事件。

查看扩展信息

完整作者

Devin A King、Shrishti Saxena、Danielle Caefer、Kyle C Downer、Laura E Saucier、Ethan Goodman、Jonmichael Aracena、Alena Zhirova、Anthilia Alchanat、Saoirse Nolan、Hrishikesh Lokhande、Howard L Weiner、Benjamin E Gewurz、Tanuja Chitnis

作者机构

  • Translational Neuroimmunology Research Center, Brigham and Women's Hospital, Boston, MA, USA.
  • Ann Romney Center, Brigham and Women's Hospital, Boston, MA, USA.
  • Neurosciences Institute, Mass General Brigham, Boston, MA, USA.
  • Harvard Medical School, Boston, MA, USA.
  • Brigham Multiple Sclerosis Center, Division of Neuroimmunology, Department of Neurology, Mass General Brigham, Boston, MA, USA.
  • Division of Infectious Diseases, Department of Medicine, Mass General Brigham, Boston, MA, USA.
  • Center for Integrated Solutions to Infectious Diseases, Broad Institute, Cambridge, MA, USA.
  • Translational Neuroimmunology Research Center, Brigham and Women's Hospital, Boston, MA, USA. tchitnis@bwh.harvard.edu.
  • Ann Romney Center, Brigham and Women's Hospital, Boston, MA, USA. tchitnis@bwh.harvard.edu.
  • Neurosciences Institute, Mass General Brigham, Boston, MA, USA. tchitnis@bwh.harvard.edu.
  • Harvard Medical School, Boston, MA, USA. tchitnis@bwh.harvard.edu.
  • Brigham Multiple Sclerosis Center, Division of Neuroimmunology, Department of Neurology, Mass General Brigham, Boston, MA, USA. tchitnis@bwh.harvard.edu.

技术路线

纵向外周血样本→单细胞RNA测序、bulk转录组、多参数流式细胞术、靶向病毒RT-qPCR→RT-qPCR验证复发前B细胞LMP-1转录本升高，流式验证CD11c+非典型B细胞扩增并表达EBV gp350

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 03:52
探索相关选题直接查重
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临床队列方法学Nature medicine

Sex-specific biological aging clocks across organs and omics.

MULTI Consortium、Zhiyuan Song、Derek Fong 等DOI: 10.1038/s41591-026-04662-6
阿尔茨海默病风险模型方法学
0 次引用趋势数据不足

研究基于性別差異構建生物衰老時鐘。研究者開發了涵蓋15個器官系統的38個性別特異性生物衰老時鐘，先證明性別分層訓練對建立性別特異性健康常模參照的重要性，繼而揭示女性與男性時鐘之間的顯著差異。關鍵遺傳參數與孟德爾隨機化結果顯示，器官特異性衰老易感性及其與心代謝、內分泌和精神性狀的關係在男女中配置不同。蛋白質體分析識別出分別追蹤女性與男性生物衰老的器官特異性突觸、免疫、血管和代謝網絡。縱向生存分析中，性別特異性時鐘以性別依賴和器官依賴的方式預測全身系統性疾病和全因死亡率。進一步分析揭示，在臨床前阿茲海默症臨床試驗中，腦衰老時鐘與認知衰退軌跡之間存在性別依賴性關聯。性別分層時鐘以性別適宜的常模參照界定生物年齡，並揭示合併模型可能掩蓋的性別依賴性遺傳、分子和臨床特徵；同時，性別合併和性別交互方法仍有價值，因為人類衰老和疾病在男女之間也存在基本生物學相似性。這些發現揭示衰老、阿茲海默症和全身健康中的性別特異性生物衰老特徵，突顯明確性別分層建模方法的必要性。

为什么值得关注

编辑短评

该研究针对现有衰老时钟按性别合并训练、默认性别不变的局限，构建了覆盖15个器官系统的38个性别特异性时钟，揭示男女在遗传、蛋白质组网络及疾病与死亡预测上的分化，并提示合并模型可能掩盖性别依赖信号。其价值在于为性别分层建模提供系统依据。

查看扩展信息

完整作者

MULTI Consortium、Zhiyuan Song、Derek Fong、Naowal Azraf Rahman、Michael R Duggan、Qu Tian、Jian Zeng、Xia Zhou、Chunrui Zou、Michael S Rafii、Li Shen、Paul Thompson、Eleanor M Simonsick、Keenan A Walker、Andrew Zalesky、Christos Davatzikos、Paul Aisen、Luigi Ferrucci、Susan M Resnick、Junhao Wen

作者机构

  • Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.
  • Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
  • Department of Computer Science, Columbia University, New York, NY, USA.
  • Alzheimer's Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, San Diego, CA, USA.
  • Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.
  • Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA. junhao.wen89@gmail.com.
  • Department of Computer Science, Columbia University, New York, NY, USA. junhao.wen89@gmail.com.
  • Department of Radiology, Columbia University, New York, NY, USA. junhao.wen89@gmail.com.
  • Department of Biomedical Engineering, Columbia University, New York, NY, USA. junhao.wen89@gmail.com.
  • New York Genome Center (NYGC), New York, NY, USA. junhao.wen89@gmail.com.

技术路线

多组织/多组学数据 → 按性别分层训练38个生物学衰老时钟（15个器官系统）

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 03:52
探索相关选题直接查重
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临床队列综述Nature medicine

On-premise medical AI agents for reliable clinical decision-making.

Li Zhang、Georg Wölflein、Dyke Ferber 等DOI: 10.1038/s41591-026-04609-x
MIMIC机器学习风险模型成人方法学
2 次引用趋势数据不足

研究开发并评估了一种完全本地部署的临床人工智能代理，将本地运行控制与多视角可靠性框架结合，以支持选择性自主决策。该代理在两项基于MIMIC-IV的基准测试中，分别于七疾病任务和四疾病任务上达到90.04%和83.8%的准确率，主要基准接近云端基线。为评估决策时可靠性，研究量化了诊断与推理的内部似然、语言及行为稳定性指标。诊断行为一致性对正确性的判别力最强（AUC=0.860），在压力测试下仍具信息性（AUC=0.875）。当一致性阈值为0.90时，49.4%的病例被保留，诊断准确率达98.9%。结论表明，决策时可靠性信号可识别低风险子集供自主处理，其余转交人工审核，为机构治理下的临床代理提供了实用框架。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Li Zhang、Georg Wölflein、Dyke Ferber、Junhao Liang、Zunamys I Carrero、Xuewei Wu、Julien Vibert、Jan Clusmann、Lino Möhrmann、Elena E Möhrmann、Catharina Wichmann、Fabian Wolf、Tim Lenz、Jakob Nikolas Kather

作者机构

  • Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
  • Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Drug Development Department, Gustave Roussy, Villejuif, France.
  • Université Paris-Saclay, Gustave Roussy, INSERM U981, IHU PRISM, Villejuif, France.
  • Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Department of Gynecology and Obstetrics, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.
  • Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. kather.jn@tu-dresden.de.
  • Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.
  • Pathology & Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK. kather.jn@tu-dresden.de.

技术路线

数据/样本：两个基于MIMIC-IV的基准（七疾病任务和四疾病任务） → 分析：开发并评估一个完全本地部署的临床智能体，耦合本地操作控制与多视角可靠性框架；量化诊断和推理的内部似然、语言和行为稳定性指标；评估诊断正确性的判别能力（AUC）及压力测试下的信息性；设定一致性阈值评估保留病例比例及诊断准确率

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：2026/10/30 06:04
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