Nature Biomedical Engineering最新文献_第10页

A data-efficient strategy for building high-performing medical foundation models 用于构建高性能医学基础模型的数据高效策略

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-03-05 DOI: 10.1038/s41551-025-01365-0

Yuqi Sun, Weimin Tan, Zhuoyao Gu, Ruian He, Siyuan Chen, Miao Pang, Bo Yan

{"title":"A data-efficient strategy for building high-performing medical foundation models","authors":"Yuqi Sun, Weimin Tan, Zhuoyao Gu, Ruian He, Siyuan Chen, Miao Pang, Bo Yan","doi":"10.1038/s41551-025-01365-0","DOIUrl":"https://doi.org/10.1038/s41551-025-01365-0","url":null,"abstract":"Foundation models are pretrained on massive datasets. However, collecting medical datasets is expensive and time-consuming, and raises privacy concerns. Here we show that synthetic data generated via conditioning with disease labels can be leveraged for building high-performing medical foundation models. We pretrained a retinal foundation model, first with approximately one million synthetic retinal images with physiological structures and feature distribution consistent with real counterparts, and then with only 16.7% of the 904,170 real-world colour fundus photography images required in a recently reported retinal foundation model (RETFound). The data-efficient model performed as well or better than RETFound across nine public datasets and four diagnostic tasks; and for diabetic-retinopathy grading, it used only 40% of the expert-annotated training data used by RETFound. We also support the generalizability of the data-efficient strategy by building a classifier for the detection of tuberculosis on chest X-ray images. The text-conditioned generation of synthetic data may enhance the performance and generalization of medical foundation models.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"52 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2 深度突变学习用于选择抗SARS-CoV-2 Omicron变体进化的治疗性抗体

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-03-05 DOI: 10.1038/s41551-025-01353-4

Lester Frei, Beichen Gao, Jiami Han, Joseph M. Taft, Edward B. Irvine, Cédric R. Weber, Rachita K. Kumar, Benedikt N. Eisinger, Andrey Ignatov, Zhouya Yang, Sai T. Reddy

{"title":"Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2","authors":"Lester Frei, Beichen Gao, Jiami Han, Joseph M. Taft, Edward B. Irvine, Cédric R. Weber, Rachita K. Kumar, Benedikt N. Eisinger, Andrey Ignatov, Zhouya Yang, Sai T. Reddy","doi":"10.1038/s41551-025-01353-4","DOIUrl":"https://doi.org/10.1038/s41551-025-01353-4","url":null,"abstract":"Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). However, Omicron and its sub-lineages, as well as other heavily mutated variants, have rendered many neutralizing antibodies ineffective. Here we show that antibodies with enhanced resistance to the evolution of SARS-CoV-2 can be identified via deep mutational learning. We constructed a library of full-length RBDs of Omicron BA.1 with high mutational distance and screened it for binding to the angiotensin-converting-enzyme-2 receptor and to neutralizing antibodies. After deep-sequencing the library, we used the data to train ensemble deep-learning models for the prediction of the binding and escape of a panel of eight therapeutic antibody candidates targeting a diverse range of RBD epitopes. By using in silico evolution to assess antibody breadth via the prediction of the binding and escape of the antibodies to millions of Omicron sequences, we found combinations of two antibodies with enhanced and complementary resistance to viral evolution. Deep learning may enable the development of therapeutic antibodies that remain effective against future SARS-CoV-2 variants.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"24 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles 通过对t细胞空间蛋白质组谱的反事实学习，识别促进t细胞浸润肿瘤的扰动

IF 26.8 1区医学

Nature Biomedical Engineering Pub Date : 2025-03-05 DOI: 10.1038/s41551-025-01357-0

Zitong Jerry Wang, Abdullah S. Farooq, Yu-Jen Chen, Aman Bhargava, Alexander M. Xu, Matt W. Thomson

{"title":"Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles","authors":"Zitong Jerry Wang, Abdullah S. Farooq, Yu-Jen Chen, Aman Bhargava, Alexander M. Xu, Matt W. Thomson","doi":"10.1038/s41551-025-01357-0","DOIUrl":"10.1038/s41551-025-01357-0","url":null,"abstract":"Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics. Minimal tumour perturbations that boost T-cell infiltration can be discovered by using deep learning to analyse large-scale spatial omics profiles of tumours.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"9 3","pages":"390-404"},"PeriodicalIF":26.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41551-025-01357-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A positron emission tomography tracer for the imaging of oxidative stress in the central nervous system 一种用于中枢神经系统氧化应激成像的正电子发射断层扫描示踪剂

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-03-05 DOI: 10.1038/s41551-025-01362-3

Justin H. Wilde, Yu-Yo Sun, Spenser R. Simpson, Ethan R. Hill, Zhongxiao Fu, Emily J. Bian, Melissa M. Kinkaid, Paulina Villanueva, Aden F. Weybright, William R. Terrell, Zoraiz Qureshi, Shashika S. Perera, Heather S. Sheppard, James R. Stone, Bijoy K. Kundu, Chia-Yi Kuan, Kiel D. Neumann

{"title":"A positron emission tomography tracer for the imaging of oxidative stress in the central nervous system","authors":"Justin H. Wilde, Yu-Yo Sun, Spenser R. Simpson, Ethan R. Hill, Zhongxiao Fu, Emily J. Bian, Melissa M. Kinkaid, Paulina Villanueva, Aden F. Weybright, William R. Terrell, Zoraiz Qureshi, Shashika S. Perera, Heather S. Sheppard, James R. Stone, Bijoy K. Kundu, Chia-Yi Kuan, Kiel D. Neumann","doi":"10.1038/s41551-025-01362-3","DOIUrl":"https://doi.org/10.1038/s41551-025-01362-3","url":null,"abstract":"Reactive oxygen and nitrogen species (RONS) contribute to the pathogenesis of neurodegeneration, but the inability to detect RONS in vivo in the central nervous system has confounded the interpretation of results of clinical trials of antioxidants. Here we report the synthesis and characterization of a positron emission tomography (PET) probe, [18F]fluoroedaravone ([18F]FEDV), for the in vivo quantification of oxidative stress. Derived from the antioxidant edaravone, the probe can diffuse through the blood–brain barrier and is stable in human plasma. In mice, PET imaging with [18F]FEDV allowed for the detection of RONS after intrastriatal injection of sodium nitroprusside, in the middle cerebral artery after stroke by photothrombosis, and in brains with tauopathy. When using dynamic PET imaging coupled with parametric mapping, the sensitivity of [18F]FEDV-PET to RONS allowed for the detection of increased oxidative stress. [18F]FEDV-PET could be used to quantify RONS longitudinally in vivo and to assess the results of clinical studies of antioxidants.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"22 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D-printed perfused models of the penis for the study of penile physiology and for restoring erectile function in rabbits and pigs 3d打印的阴茎灌注模型，用于研究兔子和猪的阴茎生理学和恢复勃起功能

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-03-04 DOI: 10.1038/s41551-025-01367-y

Zhenxing Wang, Xuemin Liu, Tan Ye, Zhichen Zhai, Kai Wu, Yudi Kuang, Serge Ostrovidov, Dan Shao, Yingjun Wang, Kam W. Leong, Xuetao Shi

{"title":"3D-printed perfused models of the penis for the study of penile physiology and for restoring erectile function in rabbits and pigs","authors":"Zhenxing Wang, Xuemin Liu, Tan Ye, Zhichen Zhai, Kai Wu, Yudi Kuang, Serge Ostrovidov, Dan Shao, Yingjun Wang, Kam W. Leong, Xuetao Shi","doi":"10.1038/s41551-025-01367-y","DOIUrl":"https://doi.org/10.1038/s41551-025-01367-y","url":null,"abstract":"The intricate topology of vascular networks and the complex functions of vessel-rich tissues are challenging to reconstruct in vitro. Here we report the development of: in vitro pathological models of erectile dysfunction and Peyronie’s disease; a model of the penis that includes the glans and the corpus spongiosum with urethral structures; and an implantable model of the corpus cavernosum, whose complex vascular network is critical for erectile function, via the vein-occlusion effect. Specifically, we 3D printed a hydrogel-based corpus cavernosum incorporating a strain-limiting tunica albuginea that can be engorged with blood through vein occlusion. In corpus cavernosum defects in rabbits and pigs, implantation of the 3D-printed tissue seeded with endothelial cells restored normal erectile function on electrical stimulation of the cavernous nerves as well as spontaneous erectile function within a few weeks of implantation, which allowed the animals to mate and reproduce. Our findings support the further development of 3D-printed blood-vessel-rich functional organs for transplantation.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"211 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Radioprotection of healthy tissue via nanoparticle-delivered mRNA encoding for a damage-suppressor protein found in tardigrades 在缓步动物中发现的一种损伤抑制蛋白编码的纳米颗粒递送mRNA对健康组织的辐射保护

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-02-26 DOI: 10.1038/s41551-025-01360-5

Ameya R. Kirtane, Jianling Bi, Netra U. Rajesh, Chaoyang Tang, Miguel Jimenez, Emily Witt, Megan K. McGovern, Arielle B. Cafi, Samual J. Hatfield, Lauren Rosenstock, Sarah L. Becker, Nicole Machado, Veena Venkatachalam, Dylan Freitas, Xisha Huang, Alvin Chan, Aaron Lopes, Hyunjoon Kim, Nayoon Kim, Joy E. Collins, Michelle E. Howard, Srija Manchkanti, Theodore S. Hong, James D. Byrne, Giovanni Traverso

{"title":"Radioprotection of healthy tissue via nanoparticle-delivered mRNA encoding for a damage-suppressor protein found in tardigrades","authors":"Ameya R. Kirtane, Jianling Bi, Netra U. Rajesh, Chaoyang Tang, Miguel Jimenez, Emily Witt, Megan K. McGovern, Arielle B. Cafi, Samual J. Hatfield, Lauren Rosenstock, Sarah L. Becker, Nicole Machado, Veena Venkatachalam, Dylan Freitas, Xisha Huang, Alvin Chan, Aaron Lopes, Hyunjoon Kim, Nayoon Kim, Joy E. Collins, Michelle E. Howard, Srija Manchkanti, Theodore S. Hong, James D. Byrne, Giovanni Traverso","doi":"10.1038/s41551-025-01360-5","DOIUrl":"https://doi.org/10.1038/s41551-025-01360-5","url":null,"abstract":"Patients undergoing radiation therapy experience debilitating side effects because of toxicity arising from radiation-induced DNA strand breaks in normal peritumoural cells. Here, inspired by the ability of tardigrades to resist extreme radiation through the expression of a damage-suppressor protein that binds to DNA and reduces strand breaks, we show that the local and transient expression of the protein can reduce radiation-induced DNA damage in oral and rectal epithelial tissues (which are commonly affected during radiotherapy for head-and-neck and prostate cancers, respectively). We used ionizable lipid nanoparticles supplemented with biodegradable cationic polymers to enhance the transfection efficiency and delivery of messenger RNA encoding the damage-suppressor protein into buccal and rectal tissues. In mice with orthotopic oral cancer, messenger RNA-based radioprotection of normal tissue preserved the efficacy of radiation therapy. The strategy may be broadly applicable to the protection of healthy tissue from DNA-damaging agents.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"16 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing phage therapy by coating single bacteriophage-infected bacteria with polymer to preserve phage vitality 通过在单个噬菌体感染的细菌表面涂覆聚合物来增强噬菌体治疗，以保持噬菌体的活力

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-02-25 DOI: 10.1038/s41551-025-01354-3

Sisi Lin, Guocheng Xie, Jun He, Lu Meng, Yan Pang, Jinyao Liu

引用次数: 0

Molecular probes for in vivo optical imaging of immune cells 用于免疫细胞体内光学成像的分子探针

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-02-21 DOI: 10.1038/s41551-024-01275-7

Jing Liu, Penghui Cheng, Cheng Xu, Kanyi Pu

{"title":"Molecular probes for in vivo optical imaging of immune cells","authors":"Jing Liu, Penghui Cheng, Cheng Xu, Kanyi Pu","doi":"10.1038/s41551-024-01275-7","DOIUrl":"https://doi.org/10.1038/s41551-024-01275-7","url":null,"abstract":"Advancing the understanding of the various roles and components of the immune system requires sophisticated methods and technology for the detection of immune cells in their natural states. Recent advancements in the development of molecular probes for optical imaging have paved the way for non-invasive visualization and real-time monitoring of immune responses and functions. Here we discuss recent progress in the development of molecular probes for the selective imaging of specific immune cells. We emphasize the design principles of the probes and their comparative performance when using various optical modalities across disease contexts. We highlight molecular probes for imaging tumour-infiltrating immune cells, and their applications in drug screening and in the prediction of therapeutic outcomes of cancer immunotherapies. We also discuss the use of these probes in visualizing immune cells in atherosclerosis, lung inflammation, allograft rejection and other immune-related conditions, and the translational opportunities and challenges of using optical molecular probes for further understanding of the immune system and disease diagnosis and prognosis.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"2 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data 通过多路图像数据的无分割表示学习表征肿瘤异质性

IF 26.8 1区医学

Nature Biomedical Engineering Pub Date : 2025-02-20 DOI: 10.1038/s41551-025-01348-1

Jimin Tan, Hortense Le, Jiehui Deng, Yingzhuo Liu, Yuan Hao, Michelle Hollenberg, Wenke Liu, Joshua M. Wang, Bo Xia, Sitharam Ramaswami, Valeria Mezzano, Cynthia Loomis, Nina Murrell, Andre L. Moreira, Kyunghyun Cho, Harvey I. Pass, Kwok-Kin Wong, Yi Ban, Benjamin G. Neel, Aristotelis Tsirigos, David Fenyö

{"title":"Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data","authors":"Jimin Tan, Hortense Le, Jiehui Deng, Yingzhuo Liu, Yuan Hao, Michelle Hollenberg, Wenke Liu, Joshua M. Wang, Bo Xia, Sitharam Ramaswami, Valeria Mezzano, Cynthia Loomis, Nina Murrell, Andre L. Moreira, Kyunghyun Cho, Harvey I. Pass, Kwok-Kin Wong, Yi Ban, Benjamin G. Neel, Aristotelis Tsirigos, David Fenyö","doi":"10.1038/s41551-025-01348-1","DOIUrl":"10.1038/s41551-025-01348-1","url":null,"abstract":"High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis. Self-supervised representation learning on data from imaging mass cytometry can be used to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"9 3","pages":"405-419"},"PeriodicalIF":26.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Charting targeted courses for vaccination 制定有针对性的疫苗接种课程

IF 26.8 1区医学

Nature Biomedical Engineering Pub Date : 2025-02-17 DOI: 10.1038/s41551-025-01366-z

引用次数: 0