Nature Biomedical Engineering最新文献_第4页

Panoramic photoacoustic computed tomography with learning-based classification enhances breast lesion characterization 全景光声计算机断层扫描与基于学习的分类增强乳房病变特征

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-24 DOI: 10.1038/s41551-025-01435-3

Xin Tong, Cindy Z. Liu, Yilin Luo, Li Lin, Jessica Dzubnar, Marta Invernizzi, Stephanie Delos Santos, Yide Zhang, Rui Cao, Peng Hu, Junfu Zheng, Jaclene Torres, Armine Kasabyan, Lily L. Lai, Lisa D. Yee, Lihong V. Wang

{"title":"Panoramic photoacoustic computed tomography with learning-based classification enhances breast lesion characterization","authors":"Xin Tong, Cindy Z. Liu, Yilin Luo, Li Lin, Jessica Dzubnar, Marta Invernizzi, Stephanie Delos Santos, Yide Zhang, Rui Cao, Peng Hu, Junfu Zheng, Jaclene Torres, Armine Kasabyan, Lily L. Lai, Lisa D. Yee, Lihong V. Wang","doi":"10.1038/s41551-025-01435-3","DOIUrl":"https://doi.org/10.1038/s41551-025-01435-3","url":null,"abstract":"Breast cancer diagnosis is crucial due to the high prevalence and mortality rate associated with the disease. However, mammography involves ionizing radiation and has compromised sensitivity in radiographically dense breasts, ultrasonography lacks specificity and has operator-dependent image quality, and magnetic resonance imaging faces high cost and patient exclusion. Photoacoustic computed tomography (PACT) offers a promising solution by combining light and ultrasound for high-resolution imaging that detects tumour-related vasculature changes. Here we introduce a workflow using panoramic PACT for breast lesion characterization, offering detailed visualization of vasculature irrespective of breast density. Analysing PACT features of 78 breasts in 39 patients, we develop learning-based classifiers to distinguish between normal and suspicious tissue, achieving a maximum area under the receiver operating characteristic curve of 0.89, which is comparable with that of conventional imaging standards. We further differentiate malignant and benign lesions using 13 features. Finally, we developed a learning-based model to segment breast lesions. Our study identifies PACT as a non-invasive and sensitive imaging tool for breast lesion evaluation.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"39 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144371138","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

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases 一种用于破译细胞动力学和复杂疾病的计算机药物发现的深度生成模型

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-20 DOI: 10.1038/s41551-025-01423-7

Yumin Zheng, Jonas C. Schupp, Taylor Adams, Geremy Clair, Aurelien Justet, Farida Ahangari, Xiting Yan, Paul Hansen, Marianne Carlon, Emanuela Cortesi, Marie Vermant, Robin Vos, Laurens J. De Sadeleer, Ivan O. Rosas, Ricardo Pineda, John Sembrat, Melanie Königshoff, John E. McDonough, Bart M. Vanaudenaerde, Wim A. Wuyts, Naftali Kaminski, Jun Ding

{"title":"A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases","authors":"Yumin Zheng, Jonas C. Schupp, Taylor Adams, Geremy Clair, Aurelien Justet, Farida Ahangari, Xiting Yan, Paul Hansen, Marianne Carlon, Emanuela Cortesi, Marie Vermant, Robin Vos, Laurens J. De Sadeleer, Ivan O. Rosas, Ricardo Pineda, John Sembrat, Melanie Königshoff, John E. McDonough, Bart M. Vanaudenaerde, Wim A. Wuyts, Naftali Kaminski, Jun Ding","doi":"10.1038/s41551-025-01423-7","DOIUrl":"https://doi.org/10.1038/s41551-025-01423-7","url":null,"abstract":"Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI’s cellular dynamics analysis, and the use of the fibrotic cocktail-treated human precision-cut lung slices confirms UNAGI’s predictions that nifedipine, an antihypertensive drug, may have anti-fibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including COVID, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fibrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"240 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328504","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

Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging 用于高分辨率代谢脑成像的超快j分辨磁共振波谱成像

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-20 DOI: 10.1038/s41551-025-01418-4

Yibo Zhao, Yudu Li, Wen Jin, Rong Guo, Chao Ma, Weijun Tang, Yao Li, Georges El Fakhri, Zhi-Pei Liang

{"title":"Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging","authors":"Yibo Zhao, Yudu Li, Wen Jin, Rong Guo, Chao Ma, Weijun Tang, Yao Li, Georges El Fakhri, Zhi-Pei Liang","doi":"10.1038/s41551-025-01418-4","DOIUrl":"https://doi.org/10.1038/s41551-025-01418-4","url":null,"abstract":"Magnetic resonance spectroscopic imaging has potential for non-invasive metabolic imaging of the human brain. Here we report a method that overcomes several long-standing technical barriers associated with clinical magnetic resonance spectroscopic imaging, including long data acquisition times, limited spatial coverage and poor spatial resolution. Our method achieves ultrafast data acquisition using an efficient approach to encode spatial, spectral and J-coupling information of multiple molecules. Physics-informed machine learning is synergistically integrated in data processing to enable reconstruction of high-quality molecular maps. We validated the proposed method through phantom experiments. We obtained high-resolution molecular maps from healthy participants, revealing metabolic heterogeneities in different brain regions. We also obtained high-resolution whole-brain molecular maps in regular clinical settings, revealing metabolic alterations in tumours and multiple sclerosis. This method has the potential to transform clinical metabolic imaging and provide a long-desired capability for non-invasive label-free metabolic imaging of brain function and diseases for both research and clinical applications.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"22 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328606","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

LbuCas13a directly targets DNA and elicits strong trans-cleavage activity LbuCas13a直接靶向DNA并引发强烈的反式切割活性

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-20 DOI: 10.1038/s41551-025-01424-6

Xiaolong Wu, Siyuan Luo, Chuanghao Guo, Yi Zhao, Jialing Zhong, Ronghuan Hu, Xinyao Yang, Conghui Liu, Qianling Zhang, Songkuan Zhuang, Yong Chen, Yizhen Liu, Xueji Zhang

{"title":"LbuCas13a directly targets DNA and elicits strong trans-cleavage activity","authors":"Xiaolong Wu, Siyuan Luo, Chuanghao Guo, Yi Zhao, Jialing Zhong, Ronghuan Hu, Xinyao Yang, Conghui Liu, Qianling Zhang, Songkuan Zhuang, Yong Chen, Yizhen Liu, Xueji Zhang","doi":"10.1038/s41551-025-01424-6","DOIUrl":"https://doi.org/10.1038/s41551-025-01424-6","url":null,"abstract":"Traditionally perceived as an RNA-specific nuclease, Cas13a has been used primarily for RNA detection. We discover the ability of Leptotrichia buccalis Cas13a (LbuCas13a) to directly target DNA without the restrictions of protospacer flanking sequence and protospacer adjacent motif sequences, coupled with robust trans-cleavage activity. Contrary to conventional understanding, LbuCas13a does not degrade DNA targets. Our study reveals an enhancement in the single-nucleotide specificity of LbuCas13a against DNA compared to RNA. This heightened specificity is attributed to the lower affinity of CRISPR RNA (crRNA) towards DNA, raising the crRNA–DNA binding energy barrier. We introduce a molecular diagnostic platform called superior universal rapid enhanced specificity test with LbuCas13a (SUREST) for high-resolution genotyping. SUREST is capable of detecting DNA concentrations of CYP2C19 (rs4986893) as minute as 0.3 aM (0.18 cps µl−1). We also apply SUREST to human genotyping scenarios, indicating that SUREST performs well across a broad range of mutations and sequence contexts. SUREST represents an advancement in real-time nucleic acid detection, making it a useful tool for pathogen identification and mutation analysis in clinical diagnostics.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"16 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328609","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

Strategies for the vascular patterning of engineered tissues for organ repair 用于器官修复的工程组织血管模式的策略

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-20 DOI: 10.1038/s41551-025-01420-w

Kevin D. Janson, Siavash Parkhideh, Joseph W. R. Swain, Jessica D. Weaver, Jeffrey D. Hartgerink, Omid Veiseh

引用次数: 0

Multiplexed imaging of radionuclides 放射性核素的多路成像

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-20 DOI: 10.1038/s41551-025-01406-8

Georgios Soultanidis, Joaquin L. Herraiz, Zahi A. Fayad, Jan Grimm, Abraham J. P. Teunissen

引用次数: 0

Biomimetic model to study penile dysfunctions 研究阴茎功能障碍的仿生模型

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-17 DOI: 10.1038/s41551-025-01434-4

Isabelle Martinier, Laetitia de Kort, Petra de Graaf

引用次数: 0

A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology 虚拟组织染色和数字病理幻觉检测的鲁棒和可扩展框架

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-16 DOI: 10.1038/s41551-025-01421-9

Luzhe Huang, Yuzhu Li, Nir Pillar, Tal Keidar Haran, William Dean Wallace, Aydogan Ozcan

{"title":"A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology","authors":"Luzhe Huang, Yuzhu Li, Nir Pillar, Tal Keidar Haran, William Dean Wallace, Aydogan Ozcan","doi":"10.1038/s41551-025-01421-9","DOIUrl":"https://doi.org/10.1038/s41551-025-01421-9","url":null,"abstract":"Histopathological staining of human tissue is essential for disease diagnosis. Recent advances in virtual tissue staining technologies using artificial intelligence alleviate some of the costly and tedious steps involved in traditional histochemical staining processes, permitting multiplexed staining and tissue preservation. However, potential hallucinations and artefacts in these virtually stained tissue images pose concerns, especially for the clinical uses of these approaches. Quality assessment of histology images by experts can be subjective. Here we present an autonomous quality and hallucination assessment method, AQuA, for virtual tissue staining and digital pathology. AQuA autonomously achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images without access to histochemically stained ground truth and presents an agreement of 98.5% with the manual assessments made by board-certified pathologists, including identifying realistic-looking images that could mislead diagnosticians. We demonstrate the wide adaptability of AQuA across various virtually and histochemically stained human tissue images. This framework enhances the reliability of virtual tissue staining and provides autonomous quality assurance for image generation and transformation tasks in digital pathology and computational imaging.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"21 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144296149","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

PET-based tracking of CAR T cells and viral gene transfer using a cell surface reporter that binds to lanthanide complexes 基于pet的CAR - T细胞跟踪和病毒基因转移使用结合镧系化合物的细胞表面报告细胞

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-13 DOI: 10.1038/s41551-025-01415-7

Volker Morath, Katja Fritschle, Linda Warmuth, Markus Anneser, Sarah Dötsch, Milica Živanić, Luisa Krumwiede, Philipp Bösl, Tarik Bozoglu, Stephanie Robu, Silvana Libertini, Susanne Kossatz, Christian Kupatt, Markus Schwaiger, Katja Steiger, Dirk H. Busch, Arne Skerra, Wolfgang A. Weber

{"title":"PET-based tracking of CAR T cells and viral gene transfer using a cell surface reporter that binds to lanthanide complexes","authors":"Volker Morath, Katja Fritschle, Linda Warmuth, Markus Anneser, Sarah Dötsch, Milica Živanić, Luisa Krumwiede, Philipp Bösl, Tarik Bozoglu, Stephanie Robu, Silvana Libertini, Susanne Kossatz, Christian Kupatt, Markus Schwaiger, Katja Steiger, Dirk H. Busch, Arne Skerra, Wolfgang A. Weber","doi":"10.1038/s41551-025-01415-7","DOIUrl":"https://doi.org/10.1038/s41551-025-01415-7","url":null,"abstract":"The clinical translation of cell- and gene-based therapies is limited by the lack of non-invasive, quantitative and specific whole-body imaging tools. Here we present a positron emission tomography reporter system based on a membrane-anchored anticalin protein that binds a fluorine-18-labelled lanthanide complex with picomolar affinity via a bio-orthogonal interaction. The reporter was introduced into therapeutic cells, including CAR T cells and adeno-associated virus-transduced cells. In vitro, reporter expression conferred >800-fold higher radioligand binding versus controls. In mice, the radioligand demonstrated rapid renal clearance, showed no off-target accumulation and enabled high-contrast detection of as few as 1,200 CAR T cells in the bone marrow. Longitudinal positron emission tomography imaging over 4 weeks revealed precise tracking of CAR T cell expansion and migration, with signal intensity correlating linearly with flow cytometry data. The system also enabled the quantitative imaging of in vivo gene transfer using an adeno-associated viral vector. This depth-independent whole-body imaging platform offers a powerful tool for monitoring therapeutic cell dynamics and gene delivery in preclinical and potentially clinical settings.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"553 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278507","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

Macrophage-augmented organoids recapitulate the complex pathophysiology of viral diseases and enable development of multitarget therapeutics 巨噬细胞增强类器官概括了病毒性疾病的复杂病理生理，并使多靶点治疗的发展成为可能

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-06-13 DOI: 10.1038/s41551-025-01417-5

Kuan Liu, Yining Wang, Jiajing Li, Jiahua Zhou, Ana Maria Gonçalves da Silva, Clara Suñer, Zhe Dai, Rick Schraauwen, Patrick P. C. Boor, Kimberley Ober-Vliegen, Francijna van den Hil, Dewy M. Offermans, Theano Tsikari, Ibrahim Ayada, Maikel P. Peppelenbosch, Martin E. van Royen, Monique M. A. Verstegen, Yijin Wang, Chloe M. Orkin, Harry L. A. Janssen, Valeria V. Orlova, Pengfei Li, Oriol Mitjà, Amaro Nunes Duarte-Neto, Luc J. W. van der Laan, Qiuwei Pan

{"title":"Macrophage-augmented organoids recapitulate the complex pathophysiology of viral diseases and enable development of multitarget therapeutics","authors":"Kuan Liu, Yining Wang, Jiajing Li, Jiahua Zhou, Ana Maria Gonçalves da Silva, Clara Suñer, Zhe Dai, Rick Schraauwen, Patrick P. C. Boor, Kimberley Ober-Vliegen, Francijna van den Hil, Dewy M. Offermans, Theano Tsikari, Ibrahim Ayada, Maikel P. Peppelenbosch, Martin E. van Royen, Monique M. A. Verstegen, Yijin Wang, Chloe M. Orkin, Harry L. A. Janssen, Valeria V. Orlova, Pengfei Li, Oriol Mitjà, Amaro Nunes Duarte-Neto, Luc J. W. van der Laan, Qiuwei Pan","doi":"10.1038/s41551-025-01417-5","DOIUrl":"https://doi.org/10.1038/s41551-025-01417-5","url":null,"abstract":"The pathophysiology of acute viral diseases is complex. It is characterized by strong inflammatory responses driven by immune cells, leading to tissue damage. Currently available in vitro models mainly recapitulate the viral life cycle but fail to model immune cell-mediated pathogenesis. Here we build macrophage-augmented organoids (MaugOs) by integrating macrophages into primary organoids that are cultured from human liver tissues. We test the infections of two RNA viruses, hepatitis E virus and SARS-CoV-2, and one DNA virus, monkeypox virus, which either primarily or secondarily affect the human liver. In all three models of acute viral diseases, MaugOs recapitulate infection and the resulting inflammatory response, although to different levels. We use this system to dissect the multifunctional role of human bile on hepatitis E virus replication and the inflammatory response through distinct mechanisms of action. We also show that MaugOs recapitulate features of inflammatory cell death triggered by hepatitis E virus infection when integrated with pro-inflammatory macrophages. Furthermore, we demonstrate a proof of concept in MaugOs for development of multitarget therapeutics that simultaneously target the virus, inflammatory response and the resultant inflammatory cell death.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"6 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278510","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