Nature Biomedical Engineering最新文献_第4页

Author Correction: Enhancing the immunogenicity of lipid-nanoparticle mRNA vaccines by adjuvanting the ionizable lipid and the mRNA 作者更正：通过佐剂可电离脂质和mRNA来增强脂质纳米颗粒mRNA疫苗的免疫原性

IF 26.8 1区医学

Nature Biomedical Engineering Pub Date : 2025-01-16 DOI: 10.1038/s41551-025-01347-2

Bowen Li, Allen Yujie Jiang, Idris Raji, Caroline Atyeo, Theresa M. Raimondo, Akiva G. R. Gordon, Luke H. Rhym, Tahoura Samad, Corina MacIsaac, Jacob Witten, Haseeb Mughal, Taras M. Chicz, Yue Xu, Ryan P. McNamara, Sangeeta Bhatia, Galit Alter, Robert Langer, Daniel G. Anderson

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Therapeutic precision, potency and promise 治疗的精确性、效力和前景

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Nature Biomedical Engineering Pub Date : 2025-01-15 DOI: 10.1038/s41551-025-01346-3

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Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity 作者更正：神经群体活动中非线性潜在因素和结构的动态柔性推断

IF 26.8 1区医学

Nature Biomedical Engineering Pub Date : 2025-01-15 DOI: 10.1038/s41551-025-01345-4

Hamidreza Abbaspourazad, Eray Erturk, Bijan Pesaran, Maryam M. Shanechi

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Moving beyond single-electrode intracortical-microstimulation-evoked tactile perception 超越单电极皮层内微刺激诱发的触觉感知

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-01-10 DOI: 10.1038/s41551-024-01339-8

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Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning 通过变压器驱动的图表示学习跨生物网络的可解释的癌症基因鉴定

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Nature Biomedical Engineering Pub Date : 2025-01-09 DOI: 10.1038/s41551-024-01312-5

Xiaorui Su, Pengwei Hu, Dongxu Li, Bowei Zhao, Zhaomeng Niu, Thomas Herget, Philip S. Yu, Lun Hu

{"title":"Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning","authors":"Xiaorui Su, Pengwei Hu, Dongxu Li, Bowei Zhao, Zhaomeng Niu, Thomas Herget, Philip S. Yu, Lun Hu","doi":"10.1038/s41551-024-01312-5","DOIUrl":"10.1038/s41551-024-01312-5","url":null,"abstract":"Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions. The model allows for the interpretation of the respective importance of multi-omic and higher-order structural features, achieved state-of-the-art performance in the prediction of cancer genes across biological networks (including networks of interactions between miRNA and proteins, transcription factors and proteins, and transcription factors and miRNA) in pan-cancer and cancer-specific scenarios, and predicted 57 cancer-gene candidates (including three genes that had not been identified by other models) among 4,729 unlabelled genes across 8 pan-cancer datasets. The model’s interpretability and generalization may facilitate the understanding of gene-related regulatory mechanisms and the discovery of new cancer genes. An interpretable transformer-based model leveraging graph representation learning accurately predicts cancer genes across homogeneous and heterogeneous pan-cancer networks of biological interactions.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"9 3","pages":"371-389"},"PeriodicalIF":26.8,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937025","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}

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A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases 一个寿命可推广的头骨剥离模型磁共振图像，利用大脑地图集的先验知识

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-01-08 DOI: 10.1038/s41551-024-01337-w

Limei Wang, Yue Sun, Jakob Seidlitz, Richard A. I. Bethlehem, Aaron Alexander-Bloch, Lena Dorfschmidt, Gang Li, Jed T. Elison, Weili Lin, Li Wang

{"title":"A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases","authors":"Limei Wang, Yue Sun, Jakob Seidlitz, Richard A. I. Bethlehem, Aaron Alexander-Bloch, Lena Dorfschmidt, Gang Li, Jed T. Elison, Weili Lin, Li Wang","doi":"10.1038/s41551-024-01337-w","DOIUrl":"https://doi.org/10.1038/s41551-024-01337-w","url":null,"abstract":"<p>In magnetic resonance imaging of the brain, an imaging-preprocessing step removes the skull and other non-brain tissue from the images. But methods for such a skull-stripping process often struggle with large data heterogeneity across medical sites and with dynamic changes in tissue contrast across lifespans. Here we report a skull-stripping model for magnetic resonance images that generalizes across lifespans by leveraging personalized priors from brain atlases. The model consists of a brain extraction module that provides an initial estimation of the brain tissue on an image, and a registration module that derives a personalized prior from an age-specific atlas. The model is substantially more accurate than state-of-the-art skull-stripping methods, as we show with a large and diverse dataset of 21,334 lifespans acquired from 18 sites with various imaging protocols and scanners, and it generates naturally consistent and seamless lifespan changes in brain volume, faithfully charting the underlying biological processes of brain development and ageing.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"24 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936109","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}

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Hormonally mediated mechanical remodelling of human haematopoietic stem cells enhances their bone-marrow engraftment 激素介导的人造血干细胞机械重塑增强其骨髓植入

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2025-01-07 DOI: 10.1038/s41551-024-01310-7

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Potent prophylactic cancer vaccines harnessing surface antigens shared by tumour cells and induced pluripotent stem cells 利用肿瘤细胞和诱导多能干细胞共享的表面抗原的强效预防性癌症疫苗

IF 26.8 1区医学

Nature Biomedical Engineering Pub Date : 2024-12-27 DOI: 10.1038/s41551-024-01309-0

Nan Li, Hao Qin, Fei Zhu, Hao Ding, Yang Chen, Yixuan Lin, Ronghui Deng, Tianyu Ma, Yuanyuan Lv, Changhao Xiong, Rong Li, Yaohua Wei, Jian Shi, Hanqing Chen, Yuliang Zhao, Guangbiao Zhou, Hua Guo, Mengyao Lv, Yongfang Lin, Bing Han, Guangjun Nie, Ruifang Zhao

{"title":"Potent prophylactic cancer vaccines harnessing surface antigens shared by tumour cells and induced pluripotent stem cells","authors":"Nan Li, Hao Qin, Fei Zhu, Hao Ding, Yang Chen, Yixuan Lin, Ronghui Deng, Tianyu Ma, Yuanyuan Lv, Changhao Xiong, Rong Li, Yaohua Wei, Jian Shi, Hanqing Chen, Yuliang Zhao, Guangbiao Zhou, Hua Guo, Mengyao Lv, Yongfang Lin, Bing Han, Guangjun Nie, Ruifang Zhao","doi":"10.1038/s41551-024-01309-0","DOIUrl":"10.1038/s41551-024-01309-0","url":null,"abstract":"The development of prophylactic cancer vaccines typically involves the selection of combinations of tumour-associated antigens, tumour-specific antigens and neoantigens. Here we show that membranes from induced pluripotent stem cells can serve as a tumour-antigen pool, and that a nanoparticle vaccine consisting of self-assembled commercial adjuvants wrapped by such membranes robustly stimulated innate immunity, evaded antigen-specific tolerance and activated B-cell and T-cell responses, which were mediated by epitopes from the abundant number of antigens shared between the membranes of tumour cells and pluripotent stem cells. In mice, the vaccine elicited systemic antitumour memory T-cell and B-cell responses as well as tumour-specific immune responses after a tumour challenge, and inhibited the progression of melanoma, colon cancer, breast cancer and post-operative lung metastases. Harnessing antigens shared by pluripotent stem cell membranes and tumour membranes may facilitate the development of universal cancer vaccines. A prophylactic cancer vaccine consisting of commercial adjuvants wrapped by membranes from pluripotent stem cells inhibited tumour progression in mice, owing to shared antigens between tumour membranes and the pluripotent stem cell membranes.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"9 2","pages":"215-233"},"PeriodicalIF":26.8,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887541","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

Sharing antigens from stem cell membranes 共享干细胞膜上的抗原

IF 26.8 1区医学

Nature Biomedical Engineering Pub Date : 2024-12-27 DOI: 10.1038/s41551-024-01323-2

Yuewen Zhai, Siwen Li

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Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation 慢性适应性深部脑刺激获取皮层记录的挑战和机遇

IF 28.1 1区医学

Nature Biomedical Engineering Pub Date : 2024-12-27 DOI: 10.1038/s41551-024-01314-3

Jeffrey Herron, Aura Kullmann, Timothy Denison, Wayne K. Goodman, Aysegul Gunduz, Wolf-Julian Neumann, Nicole R. Provenza, Maryam M. Shanechi, Sameer A. Sheth, Philip A. Starr, Alik S. Widge

{"title":"Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation","authors":"Jeffrey Herron, Aura Kullmann, Timothy Denison, Wayne K. Goodman, Aysegul Gunduz, Wolf-Julian Neumann, Nicole R. Provenza, Maryam M. Shanechi, Sameer A. Sheth, Philip A. Starr, Alik S. Widge","doi":"10.1038/s41551-024-01314-3","DOIUrl":"https://doi.org/10.1038/s41551-024-01314-3","url":null,"abstract":"<p>Deep brain stimulation (DBS), a proven treatment for movement disorders, also holds promise for the treatment of psychiatric and cognitive conditions. However, for DBS to be clinically effective, it may require DBS technology that can alter or trigger stimulation in response to changes in biomarkers sensed from the patient’s brain. A growing body of evidence suggests that such adaptive DBS is feasible, it might achieve clinical effects that are not possible with standard continuous DBS and that some of the best biomarkers are signals from the cerebral cortex. Yet capturing those markers requires the placement of cortex-optimized electrodes in addition to standard electrodes for DBS. In this Perspective we argue that the need for cortical biomarkers in adaptive DBS and the unfortunate convergence of regulatory and financial factors underpinning the unavailability of cortical electrodes for chronic uses threatens to slow down or stall research on adaptive DBS and propose public–private partnerships as a potential solution to such a critical technological gap.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"65 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887540","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