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The presubmission enquiry 提交前查询
IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-20 DOI: 10.1038/s41551-026-01626-6
The purpose of the presubmission enquiry can be a point of confusion for our authors and can vary across journals. This month we hope to demystify its role in the editorial pipeline at Nature Biomedical Engineering.
投稿前查询的目的可能会让我们的作者感到困惑,而且在不同的期刊上也会有所不同。这个月,我们希望揭开它在《自然生物医学工程》编辑管道中的作用。
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引用次数: 0
Decoding the DNA repair toolkit of the bowhead whale 破解弓头鲸的DNA修复工具箱
IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-20 DOI: 10.1038/s41551-026-01625-7
Valeria Caprettini
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引用次数: 0
Where the nanocarriers go 纳米载体去了哪里
IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-20 DOI: 10.1038/s41551-026-01623-9
Rita Strack
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引用次数: 0
Shape-conformal porous frameworks for full coverage of neural organoids and high-resolution electrophysiology 形状-适形多孔框架的全覆盖神经类器官和高分辨率电生理
IF 28.1 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-18 DOI: 10.1038/s41551-026-01620-y
Naijia Liu, Shahrzad Shiravi, Tianqi Jin, Jiaqi Liu, Zhengguang Zhu, Jiying Li, Ingrid Cheung, Haohui Zhang, Yue Wang, Qingyuan Li, Zijie Xu, Liangsong Zeng, Maria Jose Quezada, Andres Villalobos, Yasaman Samei, Shreyaa Khanna, Shuozhen Bao, Mingzheng Wu, Sida Liang, Xu Cheng, Zengyao Lv, Woo-Youl Maeng, Yamin Zhang, Haiwen Luan, Stephen A. Boppart, Yonggang Huang, Yihui Zhang, Colin K. Franz, John D. Finan, John A. Rogers
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引用次数: 0
Rapid quantification of both fungal abundance and drug resistance via reaction kinetics. 通过反应动力学快速定量真菌丰度和耐药性。
IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-13 DOI: 10.1038/s41551-026-01619-5
Yue Zhang, Chen Li, Ruijie Deng
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引用次数: 0
Generalist foundation models from a multimodal dataset for 3D computed tomography. 从三维计算机断层扫描的多模态数据集的通用基础模型。
IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-12 DOI: 10.1038/s41551-025-01599-y
Ibrahim Ethem Hamamci, Sezgin Er, Chenyu Wang, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Omer Faruk Durugol, Benjamin Hou, Suprosanna Shit, Weicheng Dai, Murong Xu, Hadrien Reynaud, Muhammed Furkan Dasdelen, Bastian Wittmann, Tamaz Amiranashvili, Enis Simsar, Mehmet Simsar, Emine Bensu Erdemir, Abdullah Alanbay, Anjany Sekuboyina, Berkan Lafci, Ahmet Kaplan, Zhiyong Lu, Malgorzata Polacin, Bernhard Kainz, Christian Bluethgen, Kayhan Batmanghelich, Mehmet Kemal Ozdemir, Bjoern Menze

Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its corresponding radiology report. Leveraging CT-RATE, we develop CT-CLIP, a CT-focused contrastive language-image pretraining framework designed for broad applications without the need for task-specific training. We demonstrate how CT-CLIP can be used in multi-abnormality detection and case retrieval, and outperforms state-of-the-art fully supervised models across all key metrics. By combining CT-CLIP's vision encoder with a pretrained large language model, we create CT-CHAT, a vision-language foundational chat model for 3D chest CT volumes. Fine-tuned on over 2.7 million question-answer pairs derived from the CT-RATE dataset, CT-CHAT underscores the necessity for specialized methods in 3D medical imaging. Collectively, the open-source release of CT-RATE, CT-CLIP and CT-CHAT not only addresses critical challenges in 3D medical imaging but also lays the groundwork for future innovations in medical AI and improved patient care.

由于缺乏全面的数据集,医学成像人工智能的进步,特别是在3D成像方面,一直受到限制。我们介绍了CT-RATE,这是一个将3D医学图像与相应的文本报告配对的公共数据集。CT- rate包括来自21,304名独特患者的25,692张非对比3D胸部CT扫描。每次扫描都附有相应的放射学报告。利用CT-RATE,我们开发了CT-CLIP,这是一个以ct为重点的对比语言-图像预训练框架,专为广泛应用而设计,无需特定任务的训练。我们展示了CT-CLIP如何用于多异常检测和病例检索,并在所有关键指标上优于最先进的全监督模型。通过将CT- clip的视觉编码器与预训练的大型语言模型相结合,我们创建了CT- chat,这是一个用于3D胸部CT体积的视觉语言基础聊天模型。CT-CHAT对来自CT-RATE数据集的270多万对答案进行了微调,强调了3D医学成像专业方法的必要性。总的来说,CT-RATE、CT-CLIP和CT-CHAT的开源发布不仅解决了3D医学成像中的关键挑战,而且为未来医疗人工智能的创新和改善患者护理奠定了基础。
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引用次数: 0
Ablation of prostaglandin E2 signalling through dual receptor knockout in CAR T cells enhances therapeutic efficacy in solid tumours 通过CAR - T细胞双受体敲除前列腺素E2信号增强实体肿瘤的治疗效果
IF 28.1 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-11 DOI: 10.1038/s41551-025-01610-6
Janina Dörr, Lisa Gregor, Sebastian B. Lacher, Arman Oner, Yi Sun, Ignazio Piseddu, Luisa Fertig, Sebastijan Spajic, Stefanie Lesch, Stefanos Michaelides, Matthias Seifert, Adrian Gottschlich, Natasha Samson, Lina Majed, Daria Briukhovetska, Donjetë Simnica, Viktoria Hartmann, Kathrin Gabriel, Sonia Cohen, Genevieve M. Boland, David Andreu-Sanz, Emanuele Carlini, Sophia Stock, Anne Holtermann, Philipp Jie Müller, Thaddäus Strzalkowski, Marcel P. Trefny, Stefan Endres, Russell W. Jenkins, Jan P. Böttcher, Sebastian Kobold
The efficacy of chimeric antigen receptor (CAR) T cell therapy in solid cancers is limited by immunosuppression in the tumour microenvironment (TME). Prostaglandin E2 (PGE2) is a key factor locally inhibiting T cell function. We hypothesized that targeted ablation of PGE2 signalling in CAR T cells may enhance their activity in PGE2-rich solid tumours. Here we generate knockout CAR T cells double deficient for the PGE2 receptors EP2 and EP4 (EP2−/−EP4−/−) by CRISPR–Cas9 engineering. EP2−/−EP4−/− CAR T cells expanded unabatedly in the presence of PGE2. Further, they effectively controlled syngeneic and human xenograft tumour models in vivo, which was accompanied by intratumoural accumulation and persistence of modified T cells. Improved anti-tumour activity was also observed against patient-derived tumour samples from patients with pancreatic ductal adenocarcinoma (PDAC), colorectal (CRC) and neuroendocrine (NET) cancer. Our data uncovers the detrimental impact of PGE2-mediated suppression on CAR T cell efficacy and highlights EP2 and EP4 targeting as a potential strategy.
嵌合抗原受体(CAR) T细胞治疗实体癌的疗效受到肿瘤微环境(TME)免疫抑制的限制。前列腺素E2 (PGE2)是局部抑制T细胞功能的关键因子。我们假设靶向消融CAR - T细胞中的PGE2信号可能会增强它们在富含PGE2的实体肿瘤中的活性。在这里,我们通过CRISPR-Cas9工程产生PGE2受体EP2和EP4双缺陷的敲除CAR - T细胞(EP2−/−EP4−/−)。EP2−/−EP4−/−CAR - T细胞在PGE2存在下增殖不明显。此外,他们在体内有效地控制了同基因和人类异种移植肿瘤模型,这伴随着肿瘤内修饰T细胞的积累和持续存在。对来自胰腺导管腺癌(PDAC)、结直肠癌(CRC)和神经内分泌癌(NET)患者的肿瘤样本的抗肿瘤活性也有所提高。我们的数据揭示了pge2介导的抑制对CAR - T细胞疗效的不利影响,并强调了靶向EP2和EP4是一种潜在的策略。
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引用次数: 0
Injury and therapy in a human spinal cord organoid 人脊髓类器官的损伤与治疗
IF 28.1 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-11 DOI: 10.1038/s41551-025-01606-2
Nozomu Takata, Zhiwei Li, Anna Metlushko, Feng Chen, Nicholas A. Sather, Xinyi Lin, Matthew J. Schipma, Oscar A. Carballo-Molina, Cassandre Jamroz, Madison E. Strong, Cara S. Smith, Yang Yang, Ching M. Wai, Neha Joshi, Jack Kolberg-Edelbrock, Kyle J. Gray, Suitu Wang, Liam C. Palmer, Samuel I. Stupp
Damage to the spinal cord can lead to irreversible paralysis and loss of sensory function, but translation of preclinical therapies remains elusive. We recently showed that bioactive supramolecular assemblies of peptide amphiphiles can reverse paralysis in an acute mouse model following severe spinal cord injury (SCI). Here we report the development of two human spinal cord organoid injury models to simulate SCI in vitro, a laceration of the organoid with a scalpel and a compressive contusion commonly used in preclinical models, both resulting in immediate neuronal death and the formation of glial scar-like tissue. Treatment of the injured organoids with the preclinical therapy suppressed the scar-like tissue and promoted significant axonal regeneration, as observed previously in vivo. With the inclusion of microglia into the spinal cord organoids, we demonstrate that the supramolecular nanomaterial reduced pro-inflammatory factors commonly associated with injury. The human spinal cord organoid models developed here could accelerate the discovery of therapies to treat SCI and possibly damage of other central nervous system tissues owing to trauma or disease.
脊髓损伤可导致不可逆转的瘫痪和感觉功能丧失,但临床前治疗的转化仍然难以捉摸。我们最近发现,肽两亲体的生物活性超分子组装可以逆转严重脊髓损伤(SCI)后急性小鼠模型的瘫痪。在这里,我们报道了两种人类脊髓类器官损伤模型的发展,以模拟体外脊髓损伤,用手术刀撕裂类器官和临床前模型中常用的压缩挫伤,两者都会导致神经元立即死亡和胶质疤痕样组织的形成。正如之前在体内观察到的那样,用临床前治疗治疗损伤的类器官抑制了疤痕样组织并促进了显著的轴突再生。通过将小胶质细胞纳入脊髓类器官,我们证明了超分子纳米材料减少了通常与损伤相关的促炎因子。这里开发的人类脊髓类器官模型可以加速发现治疗脊髓损伤的方法,以及可能因创伤或疾病造成的其他中枢神经系统组织损伤。
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引用次数: 0
X-ray activated platinum complex induces DNA damage and enhances cancer immunotherapy through abscopal effect. x射线活化铂配合物通过体外效应诱导DNA损伤,增强肿瘤免疫治疗。
IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-09 DOI: 10.1038/s41551-026-01612-y
Guiyuan Chen, Xiangxia Li, Yu Huang, Cheng Zheng, Haopeng Fang, Qiaoyun Zhang, Ke Liu, Kecheng Wu, Qilong Zhu, Changqing Guo, Huanjun Zhu, Ruike Dai, Ying He, Qiuhong Zhu, Wenchao Zhou, Yidong Yang, Zibo Li, Kun Qu, Bofeng Li, Shiyan Xiao, Shaomin Tian, Bengang Xing, Xiaoyuan Chen, Andrew Z Wang, Yujie Xiong, Yuanzeng Min

Radiotherapy is used in more than half of cancer patients, yet most radiosensitizers increase reactive oxygen species (ROS) to enhance cytotoxicity in treated cells. This approach has limited use in hypoxic tumours and may cause oxidative injury to healthy tissues. We developed a platinum(II) azido complex (Complex 1) that releases platinonitrene upon X-ray exposure. Platinonitrene reacts with nucleophilic sites on DNA bases, forming covalent adducts that disrupt DNA integrity and cause double-strand breaks, leading to tumour cell death through a mechanism distinct from classical platinum coordination. Computational modelling elucidated this pathway and supported its role in radiosensitization. Complex 1 was synthesized by sequential ligand exchange of potassium tetrachloroplatinate with cyclohexanediamine, silver nitrate and sodium azide. In murine models, complex 1 showed negligible toxicity to major organs and normal immune cells while selectively reducing regulatory T-cell infiltration in tumours. Combined with low-dose radiotherapy and programmed cell death protein 1 blockade, it achieved complete regression of bilateral tumours in 40% of mice, demonstrating a strong abscopal effect. This work establishes metallonitrene-based, ROS-independent radiosensitization for precision radiotherapy.

超过一半的癌症患者使用放射治疗,但大多数放射增敏剂会增加活性氧(ROS),从而增强治疗细胞的细胞毒性。这种方法在缺氧肿瘤中的应用有限,并且可能对健康组织造成氧化损伤。我们开发了一种铂(II)叠氮配合物(配合物1),在x射线照射下释放铂硝基。铂硝基与DNA碱基上的亲核位点发生反应,形成共价加合物,破坏DNA完整性并导致双链断裂,通过不同于经典铂配位的机制导致肿瘤细胞死亡。计算模型阐明了这一途径,并支持其在放射致敏中的作用。用四氯铂酸钾与环己二胺、硝酸银和叠氮化钠序贯交换配体合成配合物1。在小鼠模型中,复合物1对主要器官和正常免疫细胞的毒性可忽略不计,同时选择性地减少肿瘤中的调节性t细胞浸润。结合低剂量放疗和程序性细胞死亡蛋白1阻断,40%的小鼠双侧肿瘤完全消退,显示出很强的体外作用。本工作建立了基于金属镍的、不依赖ros的精确放疗的放射增敏。
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引用次数: 0
Learning neuroimaging models from health system-scale data. 从卫生系统规模的数据中学习神经成像模型。
IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2026-02-06 DOI: 10.1038/s41551-025-01608-0
Yiwei Lyu, Samir Harake, Asadur Chowdury, Soumyanil Banerjee, Rachel Gologorsky, Shixuan Liu, Anna-Katharina Meissner, Akshay Rao, Chenhui Zhao, Akhil Kondepudi, Cheng Jiang, Xinhai Hou, Rushikesh S Joshi, Volker Neuschmelting, Ashok Srinivasan, Dawn Kleindorfer, Brian Athey, Vikas Gulani, Aditya Pandey, Honglak Lee, Todd Hollon

Neuroimaging is a ubiquitous tool for evaluating patients with neurological diseases. The global demand for magnetic resonance imaging (MRI) studies has risen steadily, placing substantial strain on health systems, prolonging turnaround times and intensifying physician burnout. These challenges disproportionately impact patients in low-resource and rural settings. Here we utilize data from a large academic health system to develop Prima, an AI foundation model for neuroimaging that supports real-world, clinical MRI studies as input. Trained on over 220,000 MRI studies, Prima uses a hierarchical vision architecture that provides general and transferable MRI features. Prima was tested in a 1-year health system-wide study that included 29,431 MRI studies. Across 52 radiologic diagnoses from major neurologic disorders, Prima achieved a mean diagnostic area under the curve (AUC) of 92.0%, outperforming other state-of-the-art general and medical AI models. Prima offers explainable differential diagnoses, worklist priority for radiologists and clinical referral recommendations. Prima demonstrates algorithmic fairness across sensitive groups. These findings highlight the transformative potential of health system-scale AI training and Prima's role in advancing AI-driven healthcare.

神经影像学是评估神经系统疾病患者的普遍工具。全球对磁共振成像(MRI)研究的需求稳步上升,给卫生系统带来了巨大压力,延长了周转时间,加剧了医生的职业倦怠。这些挑战对资源匮乏和农村地区患者的影响尤为严重。在这里,我们利用来自一个大型学术卫生系统的数据来开发Prima,这是一个神经成像的人工智能基础模型,支持真实世界的临床MRI研究作为输入。Prima接受了超过220,000次MRI研究的培训,使用分层视觉架构提供通用和可转移的MRI特征。Prima在一项为期一年的卫生系统研究中进行了测试,该研究包括29,431项MRI研究。在52例主要神经系统疾病的放射学诊断中,Prima的平均曲线下诊断面积(AUC)达到92.0%,优于其他最先进的通用和医疗人工智能模型。Prima提供可解释的鉴别诊断,放射科医生的工作清单优先级和临床转诊建议。Prima在敏感群体中展示了算法的公平性。这些发现突出了卫生系统规模的人工智能培训的变革潜力,以及Prima在推进人工智能驱动的医疗保健方面的作用。
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引用次数: 0
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Nature Biomedical Engineering
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