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Nature Biomedical Engineering · Nov 06, 2025

A vision–language pretrained transformer for versatile clinical respiratory disease applications

General artificial intelligence models have unique challenges in clinical practice when applied to diverse modalities and complex clinical tasks. Here we present MedMPT, a versatile, clinically oriented pretrained model tailored for respiratory healthcare, trained on 154,274 pairs of chest computed-tomography scans and radiograph reports. MedMPT adopts self-supervised learning to acquire medical insights and is capable of handling multimodal clinical data and supporting various clinical tasks aligned with clinical workflows. We evaluate the performance of MedMPT on a broad spectrum of chest-related pathological conditions, involving common medical modalities such as computed tomography images, radiology reports, laboratory tests and drug relationship graphs. MedMPT consistently outperforms the state-of-the-art multimodal pretrained models in the medical domain, achieving significant improvements in diverse clinical tasks. Extensive analysis indicates that MedMPT effectively harnesses the potential of medical data, showing both data and parameter efficiency and offering explainable insights for decision-making. MedMPT highlights the potential of multimodal pretrained models in the realm of general-purpose artificial intelligence for clinical practice.

Biomedical engineering Machine learning Respiratory tract diseases Machine Learning Clinical Human Respiratory Disease
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Nature Biomedical Engineering · Nov 05, 2025

A pre-trained large generative model for translating single-cell transcriptomes to proteomes

Measuring protein abundance at the single-cell level can facilitate a high-resolution understanding of biological mechanisms in cellular processes and disease progression. However, current single-cell proteomic technologies face challenges such as limited coverage, constrained throughput and sensitivity, batch effects, high costs and stringent experimental operations. Inspired by the translation procedure in both natural language processing and the genetic central dogma, we propose a pre-trained, large generative model named single-cell translator (scTranslator). scTranslator can generate multi-omics data by inferring the missing single-cell proteome based on the transcriptome. Through systematic benchmarking and validation on independent datasets, we have confirmed the accuracy, stability and flexibility of scTranslator across various profiling techniques (for example, CITE-seq, spatial CITE-seq, REAP-seq, NEAT-seq), cell types (for example, monocytes, macrophages, T cells, B cells), tissues (for example, blood, lung, brain) and a wide range of disease contexts, including infectious, metabolic and oncologic conditions. Furthermore, scTranslator shows its superiority in assisting various downstream analyses and applications, including gene/protein interaction inference, perturbation prediction, cell clustering, batch correction and cell origin recognition in pan-cancer data.

Machine learning Proteome informatics biology
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Nature Biomedical Engineering · Oct 24, 2025

Implanted microelectrode arrays in reinnervated muscles allow separation of neural drives from transferred polyfunctional nerves

Targeted muscle reinnervation surgery reroutes residual nerve signals into spare muscles, enabling the recovery of neural information through electromyography (EMG). However, EMG signals are often overlapping, making the interpretation of limb functions complicated. Regenerative peripheral nerve interfaces surgically partition the nerve into individual fascicles that reinnervate specific muscle grafts, isolating distinct neural sources for precise control and interpretation of EMG signals. Here we combine targeted muscle reinnervation surgery of polyvalent nerves with a high-density microelectrode array implanted at a single site within a reinnervated muscle, and via mathematical source separation methods, we separate all neural signals that are redirected into a single muscle. In participants with upper-limb amputation, the deconvolution of EMG signals from four reinnervated muscles into motor unit spike trains revealed distinct clusters of motor neurons associated with diverse functional tasks. Our method enabled the extraction of multiple neural commands within a single reinnervated muscle, eliminating the need for surgical nerve division. This approach holds promises for enhancing control over prosthetic limbs and for understanding how the central nervous system encodes movement after reinnervation.

Biomedical engineering Computational neuroscience Machine learning Microarrays Motor neuron biology mouse experiments
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Nature Biomedical Engineering · Oct 01, 2025

Peptide design through binding interface mimicry with PepMimic

Peptides offer advantages for targeted therapy, including oral bioavailability, cellular permeability and high specificity, setting them apart from conventional small molecules and biologics. Here we develop an artificial intelligence algorithm, PepMimic, to transform a known receptor or an existing antibody of a target into a short peptide binder by mimicking the binding interfaces between targets and known binders. We apply PepMimic to drug targets PD-L1, CD38, BCMA, HER2 and CD4. Surface plasmon resonance imaging results show that 8% of the peptides exhibit dissociation constant (KD) values at the 10−8M level, and 26 peptides achievingKDvalues as low as 10−9M, substantially higher than random library screening conducted under identical conditions. We apply PepMimic to target proteins lacking available binders by first using existing algorithms to design protein binders, followed by designing peptide through simulating these artificial interfaces. We extensively validate the top-ranked peptides using tail vein injections in breast, myeloma and lung tumour mouse models. Experimental results demonstrate effective membrane binding and highlight their strong potential for clinical diagnostic imaging and targeted therapeutic applications.

Machine learning Peptides Machine Learning Drug Development Cancer Mouse Clinical
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Nature Biomedical Engineering · Sep 30, 2025

Brain–heart–eye axis revealed by multi-organ imaging genetics and proteomics

Multi-organ research investigates interconnections among multiple human organ systems, enhancing our understanding of human aging and disease mechanisms. Here we use multi-organ imaging, individual- and summary-level genetics, and proteomics data consolidated via the MULTI Consortium to delineate a brain–heart–eye axis using brain patterns of structural covariance (PSCs), heart imaging-derived phenotypes (IDPs) and eye IDPs. We find that proteome-wide associations of the PSCs and IDPs show within-organ specificity and cross-organ interconnections. Pleiotropic effects of common single-nucleotide polymorphisms are observed across multiple organs, and key genetic parameters are estimated for single-nucleotide polymorphism-based heritability, polygenicity and selection signatures across the three organs. A gene–drug–disease network shows the potential of drug repurposing for cross-organ diseases. Co-localization and causal analyses reveal cross-organ causal relationships between PSC/IDP and chronic diseases, such as Alzheimer’s disease, heart failure and glaucoma. Finally, integrating multi-organ/omics features improves prediction for systemic disease categories and cognition compared with single-organ/omics features, providing future avenues for modelling human aging and disease.

Genetics research Heritable quantitative trait Machine learning Neuroscience Genomics Proteomics Human Drug Development
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Nature Biomedical Engineering · Sep 09, 2025

Combinatorial prediction of therapeutic perturbations using causally inspired neural networks

Phenotype-driven approaches identify disease-counteracting compounds by analysing the phenotypic signatures that distinguish diseased from healthy states. Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem and predicts the perturbagens needed to achieve a desired response by embedding disease cell states into networks, learning a latent representation of these states, and identifying optimal combinatorial perturbations. In experiments in nine cell lines with chemical perturbations, PDGrapher identifies effective perturbagens in more testing samples than competing methods. It also shows competitive performance on ten genetic perturbation datasets. An advantage of PDGrapher is its direct prediction, in contrast to the indirect and computationally intensive approach common in phenotype-driven models. It trains up to 25× faster than existing methods, providing a fast approach for identifying therapeutic perturbations and advancing phenotype-driven drug discovery.

Drug development Machine learning Molecular medicine Target identification Machine Learning Drug Development Cell Biology Human
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Nature Biomedical Engineering · Sep 05, 2025

A generalist foundation model and database for open-world medical image segmentation

Vision foundation models have demonstrated vast potential in achieving generalist medical segmentation capability, providing a versatile, task-agnostic solution through a single model. However, current generalist models involve simple pre-training on various medical data containing irrelevant information, often resulting in the negative transfer phenomenon and degenerated performance. Furthermore, the practical applicability of foundation models across diverse open-world scenarios, especially in out-of-distribution (OOD) settings, has not been extensively evaluated. Here we construct a publicly accessible database, MedSegDB, based on a tree-structured hierarchy and annotated from 129 public medical segmentation repositories and 5 in-house datasets. We further propose a Generalist Medical Segmentation model (MedSegX), a vision foundation model trained with a model-agnostic Contextual Mixture of Adapter Experts (ConMoAE) for open-world segmentation. We conduct a comprehensive evaluation of MedSegX across a range of medical segmentation tasks. Experimental results indicate that MedSegX achieves state-of-the-art performance across various modalities and organ systems in in-distribution (ID) settings. In OOD and real-world clinical settings, MedSegX consistently maintains its performance in both zero-shot and data-efficient generalization, outperforming other foundation models.

Imaging Machine learning Machine Learning Clinical