Biomedical engineering articles from across Nature Portfolio

Two seminal studies report a bioengineering approach to modeling CAR-T cell therapy of solid human tumors.

The authors develop a handheld opto-US probe that integrates a transparent ultrasound transducer array for real-time ultrasound, photoacoustic, and fluorescence imaging, enabling non-invasive structural, molecular, and functional diagnosis in clinics.

A multimodal pretrained transformer uses 154,274 pairs of chest CT scans and their corresponding reports and is applied to diverse clinical tasks with multimodal data.

Digital twins are a key computational technology for the implementation of personalized approaches to medical care. They are based on computational models that have been calibrated to an individual patient and can be used to identify personalized treatment approaches. Nephrology offers several promising potential applications for this technology.

Two seminal studies report a bioengineering approach to modeling CAR-T cell therapy of solid human tumors.

The emergence of lipid nanoparticles as nucleic acid delivery vehicles has revolutionized medicine, with polyethylene glycol (PEG)-lipids playing a crucial role in particle formation and in vivo fate. However, PEG has been linked to immune responses that can provoke side effects and may prevent repeat dosing, and so PEG alternatives are now being developed. Here we argue that, rather than concentrating on PEG replacement, the field should prioritize designing around pre-existing immune memory.

The implementation of clinical needs and feedback throughout the development cycle of wearable health-monitoring technology is key to success. Close collaboration with all stakeholders involved will speed clinical translation to the market.

Despite transformative advances in materials discovery, real-world performance still hinges on an often-overlooked variable: processing. To bridge the gap between discovery and deployment, processing must be elevated from an afterthought to a central pillar in design frameworks, data generation and machine learning.

Self-driving laboratories that integrate robotic production with artificial intelligence have the potential to accelerate innovation in biotechnology. Because self-driving labs can be complex and not universally applicable, it is useful to consider their suitable use cases for successful integration into discovery workflows. Here, we review strategies for assessing the suitability of self-driving labs for biochemical design problems.