Nature Biomedical Engineering 

About: Nature Biomedical Engineering is an academic journal published by Springer Nature. The journal publishes majorly in the area(s): Medicine & Biology. It has an ISSN identifier of 2157-846X. Over the lifetime, 982 publications have been published receiving 53327 citations.

Near-infrared fluorophores for biomedical imaging

Abstract: This Review covers recent progress on near-infrared fluorescence imaging for preclinical animal studies and clinical diagnostics and interventions.

Artificial Intelligence in Healthcare

Abstract: Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Abstract: Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images. Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70). We also show that the trained deep-learning models used anatomical features, such as the optic disc or blood vessels, to generate each prediction.

Explainable Machine-Learning Predictions for the Prevention of Hypoxaemia During Surgery

Abstract: Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time during general anaesthesia, predicts the risk of hypoxemia and provides explanations of the risk factors. The system, which was trained on minute-by-minute data from the electronic medical records of over fifty thousand surgeries, improved the performance of anaesthesiologists when providing interpretable hypoxemia risks and contributing factors. The explanations for the predictions are broadly consistent with the literature and with prior knowledge from anaesthesiologists. Our results suggest that if anaesthesiologists currently anticipate 15% of hypoxemia events, with this system's assistance they would anticipate 30% of them, a large portion of which may benefit from early intervention because they are associated with modifiable factors. The system can help improve the clinical understanding of hypoxemia risk during anaesthesia care by providing general insights into the exact changes in risk induced by certain patient or procedure characteristics.

TLR7/8-agonist-loaded nanoparticles promote the polarization of tumour-associated macrophages to enhance cancer immunotherapy.

Abstract: Tumour-associated macrophages are abundant in many cancers, and often display an immune-suppressive M2-like phenotype that fosters tumour growth and promotes resistance to therapy. Yet, macrophages are highly plastic and can also acquire an anti-tumorigenic M1-like phenotype. Here, we show that R848, an agonist of the toll-like receptors TLR7 and TLR8 identified in a morphometric-based screen, is a potent driver of the M1 phenotype in vitro and that R848-loaded β-cyclodextrin nanoparticles (CDNP-R848) lead to efficient drug delivery to tumour-associated macrophages in vivo. As a monotherapy, the administration of CDNP-R848 in multiple tumour models in mice altered the functional orientation of the tumour immune microenvironment towards an M1 phenotype, leading to controlled tumour growth and protecting the animals against tumour rechallenge. When used in combination with the immune checkpoint inhibitor anti-PD-1, we observed improved immunotherapy response rates, including in a tumour model resistant to anti-PD-1 therapy alone. Our findings demonstrate the ability of rationally engineered drug-nanoparticle combinations to efficiently modulate tumour-associated macrophages for cancer immunotherapy.