Millimeter-scale magnetic implants paired with a fully integrated wearable device for wireless biophysical and biochemical sensing.

Implantable sensors can directly interface with various organs for precise evaluation of health status. However, extracting signals from such sensors mainly requires transcutaneous wires, integrated circuit chips, or cumbersome readout equipment, which increases the risks of infection, reduces biocompatibility, or limits portability. Here, we develop a set of millimeter-scale, chip-less, and battery-less magnetic implants paired with a fully integrated wearable device for measuring biophysical and biochemical signals. The wearable device can induce a large amplitude damped vibration of the magnetic implants and capture their subsequent motions wirelessly. These motions reflect the biophysical conditions surrounding the implants and the concentration of a specific biochemical depending on the surface modification. Experiments in rat models demonstrate the capabilities of measuring cerebrospinal fluid (CSF) viscosity, intracranial pressure, and CSF glucose levels. This miniaturized system opens the possibility for continuous, wireless monitoring of a wide range of biophysical and biochemical conditions within the living organism.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes.

Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment.