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.

MedKPL: A heterogeneous knowledge enhanced prompt learning framework for transferable diagnosis.

Artificial Intelligence (AI) based diagnosis systems have emerged as powerful tools to reform traditional medical care. Each clinician now wants to have his own intelligent diagnostic partner to expand the range of services he can provide. However, the implementation of intelligent decision support systems based on clinical note has been hindered by the lack of extensibility of end-to-end AI diagnosis algorithms. When reading a clinical note, expert clinicians make inferences with relevant medical knowledge, which serve as prompts for making accurate diagnoses. Therefore, external medical knowledge is commonly employed as an augmentation for medical text classification tasks. Existing methods, however, cannot integrate knowledge from various knowledge sources as prompts nor can fully utilize explicit and implicit knowledge. To address these issues, we propose a Medical Knowledge-enhanced Prompt Learning (MedKPL) diagnostic framework for transferable clinical note classification. Firstly, to overcome the heterogeneity of knowledge sources, such as knowledge graphs or medical QA databases, MedKPL uniform the knowledge relevant to the disease into text sequences of fixed format. Then, MedKPL integrates medical knowledge into the prompt designed for context representation. Therefore, MedKPL can integrate knowledge into the models to enhance diagnostic performance and effectively transfer to new diseases by using relevant disease knowledge. The results of our experiments on two medical datasets demonstrate that our method yields superior medical text classification results and performs better in cross-departmental transfer tasks under few-shot or even zero-shot settings. These findings demonstrate that our MedKPL framework has the potential to improve the interpretability and transferability of current diagnostic systems.

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.

Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial.

The personalized titration and optimization of insulin regimens for treatment of type 2 diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based reinforcement learning (RL) framework (called RL-DITR), which learns the optimal insulin regimen by analyzing glycemic state rewards through patient model interactions. When evaluated during the development phase for managing hospitalized patients with T2D, RL-DITR achieved superior insulin titration optimization (mean absolute error (MAE) of 1.10 ± 0.03 U) compared to other deep learning models and standard clinical methods. We performed a stepwise clinical validation of the artificial intelligence system from simulation to deployment, demonstrating better performance in glycemic control in inpatients compared to junior and intermediate-level physicians through quantitative (MAE of 1.18 ± 0.09 U) and qualitative metrics from a blinded review. Additionally, we conducted a single-arm, patient-blinded, proof-of-concept feasibility trial in 16 patients with T2D. The primary outcome was difference in mean daily capillary blood glucose during the trial, which decreased from 11.1 (±3.6) to 8.6 (±2.4) mmol L-1 (P < 0.01), meeting the pre-specified endpoint. No episodes of severe hypoglycemia or hyperglycemia with ketosis occurred. These preliminary results warrant further investigation in larger, more diverse clinical studies. ClinicalTrials.gov registration: NCT05409391 .

Identification and characterization of two types of amino acid-regulated acetyltransferases in actinobacteria.

One hundred and fifty GCN5-like acetyltransferases with amino acid-binding (ACT)-GCN5-related N-acetyltransferase (GNAT) domain organization have been identified in actinobacteria. The ACT domain is fused to the GNAT domain, conferring amino acid-induced allosteric regulation to these protein acetyltransferases (Pat) (amino acid sensing acetyltransferase, (AAPatA)). Members of the AAPatA family share similar secondary structure and are divided into two groups based on the allosteric ligands of the ACT domain: the asparagine (Asn)-activated PatA and the cysteine (Cys)-activated PatA. The former are mainly found in Streptomyces; the latter are distributed in other actinobacteria. We investigated the effect of Asn and Cys on the acetylation activity of Sven_0867 (SvePatA, from Streptomyces venezuelae DSM 40230) and Amir_5672 (AmiPatA, from Actinosynnema mirum strain DSM 43827), respectively, as well as the relationship between the structure and function of these enzymes. These findings indicate that the activity of PatA and acetylation level of proteins may be closely correlated with intracellular concentrations of Asn and Cys in actinobacteria. Amino acid-sensing signal transduction in acetyltransferases may be a mechanism that regulates protein acetylation in response to nutrient availability. Future work examining the relationship between protein acetylation and amino acid metabolism will broaden our understanding of post-translational modifications (PTMs) in feedback regulation.

[Efficiency of Aerobic Co-composting of Urban Sludge and Chinese Medicinal Herbal Residues].

With Chinese medicinal herbal residues and municipal sludge as raw materials for co-composting experiment, the effect of the material ratio and addition time of Chinese medicinal herbal residues on the composting efficiency were investigated, including the change of the temperature, organic matter, ammonia nitrogen, and activity of protease. The best composting conditions were determined based on the results. The experimental results showed that the temperature of the pile was raised in the presence of 60 g Chinese medicinal herbal residues as carbon source and 300 g municipal sludge, the ammonia volatilization was reduced and the activity of protease was improved. The ammonia volatilization was reduced by 35.9% and the activity of protease was increased by 80.5% in 15 d, respectively. Especially, in the early stage, addition of Chinese medicinal herbal residues as conditioner could increase the organic matter degradation. Thus, the composting process was accelerated. Changes in the UV-visible and fluorescence characteristics of dissolved organic matter (DOM) during the co-composting process were discussed. The treatment with Chinese medicinal herbal residues improved the maturity of the compost. Moreover, phospholipid fatty acid (PLFA) method was used to estimate the microbial community structure changes. It showed that the number of microbial community such as fungi and Gram negative bacteria increased with addition of Chinese medicinal herbal residues.