Energy-Confinement 3D Flower-Shaped Cages for AI-Driven Decoding of Metabolic Fingerprints in Cardiovascular Disease Diagnosis
Zhiyu Li, Shuyu Zhang, Qianfeng Xiao, Shaoxuan Shui, Pingli Dong, Yujia Jiang, Yuanyuan Chen, Fang Lan, Yong Peng, Binwu Ying, Yao Wu
Abstract
Rapid and accurate detection plays a critical role in improving the survival and prognosis of patients with cardiovascular disease, but traditional detection methods are far from ideal for those with suspected conditions. Metabolite analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is considered to be a promising technique for disease diagnosis. However, the performance of core nanomatrixes has limited its clinical application. In this study, we constructed 3D flower-shaped cages based on controllable structured metal-organic frameworks and iron oxide nanoparticles with low thermal conductivity and significant photothermal effects. The elongation of the incident light path through multilayer reflection significantly enhances the effective light absorption area of the nanomatrixes. Concurrently, the alternating layered structure confines the thermal energy, reducing thermal losses. Moreover, the 3D structure increases affinity sites, expanding the detection coverage. This approach effectively enhances the laser ionization and thermal desorption efficiency during the LDI process. We applied this technology to analyze the serum metabolomes of patients with myocardial infarction, heart failure, and heart failure combined with myocardial infarction, achieving cost-effective, high-throughput, highly accurate, and user-friendly detection of cardiovascular diseases. Subsequently, deep analysis of detected serum fingerprints via artificial intelligence models screens potential metabolic biomarkers, providing a new paradigm for the accurate diagnosis of cardiovascular diseases.