Through multi-dimensional data analysis, we will deeply explore the pathogenesis of pulmonary fibrosis and develop an early warning model:
1. Integrate genomics (whole genome sequencing, exome), proteomics (plasma protein measurement), metabolomics (serum metabolites) and imaging data (chest CT/MRI) to reveal the molecular network of pulmonary fibrosis. This will help identify new biomarkers and clarify the interactions between genetic susceptibility, inflammatory pathways and metabolic abnormalities.
2. Combining genomic data (whole genome sequencing, genotyping) and environmental exposure data (air pollution, lifestyle, etc.) to construct a polygenic risk score (PRS). PRS can quantify the combined effects of genetic and environmental factors (such as MUC5B gene mutation and smoking) on !!the risk of pulmonary fibrosis and help identify high-risk groups.
3. Using chest imaging data (CT quantification of interstitial lung changes) and longitudinal health records (pulmonary function, hospitalization data), develop a deep learning-based system to automatically identify imaging biomarkers of pulmonary fibrosis. Combined with clinical indicators, a dynamic risk prediction model is created to detect pulmonary fibrosis early so that timely intervention can improve patient prognosis.
In general, I will comprehensively analyze the pathogenesis of pulmonary fibrosis, identify new biomarkers, and develop early warning models through multi-omics mechanism analysis, gene-environment interaction research, and imaging phenotype analysis to provide a scientific basis for clinical diagnosis and treatment.
