Objective: This study aims to explore the fingerprint and quality control index components of Jiuwei Jiangtang Oral Liquid (JWJT), known for its therapeutic effects on type 2 diabetes mellitus (T2DM). Methods: This study employed network pharmacology and molecular docking to screen the core components, core targets, and pathways of JWJT. Additionally, HPLC fingerprint was established for 12 batches of JWJT. 12 batches of JWJT were evaluated using stoichiometry and common peaks were determined following a similarity assessment. The effective substances were selected to verify the index components of JWJT through assessing their control over glucose consumption in insulin-resistant HepG2 cells and their α-glucosidase inhibitory activity. Results: Through network pharmacology and molecular docking analysis, puerarin, calycosin, ellagic acid, kaempferol-3-O-rutinoside, and kaempferol were identified as core components of JWJT for treating T2DM. Key targets such as AKT1, PIK3R1, INSR, TNF, and EGFR were implicated in regulating pathways including HIF-1, MAPK, and PI3K-Akt in T2DM treatment. The HPLC fingerprints of 12 batches of JWJT samples revealed 10 common peaks, with puerarin, calycosin, ellagic acid, and kaempferol-3-O-rutinoside specifically identified among 4 chromatographic peaks. Using chemical pattern recognition, samples were categorized into two groups, with puerarin, calycosin, and ellagic acid identified as differential markers. Verification through glucose consumption in insulin-resistant HepG2 cells and α-glucosidase inhibitory activity confirmed that puerarin, calycosin, and ellagic acid were active ingredients suitable as quality control indicators for JWJT. Conclusion: The JWJT fingerprint method established in this study is both simple and reproducible. The selected index components will serve as the basis for quality control of JWJT and introduce a novel approach for selecting and verifying quality index components of JWJT in T2DM treatment.

Background: Infectious pneumonia remains a major global health challenge with high morbidity and mortality, especially among vulnerable groups. Current diagnostic approaches lack sufficient specificity and accuracy. This study aimed to identify core diagnostic genes, explore their biological functions, and predict potential natural compounds targeting these genes to improve diagnostic and therapeutic strategies. Methods: Gene expression profiles from the GEO database (GSE103119) were analyzed to identify differentially expressed genes (DEGs). Hub genes were selected by integrating protein–protein interaction (PPI) networks and multiple machine learning algorithms. Expression patterns of the identified hub gene were validated in a murine pneumonia model. Reverse network pharmacology was applied to screen natural compounds, followed by molecular docking and molecular dynamics simulations to evaluate binding affinity and complex stability. Results: A total of 2550 DEGs were identified. FCER1A was consistently determined as a hub gene through PPI and machine learning analyses, showing significant downregulation in infectious pneumonia patients. Animal experiments confirmed pronounced reduction of Fcer1a transcription in both lung tissue and whole blood of pneumonia model mice. Two natural compounds, pyrogallol and tectorigenin, were identified as potential ligands for FCER1A. Molecular simulations confirmed stable binding with the target protein, with tectorigenin exhibiting superior binding affinity. Conclusions: This study proposes FCER1A as a promising diagnostic biomarker for infectious pneumonia and suggests tectorigenin as a candidate compound for further therapeutic development.