Studies suggest that Yiguanjian (YGJ) may exert a therapeutic effect on liver fibrosis. However, the active components and molecular targets responsible for its action remain unclear. This study aimed to systematically evaluate the active ingredients and potential targets of YGJ in the treatment of liver fibrosis.

Active compounds and corresponding targets of YGJ were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Encyclopedia of Traditional Chinese Medicine (ETCM) databases. Liver fibrosis-related datasets were obtained from the Gene Expression Omnibus (GEO) database and divided into training and validation sets. Differentially expressed genes (DEGs) from the training set were subsequently analyzed using network pharmacology, molecular dynamics simulations, and immune infiltration analysis. Three machine learning models were employed to screen for core targets, followed by Gene Set Enrichment Analysis (GSEA) and Mendelian randomization (MR) analysis. The validation set was used to assess the expression levels and diagnostic potential of core targets.

A total of 2,887 liver fibrosis-related targets and 1,198 YGJ-related targets were identified. Three hundred and three putative targets for YGJ in the treatment of liver fibrosis were identified. Three machine learning methods further narrowed these down to five core targets. Immune infiltration analysis revealed an increase in effector B cells, resting CD4+ memory T cells, γδ T cells, and M1 macrophages during liver fibrosis progression. MR analysis showed that all five core targets (FABP4, MDM2, AKR1B1, PDGFRB, and NR1H4) had odds ratios greater than 1, indicating that they function as risk factors. Expression analyses in both the training and validation sets consistently validated the MR results, demonstrating strong diagnostic potential. GSEA revealed that the core targets were enriched in key signaling pathways, including Wnt, PPAR, and MAPK. Molecular docking and molecular dynamics simulations showed that the active compounds of YGJ exhibited strong binding affinity and stability with the core targets.

YGJ exerts its potential antifibrotic effects by downregulating or antagonizing the risk-associated targets (FABP4, MDM2, AKR1B1, PDGFRB, and NR1H4). These findings provide new insights into the potential of YGJ for treating liver fibrosis, while offering a scientific reference for the prevention and treatment of chronic liver diseases.

Due to the lack of specific Western medicine therapies for post-coronavirus disease 2019 (COVID-19) syndrome in clinical practice, this study aimed to investigate the efficacy of traditional Chinese medicine (TCM) for post-COVID-19 syndrome using a cohort study design and to explore its clinical value in alleviating patients’ symptoms and improving clinical outcomes.

In this cohort study, patients were divided into two groups according to clinical treatment. The control group received conventional Western medicine, and the treatment group received additional TCM syndrome differentiation–based treatment. Propensity score matching methods were used to reduce selection bias by equating groups based on observed covariates. Clinical data, including TCM symptom scores, the Short Form 36 Health Survey, clinical efficacy, and adverse events at Day 7, were collected. The primary outcome was the efficacy rate, defined by improvement at Day 7 compared with the Day 0 score. Data were processed and analyzed using SPSS 23.0 and R 4.5.0 software.

A total of 434 patients were enrolled in the cohort, including 306 patients in the control group and 128 in the treatment group. After 1:1 matching, 94 matched pairs were analyzed. For the primary outcome, the effective rate in the treatment group was higher than that in the control group (30.8% vs. 17.2%; odds ratio (OR) = 2.17, 95% confidence interval (CI): 1.09–4.35, P = 0.003). After seven days of treatment, the TCM syndrome score improved more in the treatment group than in the control group (median difference (MD) = 2.00, 95% CI: 0.50–3.50, P = 0.009). Subgroup analyses showed generally favorable efficacy in the treatment group across subgroups, though not all reached statistical significance.

TCM syndrome differentiation–based therapy effectively relieves clinical symptoms in patients with post-COVID-19 syndrome.