Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework’s reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (S_i = 0.73), Cuscutae Semen (S_i = 0.68), Artemisiae Annuae Herba (S_i = 0.62), Schizonepetae Herba (S_i = 0.73), Gleditsiae Spina (S_i = 0.89), Prunellae Spica (S_i = 0.75), and Achyranthis Bidentatae Radix (S_i = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

Background: Bioinformatics and large-scale computational modelling have emerged as essential research fields in modern biomedical science, enabling drug discovery and therapeutic optimisation. A unique and potent technical framework for the modernisation and mechanistic clarification of traditional Chinese medicine (TCM) formulations is provided by the integration of multidimensional data using systems biology and artificial intelligence (AI) techniques. Methods: Ankylosing spondylitis–associated key hub genes were identified using multi-omics datasets, differential gene expression analysis, weighted gene co-expression network analysis, single-cell transcriptomic analysis, Mendelian randomization, and network module partitioning. In order to predict the optimal drug combinations and synergistic principal-auxiliary therapeutic roles, an interpretable, multi-layer graph convolutional network model was built using network topology features, molecular docking data, empirical clinical medication knowledge, and compound clustering similarity. Results: Eight AS-associated hub genes were found using AS as a representative disease model. A possible TCM formula, ASD-A, comprising Myrrha , Drynariae Rhizoma , Lycii Fructus , Epimedii Folium , Achyranthis Bidentatae Radix , Alpiniae Officinarum Rhizoma , Forsythiae Fructus , Astragali Radix, was prioritised by the proposed model. While ablation studies highlighted the crucial role of multi-source information integration in compound formula screening and the creation of customised intervention methods, model performance evaluation showed strong predictive potential. The identified hub genes were found to be tightly linked to immunological responses and T-cell-mediated immune processes, according to functional enrichment analyses. Experiments conducted both in vivo and in vitro confirmed that ASD-A significantly reduced pro-inflammatory cytokines like IL-6 and TNF-α ( P < 0.05), modulated the proportions of CD80 and CD86 cell subsets, and regulated the expression of important genes like KRAS, SMAD2, and MAPK14 ( P < 0.05). Furthermore, in activated Jurkat cells, ASD-A significantly decreased IL17 fluorescence while increasing Foxp3 fluorescence ( P < 0.05), indicating a rebalancing of the IL17/Foxp3 axis. The roles of major and auxiliary components in controlling hub gene activity were further clarified by formula decomposition analysis of ASD-A. Conclusion: The suggested AI-driven formula design approach provides new insights into combinatorial therapy approaches for AS while also conforming to the TCM theory of Jun-Chen-Zuo-Shi principle. The significant potential of combining contemporary bioinformatics and AI techniques with traditional medicine is demonstrated by this study, which could facilitate efficient and mechanistically informed disease therapy.

Research on functional recovery after ischemic stroke has primarily focused on non-invasive brain stimulation and motor rehabilitation therapies, while direct pharmacological interventions are relatively underexplored. This study utilized a bidirectional Mendelian randomization approach to investigate the causal relationship between 191 resting-state functional magnetic resonance imaging (rs-fMRI) features and post-ischemic stroke functional recovery (PISFR). Significant rs-fMRI phenotypes were identified, and Mendelian randomization was employed to determine their associated proteins. Bidirectional Mendelian randomization identified four rs-fMRI phenotypes potentially associated with functional recovery after ischemic stroke. Subsequent MR analysis, using pheno12 as the outcome and plasma protein as the exposure, highlighted Fas-Associated protein with Death Domain (FADD) as a significant protein. Further exploration within the protein–protein interaction (PPI) network identified FADD, Cysteinyl Aspartate Specific Proteinase 8 (CASP8), and Receptor-Interacting Serine/Threonine-Protein Kinase 1 (RIPK1) as potential drug targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that these proteins are involved in the extrinsic apoptotic pathway, providing new insights for pharmacological strategies in post-ischemic stroke recovery. This study offers genetic evidence linking rs-fMRI to functional recovery post-ischemic stroke and identifies potential drug targets that may facilitate therapeutic interventions to enhance recovery after ischemic stroke.

To address the heterogeneity in treatment effects (HTE) in precision medicine for coronary heart disease (CHD), we employed an individualized network analysis framework (Pheno-NM) to elucidate the molecular mechanisms of HTE in patients treated with Danhong injection (DHI). We integrated clinical phenotyping and transcriptomic data to identify three efficacy-based subgroups. The best-responding subgroup (D(+)S(+)) displayed the most complex gene network, with its key hub gene ORM1 linked to platelet activation. Individualized network analysis revealed that patient-specific symptom improvement correlated with unique functional module connectivity and gene expression variations (e.g., HSBP1L1 and KCNG2). Furthermore, six core network topological parameters significantly correlated with treatment efficacy and differed between subgroups (p < 0.05), alongside significant differential expression of genes such as IQCD and MTFR1. This work establishes a novel joint phenotype-genetic network modeling paradigm, providing a molecular framework for HTE and paving the way for precise, personalized cardiovascular interventions by revealing patient-specific network architectures.

Background: Motherwort injection (MI) is a traditional Chinese medicine injection widely used for hemostasis and menstrual regulation in obstetrics and gynecology. Although clinical trials and systematic reviews have demonstrated its effectiveness for specific indications, comprehensive real-world evidence (RWE) regarding its safety, effectiveness, and use patterns across diverse clinical settings remains limited. Objective: This study aims to evaluate the safety and effectiveness of MI in routine clinical practice and to generate RWE to support its rational use in women receiving treatment for approved obstetric and gynecological indications. Methods: This prospective, observational, multicenter real-world registry study plans to enroll 20,000 patients receiving MI for approved obstetric and gynecological indications in routine clinical practice in China. Treatment decisions, including whether to use MI and the dose and duration of treatment, as well as the use of concomitant medications, are made independently by treating physicians. Safety outcomes include the occurrence, type, severity, and incidence of adverse events and adverse drug reactions (ADRs). Effectiveness outcomes are assessed according to the clinical indication and include perioperative bleeding, postpartum hemorrhage, postabortion bleeding and menstrual resumption, uterine involution, menstrual regulation, and treatment satisfaction. Descriptive statistics will be used to characterize patient characteristics, MI use, and safety and effectiveness outcomes. Prespecified subgroup analyses will examine heterogeneity across major clinical groups. An exploratory nested case-control analysis with 1:4 matching and conditional logistic regression will be used to investigate factors associated with the occurrence of ADRs. Results: This study has received funding and obtained ethics approval in March 2024 (approval P24003-PJ03). Participant recruitment began on March 31, 2026. By September 2026, a total of 774 participants had been enrolled and included in the study database. The protocol specifies a target sample size of 20,000 participants, with data collection, monitoring, and quality control being conducted according to standardized study procedures. Data analysis has not yet commenced, and the study findings are expected to be published in October 2027. Conclusions: This study will provide large-scale RWE regarding the safety and effectiveness of MI in obstetric and gynecological practice. The findings are expected to support postmarketing safety surveillance, optimize clinical decision-making, and contribute to the evidence-based evaluation of traditional Chinese medicine injections.