Huamin Zhang

Background: Rhabdomyolysis (RM) is a potentially life-threatening syndrome characterized by skeletal muscle damage, with acute kidney injury (AKI) being its most severe complication. Currently, no effective treatment exists for RM-induced AKI. Crocins, the major bioactive constituents extracted from the stigma of Crocus sativus L. (Saffron), possess diverse pharmacological activities. Objectives: This study aimed to explore the pharmacological effect and possible mechanism of crocins in the treatment of RM-induced AKI. Method: Network analysis was first applied to predict potential targets of crocins against RM. A rat model of hypertonic glycerol-induced RM was then established to evaluate pharmacological effect, including assessment of muscle and renal pathology, inflammatory cytokines, biochemical markers, and oxidative stress-related enzymes. Tandem mass tag–based quantitative proteomics was further employed to identify key disease targets. Molecular docking was then conducted to validate potential target interactions of crocins in RM-induced AKI treatment. Results: Animal experiments demonstrated that crocins alleviated muscle and renal injuries by inhibiting inflammation and oxidative stress while preserving hepatic and renal functions. Proteomic analysis identified perilipin 1 (PLIN1) as a critical candidate biomarker mediating these effects. Both network analysis and proteomics indicated that the peroxisome proliferator-activated receptors (PPARs) signaling pathway was closely involved in protective mechanism. Furthermore, Western blotting confirmed that crocins exerted pharmacological effect through regulating the PLIN1/PPARs signaling pathway. Molecular docking revealed that the best docking activities were demonstrated by crocin I/II–PLIN1, crocin I–PPARα, and crocin II–PPARγ. Conclusion: Crocins mitigated RM-induced AKI primarily by suppressing inflammation and oxidative stress via the PLIN1/PPARs signaling pathway. These findings provide a scientific and theoretical basis for the potential clinical application of crocins in treating RM-induced AKI.

Coronary heart disease (CHD) is a global cardiovascular disease with high morbidity and mortality, and its complex pathological mechanism poses great challenges to clinical prevention and treatment. Paeoniflorin (PA), a monoterpene glycoside active ingredient from Ranunculaceae plants, has shown potential in cardiovascular protection, but its specific anti-CHD molecular targets and systematic regulatory networks remain unclear. In this study, a mouse model of CHD was established, and a multi-omics strategy combining label-free quantitative proteomics and metabolomics was adopted to explore the mechanism of PA in treating CHD. The results showed that PA significantly improved cardiac function, alleviated myocardial pathological injury and fibrosis, and regulated lipid metabolism in CHD model mice, with the high-dose group showing the optimal effect. Proteomic analysis identified 51 key differentially expressed proteins (DEPs) reversed by PA, which were mainly enriched in complement and coagulation cascades, and neutrophil extracellular trap formation pathways, with the C3-Cfd-C3aR signaling axis as the core hub. Further verification confirmed that PA could downregulate the expression of C3, Cfd, C3aR, and their downstream molecule BTK, thereby inhibiting myocardial inflammatory response and cardiomyocyte apoptosis. In addition, PA downregulated the expression of platelet activation markers ITGA2B/ITGB3. Metabolomic analysis revealed that PA reversed 57 abnormal metabolites in CHD mice, which were enriched in GABAergic synapse, retrograde endocannabinoid signaling and other pathways. Molecular docking confirmed that PA could stably bind to C3, Cfd, C3aR, BTK, and ITGA2B/ITGB3 with strong binding activity. In conclusion, PA exerts anti-CHD effects through a multi-target and multi-pathway synergism, mainly by targeting the C3-Cfd-C3aR axis to inhibit inflammation, apoptosis and platelet activation, and regulating metabolic disorders. This study provides experimental evidence and theoretical support for the clinical application of PA as a multi-target therapeutic drug for CHD.

Background: Coronary heart disease (CHD) has a high global disease burden. According to traditional Chinese medicine theory, the main syndrome type of CHD is the syndrome of intermingled phlegm and blood stasis (SI-GPBS). Tanyu Tongzhi Decoction (TYTZD) exerts clear cardioprotective effects on CHD patients with SI-GPBS, while its specific regulatory mechanism remains unclear. Methods: Clinical serum proteomics and network pharmacology were used to screen key targets and pathways for CHD with SI-GPBS. An APOE^−/− mouse model of CHD complicated with SI-GPBS was established and treated with TYTZD. Transcriptomics, proteomics and WGCNA were combined to screen core genes, with Western blotting, immunofluorescence, co-localization analysis and Carstairs staining for target verification and observation of coronary microthrombosis and endothelial injury. Results: A total of 754 differentially expressed proteins were identified in CHD patients with SI-GPBS, significantly enriched in the platelet activation pathway, with ITGA2B as the upregulated core hub protein. Network pharmacology found 94 active ingredients and 144 therapeutic targets of TYTZD for CHD with SI-GPBS, and key components bound well with ITGA2B. In APOE^−/− mice with SI-GPBS, TYTZD improved cardiac function, reduced blood lipids, myocardial enzymes, aortic lipid deposition and myocardial damage, downregulated ITGA2B, F2RL2, FGA and FGB, inhibited integrin αIIbβ3 signaling, restrained endothelial activation and reduced coronary microthrombosis. Conclusions: TYTZD treats CHD with SI-GPBS mainly by inhibiting platelet activation, improving endothelial dysfunction, and reducing coronary microthrombosis. This study provides experimental basis for TYTZD’s clinical application in CHD with SI-GPBS and new ideas for TCM syndrome–disease combination research.

This study aimed to systematically elucidate the antihyperlipidemic mechanism of paeoniflorin, and we adopted an integrated multi-omics strategy to screen the key molecular targets and regulatory pathways involved in its action, followed by experimental validation to verify the potential regulatory effects of paeoniflorin on the screened targets and metabolic processes. Rats with high-fat diet-induced hyperlipidemia received paeoniflorin treatment. Liver histopathology was evaluated using hematoxylin–eosin and Oil Red O staining. Serum levels of total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, total bile acids, activated partial thromboplastin time, prothrombin time, thrombin time, and fibrinogen were measured using a biochemical analyzer. Integrated multi-omics analyses were performed to investigate paeoniflorin’s lipid-lowering mechanism. Critical pathways and targets identified were validated using Western blotting. Paeoniflorin alleviated pathological liver damage in hyperlipidemic rats and improved blood lipid levels, coagulation function, and liver function markers. Multi-omics analyses verified that paeoniflorin downregulated the expression of TREM-1, TLR4, NF-κB, TNF-α, and IL-1β, thereby alleviating hepatic inflammation. Paeoniflorin also upregulated the expression of low-density lipoprotein receptors (LDLR), liver X receptor alpha (LXRα), and ATP-binding cassette subfamily G member 1 (ABCG1), while downregulating proprotein convertase subtilisin/kexin type 9 (PCSK9) expression, contributing to balanced cholesterol metabolism. Paeoniflorin normalized glycerophospholipid and branched-chain amino acid metabolism, which correlated with reduced inflammation and improved cholesterol metabolism. Paeoniflorin ameliorates hyperlipidemia through multitarget mechanisms, potentially by suppressing the TREM-1-TLR4-NF-κB signaling pathway to reduce inflammation and by regulating cholesterol metabolism via the PCSK9-LDLR and LXRα-ABCG1 pathways.

Immune checkpoint inhibitors (ICIs) for cancer therapy may induce immune-related adverse events including myocarditis, which occurs infrequently but carries a high mortality rate. Crocins are the active constituents derived from Crocus sativus L. (saffron), and have demonstrated various bioactivities including anti-tumor, anti-inflammation, antioxidation, anti-ischemia, anti-aging, and neuroprotective effects. This study established a subcutaneous xenotransplanted tumor model of human liver cancer in nude mice to better mimic ICI-related myocarditis. Animal experimental results revealed that crocins improved cardiac function, relieved myocardial damage and autoimmune response, and suppressed oxidative stress and inflammatory reaction. Quantitative proteomics and Western blotting verification confirmed that crocins ameliorated experimental ICI-related myocarditis by targeting the Hpx/Nrf2/HO-1 pathway. Molecular docking revealed that the best docking activities were demonstrated by crocin I–HO-1, crocin II–Hpx, and crocin III–Nrf2. These findings shed new light on the development of therapeutic strategies for treating ICI-related myocarditis and provided the fundamental basis for expanding the clinical application of crocins.

Background: The no-reflow (NR) phenomenon remains a challenge in the treatment of acute myocardial infarction. This study aimed to explore the therapeutic potential and underlying mechanism of a combination of ferulic acid (FA) and ginsenoside Rb1 (Rb1), active components of the traditional Chinese herbal pair of Ligusticum chuanxiong Hort. and Panax ginseng C. A. Mey., respectively, in alleviating myocardial ischemia–reperfusion injury (MIRI) and NR. Methods: A rat model of MIRI was established to evaluate the effects of FA and Rb1 on cardiac function, infarction/NR area, microthrombi formation, and serum biomarkers. An integrated strategy combining network pharmacology, molecular docking, and molecular dynamics simulations was employed to predict key pathways and targets. Platelet HMGB1 release and neutrophil extracellular trap (NET) formation were investigated both in vitro and vivo. Results: MIRI induced obvious NR, accompanied by enhanced platelet HMGB1 release, increased NET formation and microthrombi accumulation. Bioinformatical analyses confirmed that FA and Rb1 stably interacts with HMGB1 and PAD4. Experimentally, FA predominantly inhibited platelet HMGB1 release, with IC ₅₀ of 19.28 µM, by suppressing the p38/ERK1/2 pathway. Rb1 exhibited stronger efficacy in inhibiting PAD4 enzyme activity. The FA-Rb1 combination demonstrated superior effects compared to either agent alone, effectively suppressing NET formation, improving cardiac function, and reducing both NR area and microthrombi burden. Conclusions: The combination of FA and Rb1 not only inhibits platelet HMGB1 release but also reduces NETs, thereby enhancing anti-NR efficacy. These findings propose a novel therapeutic approach involving FA-Rb1 combination therapy for alleviating myocardial NR. Graphical

Background: Type 2 diabetes mellitus (T2DM), Alzheimer′s disease (AD), and hypertension (HTN) tend to be comorbidities and mutually influence each other; however, the mechanisms underlying their association remain unclear. This study was aimed at identifying genes associated with susceptibility to these three diseases and their mechanisms of action using integrated network modularization analysis. Methods: The transcriptome data of T2DM, AD, and HTN were downloaded from the GEO database to identify the differentially expressed genes (DEGs), and the coexpression modules of each disease were detected by WGCNA. Z _summary algorithm was used to identify the common modules of three diseases, and the driver genes of their comorbidity were identified by flow centrality (FC) and shortest distance indexes. Gene set enrichment analysis (GSEA) was performed to define the biological functions and pathways for each module and driver genes. The HPA database and CIBERSORT method were used to analyze the mechanisms of the shared key genes from the types of single cells and immune infiltration analysis. Results: Based on the 343 overlapping DEGs that were identified, four common modules between AD, T2DM, and HTN were identified using Z _summary . GSEA revealed that the DEGs were mainly involved in the MAPK and mTOR signaling pathways. Eight key genes (ACTN4, BGN, PRELP, TSFM, UBC, ELAVL1, NRF1, and SUMO2) related to the comorbidities AD, T2DM, and HTN were identified by integrating the shared genes at the levels of DEGs, common modules, and FC‐based driver genes. As potential biomarkers, the expression of these key genes was significantly different between the three disease groups, and they were mainly expressed in endothelial cells, Langerhans cells, smooth muscle cells, and T cells. Immune infiltration analysis revealed that five different types of immune cells were related to these three diseases, including T‐regs and nonclassical monocytes. Conclusions: Common modules between T2DM, AD, and HTN and eight key susceptibility genes were identified, which may reflect the underlying mechanism of the comorbidity of T2DM, AD, and HTN. These results provide insights for the development of clinical therapies for these diseases.

The scarcity of large-scale and high-quality electronic health records (EHRs) remains a major bottleneck in biomedical research, especially as large foundation models become increasingly data-hungry. Synthesizing substantial volumes of de-identified and high-fidelity data from existing datasets has emerged as a promising solution. However, existing methods suffer from a series of limitations: they struggle to model the intrinsic properties of heterogeneous multimodal EHR data (e.g., continuous, discrete, and textual modalities), capture the complex dependencies among them, and robustly handle pervasive data incompleteness. These challenges are particularly acute in Traditional Chinese Medicine (TCM). To this end, we propose TCDiff (Triplex Cascaded Diffusion Network), a novel EHR generation framework that cascades three diffusion networks to learn the features of real-world EHR data, formatting a multi-stage generative process: Reference Modalities Diffusion, Cross-Modal Bridging, and Target Modality Diffusion. Furthermore, to validate our proposed framework, besides two public datasets, we also construct and introduce TCM-SZ1, a novel multimodal EHR dataset for benchmarking. Experimental results show that TCDiff consistently outperforms state-of-the-art baselines by an average of 10% in data fidelity under various missing rate, while maintaining competitive privacy guarantees. This highlights the effectiveness, robustness, and generalizability of our approach in real-world healthcare scenarios.

Syndrome is a unique and crucial concept in traditional Chinese medicine (TCM). However, much of the syndrome knowledge lacks systematic organization and correlation, and current information technologies are unsuitable for TCM ancient texts.We aimed to develop a knowledge graph that presents this knowledge in a more orderly, structured, and semantically oriented manner, providing a foundation for computer-aided diagnosis and treatment.We developed a construction framework of TCM syndrome knowledge from ancient books, using a pretrained model and rules (TCMSF). We conducted fine-tuning training on Enhanced Representation through Knowledge Integration (ERNIE), Bidirectional Encoder Representation from Transformers pretrained language models, and chatGLM3–6b large language models for named entity recognition (NER) tasks. Furthermore, we employed the progressive entity relationship extraction method based on the dual pattern feature combination to extract and standardize entities and relationships between entities in these books.We selected Yin deficiency syndrome as a case study and constructed a model layer suitable for the expression of knowledge in these books. Compared with multiple NER methods, the combination of ERNIE and Conditional Random Fields performs the best. By utilizing this combination, we completed the entity extraction of Yin deficiency syndrome, achieving an average F1 value of 0.77. The relationship extraction method we proposed reduces the number of incorrectly connected relationships compared with fully connected pattern layers. We successfully constructed a knowledge graph of ancient books on Yin deficiency syndrome, including over 120,000 entities and over 1.18 million relationships.We developed TCMSF in line with the knowledge characteristics of ancient TCM books and improved the accuracy of knowledge graph construction.