Search Results (550)

This work aims to solve the problem of product quality fluctuations caused by batch-to-batch variations in the adsorption capacity of activated carbon during the production of traditional Chinese medicine (TCM) injections. In this work, Shenqi Fuzheng injection was selected as an example. Diluted Shenqi Extract (DSE), an intermediate in the production process of Shenqi Fuzheng injection, was adsorbed with different batches of activated carbon. The adsorption capacities of adenine, adenosine, calycosin-7-glucoside, and astragaloside IV in DSE were selected as evaluation indices for activated carbon absorption. Characterization methods such as nitrogen adsorption, X-ray photoelectron spectrum (XPS), and Fourier transform infrared (FTIR) were chosen to explore the quantitative relationships between the properties of activated carbon (i.e., specific surface area, pore volume, surface elements, and spectrum) and the adsorption capacities of these four components. It was found that the characteristic wavelengths from FTIR characterization, i.e., 1560 cm⁻¹, 2325 cm⁻¹, 3050 cm⁻¹, and 3442 cm⁻¹, etc., showed the strongest correlation with the adsorption capacities of these four components. Prediction models based on the transmittance at characteristic wavelengths were successfully established via multiple linear regression. In validation experiments of models, the relative errors of predicted adsorption capacities of activated carbon were mostly within 5%, indicating good predictive ability of the models. The results of this work suggest that the prediction method of adsorption capacity based on the mid-infrared spectrum can provide a new way for the quality control of activated carbon. Full article

Trust in Traditional Thai Medicine (TTM) among Thai people has been reduced due to a lack of objective standards and the susceptibility of the general population to false information. The emergence of generative artificial intelligence (Gen AI) has significantly impacted various industries, including traditional medicine. However, previous Gen AI models have primarily focused on prescription generation based on Traditional Chinese Medicine (TCM), leaving TTM unexplored. To address this gap, we propose a novel fast-learning fine-tuned language model fortified with TTM knowledge. We utilized textual data from two TTM textbooks, Wat Ratcha-orasaram Ratchaworawihan (WRO), and Tamra Osot Phra Narai (NR), to fine-tune Unsloth’s Gemma-2 with 9 billion parameters. We developed two specialized TTM tasks: treatment prediction (TrP) and herbal recipe generation (HRG). The TrP and HRG models achieved precision, recall, and F1 scores of 26.54%, 28.14%, and 24.00%, and 32.51%, 24.42%, and 24.84%, respectively. Performance evaluation against TCM-based generative models showed comparable precision, recall, and F1 results with a smaller knowledge corpus. We further addressed the challenges of utilizing Thai, a low-resource and linguistically complex language. Unlike English or Chinese, Thai lacks explicit sentence boundary markers and employs an abugida writing system without spaces between words, complicating text segmentation and generation. These characteristics pose significant difficulties for machine understanding and limit model accuracy. Despite these obstacles, our work establishes a foundation for further development of AI-assisted TTM applications and highlights both the opportunities and challenges in applying language models to traditional medicine knowledge systems in Thai language contexts. Full article

Atherosclerosis (AS) is a complex pathological process characterized by the pivotal involvement of foam cells in its pathogenesis. As the primary cellular components of arterial plaques, foam cells critically determine plaque stability. Foam cells derive mainly from macrophages, and their formation is driven by dysregulated lipid metabolism within these immune cells. Macrophage cholesterol metabolism is a highly regulated process comprising four key phases: uptake, esterification, hydrolysis, and efflux. Under physiological conditions, these four phases maintain a delicate balance. However, disruption of cholesterol homeostasis results in the excessive accumulation of intracellular lipid, promoting the formation of foam cell and inflammasome activation, thereby accelerating the atherosclerotic progression. Therefore, targeting macrophage cholesterol metabolism has emerged as a promising therapeutic approach for AS. This review summarizes the mechanisms underlying macrophage cholesterol metabolism and highlights recent progress in identifying bioactive components of traditional Chinese medicines (TCMs) that mitigate AS through the modulation of macrophage cholesterol homeostasis. These findings may offer novel insights into the development of clinically effective therapies for the prevention of AS. Full article

Traditional Chinese medicine (TCM), a holistic medical system rooted in dialectical theories and natural product-based therapies, has served as a cornerstone of healthcare systems for millennia. While its empirical efficacy is widely recognized, the polypharmacological mechanisms stemming from its multi-component nature remain poorly characterized. The conventional trial-and-error approaches for bioactive compound screening from herbs raise sustainability concerns, including excessive resource consumption and suboptimal temporal efficiency. The integration of artificial intelligence (AI) and multi-omics technologies with network pharmacology (NP) has emerged as a transformative methodology aligned with TCM’s inherent “multi-component, multi-target, multi-pathway” therapeutic characteristics. This convergent review provides a computational framework to decode complex bioactive compound–target–pathway networks through two synergistic strategies, (i) NP-driven dynamics interaction network modeling and (ii) AI-enhanced multi-omics data mining, thereby accelerating drug discovery and reducing experimental costs. Our analysis of 7288 publications systematically maps NP-AI–omics integration workflows for natural product screening. The proposed framework enables sustainable drug discovery through data-driven compound prioritization, systematic repurposing of herbal formulations via mechanism-based validation, and the development of evidence-based novel TCM prescriptions. This paradigm bridges empirical TCM knowledge with mechanism-driven precision medicine, offering a theoretical basis for reconciling traditional medicine with modern pharmaceutical innovation. Full article

Background: Huang Qin Decoction (HQD) is a well-established Traditional Chinese Medicine (TCM) formulation recognized for its application in the treatment of colorectal cancer (CRC). However, the precise therapeutic mechanisms remain inadequately defined. Methods: This study integrates metabolomics from a mouse model and network pharmacology to screen potential targets and bio-active ingredients of HQD. The pharmacological activity of HQD for CRC was evidenced via single-cell RNA sequencing (scRNA-seq), molecular docking, and molecular dynamics simulations. Atomic force microscopy (AFM) assays and cellular experimental validation were used to confirm the relative mechanisms. Results: The metabolite profile undergoes significant alterations, with metabolic reprogramming evident during the malignant progression of CRC liver metastasis. Network pharmacology analysis identified that HQD regulates several metabolic pathways, including arginine biosynthesis, alanine, aspartate, and glutamate metabolism, nitrogen metabolism, phenylalanine metabolism, and linoleic acid metabolism, by targeting key proteins such as aspartate aminotransferase (GOT1), cytochrome P450 1A2 (CYP1A2), and carbonic anhydrase 2 (CA2). ScRNA-seq analysis indicated that HQD may enhance the functionality of cytotoxic T cells, thereby reversing the immunosuppressive microenvironment. Virtual verification revealed a strong binding affinity between the identified hub targets and active constituents of HQD, a finding subsequently corroborated by AFM assays. Cellular experiments confirmed that naringenin treatment inhibits the proliferation, migration, and invasion of CRC cells by downregulating GOT1 expression and disrupting glutamine metabolism. Conclusions: Computational prediction and in vitro validation reveal the active ingredients, potential targets, and molecular mechanisms of HQD against CRC liver metastasis, thereby providing a scientific foundation for the application of TCM in CRC treatment. Full article

Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the major subtype, accounting for more than 85% of all lung cancer cases. Recent advances in precision oncology have allowed NSCLC patients bearing specific oncogenic epidermal growth factor receptor (EGFR) mutations to respond well to EGFR tyrosine kinase inhibitors (TKIs). Due to the high EGFR mutation frequency (up to more than 50%) observed particularly in Asian NSCLC patients, EGFR-TKIs have produced unprecedented clinical responses. Depending on their binding interactions with EGFRs, EGFR-TKIs are classified as reversible (first-generation: gefitinib and erlotinib) or irreversible inhibitors (second-generation: afatinib and dacomitinib; third-generation: osimertinib). While the discovery of osimertinib represents a breakthrough in the treatment of NSCLC, most patients eventually relapse and develop drug resistance. Novel strategies to overcome osimertinib resistance are urgently needed. In Asian countries, the concomitant use of Western medicine and traditional Chinese medicine (TCM) is very common. Ganoderma lucidum (Lingzhi) and Coriolus versicolor (Yunzhi) are popular TCMs that are widely consumed by cancer patients to enhance anticancer efficacy and alleviate the side effects associated with cancer therapy. The bioactive polysaccharides and triterpenes in these medicinal mushrooms are believed to contribute to their anticancer and immunomodulating effects. This review presents the latest update on the beneficial combination of Lingzhi/Yunzhi and EGFR-TKIs to overcome drug resistance. The effects of Lingzhi/Yunzhi on various oncogenic signaling pathways and anticancer immunity, as well as their potential to overcome EGFR-TKI resistance, are highlighted. The potential risk of herb–drug interactions could become critical when cancer patients take Lingzhi/Yunzhi as adjuvants during cancer therapy. The involvement of drug transporters and cytochrome P450 enzymes in these herb–drug interactions is summarized. Finally, we also discuss the opportunities and future prospects regarding the combined use of Lingzhi/Yunzhi and EGFR-TKIs in cancer patients. Full article

Background: Obesity is a global health challenge associated with metabolic and cardiovascular diseases. Traditional Chinese Medicine (TCM) body constitution theory offers a unique perspective on individual susceptibility to obesity; however, its integration into public health strategies remains underexplored. Objective: To examine the associations between vegetarian dietary patterns, TCM body constitution types (Phlegm stasis, Yang deficiency, and Yin deficiency), and overweight/obesity in a large-scale national cohort. Methods: Data were obtained from 3597 participants enrolled in the Taiwan Biobank. Socio-demographic variables, lifestyle behaviors (diet, smoking, physical activity), and anthropometric indicators (BMI and waist circumference) were assessed. Participants were categorized by weight status and TCM body constitution. Polytomous logistic regression models were used to evaluate associations between vegetarian dietary patterns, constitution types, and overweight/obesity, adjusting for potential confounders. Results: Among participants (mean age, 50.1 ± 9.4 years), 55.6% had normal BMI, 27.3% were overweight, and 17.1% were obese. Vegetarian dietary patterns were significantly associated with lower odds of Phlegm stasis (OR: 0.96; p < 0.001), Yang deficiency (OR: 0.97; p < 0.001), and Yin deficiency (OR: 0.97; p < 0.001), as well as with lower odds of overweight (OR: 0.72; p < 0.05) and obesity (OR: 0.67; p < 0.05). Physical activity was also associated with lower odds of all three constitution types and obesity. Phlegm stasis constitution was associated with higher odds of obesity (range of ORs: 1.18–1.58; p < 0.001). Conclusions: Vegetarian dietary patterns and regular physical activity were associated with lower odds of obesity and TCM constitution imbalances, particularly Phlegm stasis. These findings suggest a potential role for constitution-informed strategies in obesity-related public health approaches. Longitudinal studies are warranted to clarify temporal relationships and mechanisms. Clinical Trials Registration: ClinicalTrials.gov NCT03938207 (Study Start: 1 October 2022). Full article

DNA methylation is a crucial epigenetic mechanism that modifies the epigenome without altering the DNA sequence, leading to heritable changes in gene expression and playing a vital role in biological processes. The regulation of DNA methylation has gained significant attention in recent years for its role in inflammatory diseases, with numerous studies indicating a correlation between abnormal gene methylation and disease development. However, current research on mature methylation-regulation drugs remains in its infancy. Traditional Chinese medicine (TCM) has been demonstrated to have a potential therapeutic effect in treating inflammatory diseases by modulating DNA methylation. In this review, we provide an overview of how DNA methylation participates in inflammatory diseases and how TCM assesses its function in regulating DNA methylation modifications. We aim to demonstrate a theoretical foundation for further research on the therapeutic targets and mechanisms of TCM in inflammatory disease treatment. Full article

Diabetic nephropathy (DN) represents a severe microvascular complication of diabetes mellitus. As a Traditional Chinese Medicine (TCM) with extensive clinical applications, Ligustri Lucidi Fructus (LLF) exhibits significant anti-DN activity. However, the underlying pharmacological mechanisms, crucial components, and targets for LLF in DN treatment remain unclear. By integrating network pharmacology, molecular docking, and molecular dynamics simulations, the bioactive compounds, potential therapeutic targets, and underlying mechanisms of LLF in the treatment of DN were elucidated, followed by biological validation in a palmitic acid (PA)-induced MPC5 podocyte injury model. Among the 383 DN-related LLF targets identified, TNF emerged as a pivotal one, demonstrating potential binding interaction with the active components salidroside (Sal), apigenin (Api), and tormentic acid (TA). Moreover, Gene Expression Omnibus (GEO) database and KEGG enrichment analysis collectively highlighted the cytosolic DNA-sensing pathway. Notably, the cGAS-STING pathway is central to this pathway. Experimental studies further demonstrated that LLF-containing serum exerted a protective effect on MPC5 podocytes through cGAS-STING pathway suppression. Overall, these findings elucidate the pleiotropic mechanisms underlying LLF’s protective effects against DN, integrating compound–target–pathway interactions and thus offering a rationale for further investigation. Full article

Traditional Chinese medicine (TCM), as a vital component of traditional healthcare systems, relies heavily on its chemical constituents, which serve as a bridge between ancient therapeutic theories and modern biomedical science. Efficient access to compound-related information is crucial for promoting the modernization and scientific understanding of TCM. However, existing approaches primarily rely on fragmented databases and literature-based retrieval methods, which suffer from low intelligence, poor data integration, and limited retrieval efficiency.This study presents a novel AI agent-based retrieval system tailored for compound information in TCM. The core innovation of the system lies in its hybrid retrieval-augmented generation framework, which seamlessly combines structured database queries with semantic vector retrieval. Furthermore, it integrates knowledge from three complementary sources—locally built knowledge bases, domain-specific APIs, and open web search—allowing for comprehensive coverage and adaptive handling of diverse natural language queries. Experiments conducted on a benchmark dataset of 150 compound-related queries demonstrate that the system achieves a peak accuracy of 96.67% across multiple mainstream LLMs. Ablation studies further reveal that removing either the hybrid RAG or multi-source knowledge module leads to a notable accuracy decline, while the full system outperforms typical RAG baselines by over 25%. These results confirm the effectiveness and robustness of the proposed architecture in TCM compound retrieval, and highlight the advantage of combining structured matching with dynamic knowledge access in specialized biomedical applications. Full article

Cellular therapeutics, encompassing stem cell-based regeneration and engineered immune cell platforms, have demonstrated efficacy in treating degenerative diseases, immune-related diseases, and oncology. However, low engraftment rates and limited long-term efficacy remain critical translational barriers. This review compiled clinical projects on cell therapy in China over the past five years (over 1200 patients across 172 clinical trials) to highlight its rapid development in recent years and illustrate the directions of indications for application. This review also analyzes published clinical achievements all over the world, revealing significant therapeutic improvements in degenerative disorders (40–60% improvement in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and oncology (78% ctDNA clearance, p < 0.001)). We propose integrating traditional Chinese medicine (TCM) bioactive compounds to enhance cell viability via C-X-C motif chemokine receptor (CXCR4) upregulation and mitochondrial biogenesis. Despite mechanistic insights, translational barriers include limited TCM validation (72% lacking single-cell omics) and regulatory misalignment. Future efforts should prioritize randomized trials and standardized TCM-cell therapy protocols to bridge discovery and clinical translation. Full article

Background/Objectives: Epileptic seizures significantly impact patients’ lives due to their unpredictability, making early and accurate detection crucial for effective treatment. Machine learning (ML) models based on electroencephalogram (EEG) signals have been explored for automated seizure detection. This meta-analysis reviews the performance of ML models in seizure detection and analyzes factors such as the model type (deep learning vs. traditional ML), data preprocessing methods, and dataset types. Aim: This study aims to provide an evidence-based foundation for the future development of intelligent tools by evaluating the performance of ML models in detecting epileptic seizures through a meta-analysis. Methods: A systematic search of multiple databases up to April 2025 identified 60 studies and 93 datasets. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated using Stata 17.0. Subgroup analyses were performed to identify sources of heterogeneity. Publication bias was assessed using Deek’s test and funnel plots. Results: The pooled sensitivity, specificity, and AUC were 0.96 (95% CI 0.95–0.97), 0.97 (95% CI 0.96–0.98), and 0.99 (95% CI 0.98–1.00), respectively, indicating a good performance of ML in seizure detection. Subgroup analyses revealed that the model type, data preprocessing methods, and dataset type contributed to heterogeneity. Conclusions: ML shows a strong potential for EEG-based seizure detection. Imaging devices integrating ML may serve as effective tools for early epilepsy diagnosis. However, larger, multicenter clinical studies are needed to validate these algorithms and enhance their interpretability, safety, and applicability in real-world clinical settings. Full article

Radix Pseudostellariae (Tai-Zi-Shen), the dried tuberous root of the Caryophyllaceae plant Pseudostellaria heterophylla (Miq.) Pax (P. heterophylla), has been widely used in traditional Chinese medicine (TCM) for thousands of years. It is slightly bitter, neutral in nature, associated with the spleen and lung meridians, and used for nourishing qi, invigorating the spleen, as well as promoting body fluid production and moistening the lungs. In recent years, with the development in natural product chemistry, cyclic peptides, as some of the active constituents derived from P. heterophylla, have gained increasing attention. These cyclic peptides demonstrate a broad range of biological activities, including anticancer, antioxidant, and immunomodulatory effects, as well as cognitive benefits. This review provides an overview of the chemical characteristics and preparation strategies of cyclic peptides from P. heterophylla, and their biological activities and potential mechanisms are also described. The presented work establishes a scientific framework to facilitate the future research and development of P. heterophylla cyclic peptides as potential therapeutic agents. Full article

Gut microbiota dysbiosis is intricately linked to metabolic disorders such as obesity, type 2 diabetes mellitus (T2DM), hyperlipidemia, and non-alcoholic fatty liver disease (NAFLD). Traditional Chinese medicine (TCM), particularly when combined with probiotic fermentation, offers a promising therapeutic strategy by modulating microbial balance and host metabolism. This narrative review synthesizes current research on probiotic-fermented herbal bioactives, focusing on their mechanisms in ameliorating metabolic diseases. Probiotic and bioactive compounds (e.g., berberine, polysaccharides) are highlighted for their roles in enhancing intestinal barrier function, regulating microbial metabolites like short-chain fatty acids (SCFAs), and reducing inflammation. Fermentation techniques improve the bioavailability of TCM components while reducing toxicity, as seen in fermented Salvia miltiorrhiza and Rhizoma Coptidis. Despite promising results, challenges include the complexity of microbiota–host interactions and variability in TCM standardization. Future directions emphasize integrating multi-omics technologies and personalized approaches to optimize probiotic-fermented TCM therapies. This review underscores the potential of combining traditional herbal wisdom with modern biotechnology to address metabolic disorders, which pose significant global health challenges, through a “gut microbiota–metabolism” axis. Emerging evidence highlights the critical role of gut microbiota dysbiosis in the pathogenesis of these conditions. TCM has shown promise in modulating gut microbiota to restore metabolic homeostasis. This review synthesizes current research on TCM-derived interventions, such as herbal compounds, probiotics, and fermentation techniques, that target gut microbiota to ameliorate metabolic disorders. We discuss mechanisms of action, including prebiotic effects, enhancement of intestinal barrier function, and regulation of microbial metabolites, while addressing the limitations and future directions of TCM-based therapies. Full article

Background: Assisted reproductive technologies (ARTs) are essential in treating infertility but often face limited success due to low implantation and live birth rates. East Asian traditional medicine (EATM), including acupuncture and herbal medicine (HM), may enhance physiological responses during ART cycles. This study evaluated the effectiveness and safety of EATM in improving clinical pregnancy and live birth outcomes in women undergoing ART. Methods: This review, registered in PROSPERO (CRD42023411712), systematically searched 11 databases up to 31 March 2023. We included randomized controlled trials (RCTs) comparing EATM interventions to control groups. Data extraction and quality assessment were performed independently by two authors. Meta-analysis used the inverse-variance method in Stata 12.0. A total of 37 RCTs involving 10,776 women (aged 29–38) were analyzed. Studies addressed infertility causes including polycystic ovary syndrome, tubal blockage, diminished ovarian reserve, and unexplained infertility. Acupuncture therapies included body, electro-, laser, and auricular acupuncture. Herbal treatments were administered as powders, pills, granules, decoctions, and ointments based on traditional Chinese formulas. Results: EATM interventions were associated with significant improvements in clinical pregnancy and live birth rates. Acupuncture increased clinical pregnancy rates (CPR: RR 1.316, 95% CI 1.171–1.480) and live birth rates (LBR: RR 1.287, 95% CI 1.081–1.533). HM also enhanced CPRs (RR 1.184) and LBRs (RR 1.147). Subgroup analysis showed true acupuncture and HM were more effective than sham or placebo. No significant differences in adverse events were found. Conclusions: EATM, particularly acupuncture and HM, appears to be a safe and effective complementary therapy that can be used to improve ART outcomes. Future research should focus on developing standardized acupuncture and herbal protocols to optimize integration with ART. Full article