Macroporous resin adsorption column chromatography, silica gel column chromatography, ODS column chromatography, and semi-preparative high-performance liquid chromatography, combined with analytical methods such as NMR and MS, were employed to separate and identify compounds from the 70% ethanol extract of Ajania purpurea. A total of 30 compounds were isolated and identified, including 13 phenolic acids, 7 coumarins, 2 lignans, 1 flavonoid, 2 sesquiterpenes, 1 steroid, and 4 others. Among them, compounds 1 and 2 were newly discovered compounds, and compounds 4, 6, 8, 12, 14-23, 25, 28, and 30 were isolated from Ajania plants for the first time. Bioactivity screening showed that multiple compounds significantly inhibited the production of nitric oxide in lipopolysaccharide-stimulated RAW264.7 cells in a dose-dependent manner. Furthermore, compound 2 elevated the levels of glutathione in LPS-induced BEAS-2B cells, reduced the expression of pro-inflammatory cytokines such as tumor necrosis factor(TNF)-α, interleukin(IL)-6, and IL-1β, enhanced the mRNA of GPX4, HMOX1, NFE2L2, and enhanced protein levels of GPX4, HO-1, Nrf2, and SLC7A11, demonstrating potential anti-ferroptotic effect.
Mice ; Animals ; Lignans/isolation & purification* ; RAW 264.7 Cells ; Humans ; Nitric Oxide ; Tumor Necrosis Factor-alpha/immunology* ; Drugs, Chinese Herbal/isolation & purification* ; NF-E2-Related Factor 2/metabolism* ; Macrophages/metabolism* ; Interleukin-6/immunology*

Colorectal cancer(CRC) is one of the most common malignant tumors worldwide, primarily originating from recurrent inflammatory bowel disease(IBD). Therefore, blocking the inflammation-cancer transformation in the colon has become a focus in the early prevention and treatment of CRC. The inflammation-cancer transformation in the colon involves multiple types of cells and complex pathological processes, including inflammatory responses and tumorigenesis. In this complex pathological process, immune cells(including non-specific and specific immune cells) and non-immune cells(such as tumor cells and fibroblasts) interact with each other, collectively promoting the progression of the disease. In traditional Chinese medicine(TCM), inflammation-cancer transformation in the colon belongs to the categories of dysentery and diarrhea, with the main pathogenesis being cold and heat in complexity. This paper first elaborates on the complex molecular mechanisms involved in the inflammation-cancer transformation process in the colon from the perspectives of inflammation, cancer, and their mutual influences. Subsequently, by comparing the pathogenic characteristics and clinical manifestations between inflammation-cancer transformation and the TCM pathogenesis of cold and heat in complexity, this paper explores the intrinsic connections between the two. Furthermore, based on the correlation between inflammation-cancer transformation in the colon and the TCM pathogenesis, this paper delves into the importance of the interaction between inflammation and cancer. Finally, it summarizes and discusses the clinical and basic research progress in the TCM intervention in the inflammation-cancer transformation process, providing a theoretical basis and treatment strategy for the treatment of CRC with integrated traditional Chinese and Western medicine.
Humans ; Colon/pathology* ; Integrative Medicine ; Animals ; Cold Temperature ; Cell Transformation, Neoplastic/drug effects* ; Medicine, Chinese Traditional ; Hot Temperature ; Inflammation ; Drugs, Chinese Herbal/therapeutic use* ; Colonic Neoplasms/drug therapy*

Objective:To analyze the current status of the research in two emerging fields-digital health and digital medicine,and the distinctions and interconnections between them,and to explore critical cognitionfor benign development of this field.Methods:Based on bibliometric method,this study analyzed 1 747 papers published in two Q1 journals categorized under"medical informatics"in Chinese Academy of Sciences Journal Citation Reports between January 1st,2019,and June 30th,2024.A biclustering analysis of highly cited and source article was conducted using a"co-cited literature clustering and all articles citing this cluster"approach.Results:In 1 747 literature,a total of 28 classic high cited articles in digital medicinejournals and digital health journals were screened out afterthe literatures of irrelevant citations were deleted,and then,they were further analyzed.The clustering performancesof similar clusters for high cited articles of two kinds of journals were similar,and the difference of intra-cluster similarity(Isim)between them was not statistically significant(P>0.05).The inter-cluster similarity(Esim)of digital health journal was 0.0480,which was significantly higher than that of digital health journal,and the difference of that between them wasstatistically significant(t=-3.925,P<0.05).The clustering result of digital medicine was better,which whole research direction could be divided into deep learning-based ophthalmic medical image analysis,image recognition with artificial intelligence(AI),algorithm model of using AI,reporting standard and assessment method of digital medicine and scientific research by using AI.However,the contents of high cited literatures of digital health field had a lot of overlap with digital medicine field,and various clusters of literature have inconsistent themesafter these literatures were clustered.Conclusions:Research of digital medicine is tending towards two primary directions:specific application of AI,and theimprovement of digital medical standards and processes.Meanwhile,the articles and literatures with high quality in digital health still cannot divorce the medical field,and the discipline system still is immature.

AIM To explore the effects of Hofmeister series ions on encapsulation efficiencies of cinnamaldehyde,paeonol,and ligustilide in inclusion complex of volatile oils from Wenjing Decoction.METHODS The volatile oils were extracted,after which the β-cyclodextrin inclusion complex was prepared,the thermal stability was evaluated,and encapsulation efficiencies and inclusion complex constants of various volatile components in Na2SO4,NaH2PO4,NaCl,NaI,NaSCN solutions were determined.RESULTS The β-cyclodextrin inclusion complex demonstrated good thermal stability within 10 d.After the addtion of Hofmeister series ions,various volatile components displayed increased encapsulation efficiencies and inclusion complex constants,and concentration-dependent manner was observable in the latter.CONCLUSION Hofmeister series ions can affect the binding affinities of volatile components and β-cyclodextrin in volatile oils from Wenjing Decoction,thus regulate their encapsulation efficiencies.

Cardiotoxicity is a critical issue in drug development that poses serious health risks, including potentially fatal arrhythmias. The human ether-à-go-go related gene (hERG) potassium channel, as one of the primary targets of cardiotoxicity, has garnered widespread attention. Traditional cardiotoxicity testing methods are expensive and time-consuming, making computational virtual screening a suitable alternative. In this study, we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds, with the aim of improving prediction accuracy and efficiency. We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. Our models demonstrated advanced predictive performance. The best machine learning model, XGBoost Morgan, achieved an accuracy (ACC) value of 0.84, and the deep learning model, Transformer_Morgan, achieved the best ACC value of 0.85, showing a high ability to distinguish between toxic and non-toxic compounds. On an external independent validation set, it achieved the best area under the curve (AUC) value of 0.93, surpassing ADMETlab3.0, Cardpred, and CardioDPi. In addition, we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods, such as voting and stacking, provided slight improvements in model stability. Furthermore, the SHapley Additive exPlanations (SHAP) explanations revealed the relationship between benzene rings, fluorine-containing groups, NH groups, oxygen in ether groups, and cardiotoxicity, highlighting the importance of these features. This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment. Using computational methods, this study facilitates a more efficient drug development process, reduces costs, and improves the safety of new drug candidates, ultimately benefiting medical and public health.

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

BACKGROUND: Acupuncture therapy provides a complementary and alternative approach to treating major depressive disorder (MDD), but its efficacy and safety have still not been comprehensively assessed. Recently published systematic reviews remain confusing and inconclusive. OBJECTIVE: This systematic review evaluated the efficacy and safety of acupuncture therapy alone or combined with antidepressants for adult patients with mild and moderate MDD. SEARCH STRATEGY: Chinese Biomedical Literature Database, China National Knowledge Infrastructure Database, Wanfang Database, Chinese Science and Technology Journal Database, PubMed, Embase, and Cochrane Library were searched from their inceptions to March 2025. INCLUSION CRITERIA: Randomized controlled trials that compared acupuncture therapy with antidepressants, or acupuncture therapy plus antidepressants with acupuncture therapy or antidepressants for adult patients with mild and moderate MDD were included. DATA EXTRACTION AND ANALYSIS: Five reviewers independently extracted data from original literature using a standardized form, and the data were verified by two reviewers to ensure accuracy. Statistical meta-analyses, publication bias analyses, and subgroup analyses were performed by using Review Manager 5.3 software. The Grading of Recommendations Assessment, Development, and Evaluation approach was used to assess the certainty of the evidence. RESULTS: A total of 60 eligible studies including 4675 participants were included. Low-certainty evidence showed that compared with antidepressants, acupuncture therapy (standardized mean difference [SMD] = -0.57; 95% confidence interval [CI] = [-0.87, -0.27]; I² = 86%; P = 0.006) or acupuncture therapy plus antidepressants (SMD = -1.00; 95% CI = [-1.18, -0.81]; I² = 77%; P < 0.00001) may reduce the severity of depression at the end of treatment. Low-certainty evidence indicated that compared with acupuncture therapy alone, acupuncture therapy plus antidepressants slightly reduced the severity of depression at the end of treatment (SMD = -0.38; 95% CI = [-0.61, -0.14]; I² = 18%; P = 0.002). Similar results were also found for acupuncture's relief of insomnia. The reported adverse effects of acupuncture therapy were mild and transient. For most of the subgroup analyses, acupuncture type, scale type, and the course of treatment did not show a significant relative effect. CONCLUSION Acupuncture therapy may provide antidepressant effects and relieve insomnia with mild adverse effects for adult patients with mild and moderate MDD. But the certainty of evidence was very low. More high-quality, well designed, large-scale studies with long-term follow-up are needed in the future. Please cite this article as: Kuang HJ, Yang HS, Feng YX, Tang H, Fan Q, Xu YQ, Cui S, Musil R, Luxenburger H, Zhang YX, Zhao H, Zhang YQ. Efficacy and safety of acupuncture therapies for adult patients with mild and moderate major depressive disorder: A systematic review and meta-analysis. J Integr Med. 2025; 23(5):471-491.
Humans ; Acupuncture Therapy/methods* ; Depressive Disorder, Major/therapy* ; Adult ; Antidepressive Agents/therapeutic use* ; Treatment Outcome ; Randomized Controlled Trials as Topic

OBJECTIVE: To investigate the association of various polycyclic aromatic hydrocarbon (PAH) metabolites with diverse subtypes of cardiovascular disease (CVD) risk. METHODS: A novel predicting risk of cardiovascular disease EVENTs PREVENT equation was used to estimate the 10-year diverse subtypes of CVD risk, and their associations with PAH metabolites were analyzed using multiple logistic regression models, the weighted quantile sum (WQS) model, the quantile g-computation (qgcomp) model, and a stratified analysis of subgroups. RESULTS: For this study, six thousand seven hundred and forty-five participants were selected, and significant positive associations were observed between PAHs, naphthalene (NAP), and fluorene (FLU), and the risks of total CVD, atherosclerotic cardiovascular disease (ASCVD), and heart failure (HF). NAP and FLU were the primary contributors to the effects of PAH mixtures, and their associations with total CVD, ASCVD, and HF risk were significant in younger participants (30 ≤ age < 50 years); however, the associations of phenanthrene (PHEN) with ASCVD, HF, coronary heart disease (CHD), and stroke were dominant in aging participants (age ≥ 50 years). Notably, pyrene (PYR) was negatively associated with the risk of ASCVD, HF, CHD, and stroke. Similarly, negative associations of PYR with the four CVD subtypes were noticeable in aging participants. CONCLUSION Different PAHs metabolites had different impacts on each CVD subtype among different age groups. Notably, the protective effects of PYR on ASCVD, HF, CHD, and stroke were noticeable in aging individuals.
Humans ; Cardiovascular Diseases/chemically induced* ; Middle Aged ; Polycyclic Aromatic Hydrocarbons/metabolism* ; Male ; Female ; Adult ; Aged ; Risk Factors ; China/epidemiology*