ObjectiveTo investigate the expression and distribution changes of G protein-gated inwardly rectifying potassium （GIRK） channel subunits GIRK1 and GIRK2 following morphine tolerance using human embryonic kidney 293 （HEK293） cells. MethodsHEK293 cells were transfected with the lentiviral vector pLV-CMV-GIRK1-T2A-GIRK2-P2A-MOR to establish a stable cell line overexpressing GIRK1， GIRK2， and the μ-opioid receptor （MOR）.Morphine （1 μmol/L， 24 h） was applied to these stable cells to construct a morphine-tolerant HEK293 cell model.Cellular immunofluorescence was used to examine the distribution and co-localization of MOR with GIRK1 and GIRK2.ELISA was performed to measure cAMP levels to confirm the establishment of the morphine-tolerant model.Cellular immunofluorescence and Western blot were employed to analyze the distribution and protein expression changes of GIRK1 and GIRK2 after morphine tolerance.A multimode microplate reader was used to measure fluorescence intensity for detecting changes in cellular membrane potential following morphine tolerance. ResultsIn the stable HEK293 cells， immunohistochemistry showed that GIRK1 and GIRK2 were primarily expressed on the plasma membrane， with minimal presence in the cytoplasm.Both GIRK1 and GIRK2 co-localized with MOR and with each other.Compared with the control group， cAMP levels significantly decreased after 1 h of morphine treatment （1.42±0.07 vs.0.72±0.12， P=0.001 0）， while they significantly increased after 24 h of treatment （0.72±0.12 vs.1.98±0.17， P=0.000 5）.Fluorescence double staining revealed that after 24 h of morphine treatment， the morphine-tolerant group showed a significant increase in cytoplasmic GIRK1 （13.76±7.67 vs.63.72±16.02， P＜0.000 1） and GIRK2 （7.16±2.61 vs.32.92±7.67， P=0.002 9）.Western blot analysis indicated that the expression of membrane proteins GIRK1 （1.11±0.14 vs.0.85±0.01， P=0.004 5） and GIRK2 （1.32±0.02 vs.0.86±0.08， P=0.000 1） was significantly downregulated after morphine tolerance.Membrane potential measurements showed that the hyperpolarization response in morphine-tolerant cells was significantly attenuated （-15.53±0.12） % vs.（-8.17±0.11） %， P＜0.000 1. ConclusionChronic morphine treatment can induce downregulation of GIRK1 and GIRK2 expression on the plasma membrane in stable HEK293 cells.

OBJECTIVE To develop evidence-based clinical practice recommendations for the use of hemostatic drugs in special populations， so as to improve the efficacy and safety of medication.METHODS Led by the Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital， a working group of 79 experts covering clinical pharmacy， obstetrics and gynecology， pediatrics and other fields was established. Clinical questions regarding the efficacy and safety of hemostatic drugs in special populations were screened through literature review and two rounds of Delphi questionnaire surveys. Intervention questions were structured using the population， intervention， comparison， and outcome framework， while management questions were additionally described using the population， clinical setting, management measure， and outcome framework. Databases including Medline， Embase， Cochrane Library， CNKI， Wanfang Data， and VIP were systematically searched to collect and screen relevant evidence. Recommendations were formulated through recommendation questionnaire surveys and expert consensus meetings， and the quality of evidence and strength of recommendations were graded according to the Grading of Recommendations Assessment， Development and Evaluation system. RESULTS &CONCLUSIONS A total of 8 clinical questions were finally determined， the consensus rates of all clinical questions and recommendations were more than 90%， and 8 recommendations on the use of hemostatic drugs in special populations were formulated. These recommendations cover special populations including children， women during pregnancy/puerperium/lactation， the elderly， patients with hepatic impairment， patients in the perioperative period of liver transplantation， patients with renal impairment/on dialysis/in the perioperative period of kidney transplantation， immunocompromised patients， and patients with hematological diseases. The recommendations specify the applicable scenarios， dose adjustment principles， monitoring indicators， and safety management strategies of hemostatic drugs in different special populations， which can provide a reference for the clinical application of hemostatic drugs in special populations and help clinicians and pharmacists develop individualized medication plans.

Objective:To investigate the mosquito-borne viruses carried by mosquito specimens collected from Huachuan county and Huanan county in Heilongjiang province.Methods:Mosquito samples were collected locally in 2023 and processed in the laboratory. Homogenates of the mosquitoes were inoculated into cells for virus isolation, followed by molecular and bioinformatics analyses of the viral isolates.Results:In 2023, ten viral isolates were obtained from Anopheles sinensis specimens collected in Heilongjiang province, China. Among these isolates, one was identified as Culex flavivirus (CxFV), one as Menghai rhabdovirus (MRV), and eight as Nam Dinh virus (NDiV). The phylogenetic analysis showed that CxFV belongs to genotype I and is clustered with the strains isolated from Liaoning province in 2011 and Ningxia Hui autonomous Region in 2019 in the same evolutionary branch, with amino acid similarity ranging from 98.2% to 99.2% and nucleotide similarity ranging from 98.8% to 99.2%. The MRV strain belongs to the same evolutionary subclade as the strain detected in Guangdong, with both nucleotide and amino acid similarity of 98.0%. Eight NDiV isolates clustered with the South Korean isolates on the same evolutionary branch, forming an independent evolutionary sub-branch. The nucleotide similarity among these eight isolates ranged from 98.5% to 99.7%, while the amino acid similarity ranged from 98.1% to 99.7%. In comparison, when matched with other NDiV isolates from China, the nucleotide similarity of these eight isolates ranged from 94.1% to 97.8%, and the amino acid similarity ranged from 93.5% to 97.7%.Conclusions:This study represents the first isolation of CxFV, MRV, and NDiV in Heilongjiang province, China, and the findings provide fundamental data for the prevention and control of mosquito-borne viral diseases in this region.

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.

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*

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 pri-mary targets of cardiotoxicity,has garnered widespread attention.Traditional cardiotoxicity testing methods are expensive and time-consuming,making computational virtual screening a suitable alter-native.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 Trans-former 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.