OBJECTIVE To investigate the protective effect of 2-dodecyl-6-methoxy-2，5-diene-1，4-cyclohexanedione （DMDD）， an active component from Averrhoa carambola root， on myocardial injury in diabetic mice based on the nuclear receptor coactivator 4/ferritin heavy chain 1/autophagy-related protein 8 （NCOA4/FTH1/ATG8） axis. METHODS The successfully modeled diabetic mice were randomly divided into model group and DMDD low-， medium-， and high-dose （12.5， 25， 50 mg/kg） groups， while an additional non-modeled control group was established， with 6 mice in each group. Each group received the corresponding drug solution or an equal volume of normal saline intragastically once daily for 21 consecutive days. After the administration， the levels of fasting blood glucose （FBG）， serum lactate dehydrogenase （LDH）， and creatine kinase isoenzyme MB （CK-MB） were measured. Myocardial pathological changes， degree of fibrosis， and myocardial cell ultrastructure were observed. Myocardial cell death index and NCOA4 protein positive index were detected. The protein expression levels of NCOA4， FTH1， ATG8， solute carrier family 7 member 11 （SLC7A11）， and glutathione peroxidase 4 （GPX4） in cardiac tissue were measured. RESULTS Compared with model group， each DMDD group showed significant alleviation of cardiac pathological injury and varying degrees of improvement in the myocardial cell ultrastructure. The FBG and serum LDH and CK-MB levels， the myocardial cell death index and NCOA4 protein positive index，the protein expression levels of NCOA4， FTH1， and ATG8 in cardiac tissue were significantly decreased （ P ＜0.001）， while the protein expression levels of SLC7A11 and GPX4 were significantly increased （ P ＜0.001）. CONCLUSIONS DMDD can reduce blood glucose levels， alleviate myocardial histopathological injury， and inhibit cell death in diabetic mice. The mechanism is associated with inhibiting excessive activation of the NCOA4/FTH1/ATG8 axis and reducing ferritinophagy.

Driven by policy initiatives promoting the integration of digital-intelligent technologies with primary healthcare and the digital transformation of traditional Chinese medicine (TCM), Smart TCM has emerged as a pivotal strategy for enhancing primary healthcare services for major chronic diseases. This paper reviews the current status, challenges, and feasible pathways of Smart TCM in community-level management of cardiovascular–cerebrovascular diseases and tumors, which represent major chronic disease burdens. Our findings indicate that Smart TCM demonstrates emerging potential in primary healthcare for chronic diseases across the entire continuum of “prevention-screening-diagnosis-treatment-management.” However, several significant challenges persist, including data silos and security vulnerabilities, limited applicability of existing models to real-world clinical needs, and insufficient digital literacy among primary healthcare physicians and elderly patients. To address these constraints, this paper proposes a multidimensional strategy encompassing the development of secure and interoperable regional data platforms, lightweight intelligent devices and support services aligned with primary care capacity, unified technical and data standards with corresponding quality control systems, adaptive and dynamically updated artificial intelligent models, interdisciplinary workforce training and patient education programs, and enhanced policy and health insurance support. Overall, Smart TCM shows great promise for improving the efficiency of primary healthcare delivery and establishing innovative TCM-based chronic disease management paradigms.

ObjectiveTo investigate the efficacy and safety of Shuganning injection in the treatment of liver injury by observing the clinical outcome of patients with liver injury after treatment with Shuganning injection and comparing it with reduced glutathione （GSH）. MethodsThis study was conducted among 175 patients with liver injury who attended 6 hospitals in China （Beijing YouAn Hospital， Capital Medical University； Nanyang First People’s Hospital； The Second Affiliated Hospital of Xi’an Jiaotong University； The Fifth Medical Center of Chinese PLA General Hospital； The Third Hospital of Hebei Medical University； The Affiliated Hospital of Guizhou Medical University） from April 25， 2019 to February 28， 2022. The patients were divided into experimental group （89 patients treated with Shuganning injection） and control group （86 patients treated with GSH）， and the course of treatment was 4 weeks for both groups. Related indicators were recorded before treatment， after treatment， and after drug withdrawal for 8 weeks， including liver function parameters， blood biochemical parameters， blood lipids， coagulation profiles， liver fibrosis markers， traditional Chinese medicine （TCM） syndrome score， and ultrasound findings， as well as adverse events during treatment. The Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The Wilcoxon signed-rank test was used for comparison of observation indicators before treatment， at week 4 of treatment， and after drug withdrawal for 8 weeks. When the efficacy evaluation of TCM syndromes was used as the outcome， a pooled analysis was performed for the effective cases and the markedly effective cases， and a Logistic regression model was used to calculate the odds ratio （OR） and its 95%CI. ResultsAfter 4 weeks of treatment with Shuganning injection， the patients showed significant reductions in TCM syndrome score （P<0.001） and the levels of aspartate aminotransferase， alanine aminotransferase， total bilirubin， and direct bilirubin （all P<0.001） and a significant increase in the level of albumin （P=0.002）， and these significant improvements persisted after drug withdrawal for 8 weeks. Furthermore， compared with the control group， the experimental group had a significantly higher proportion of patients assessed as effective or markedly effective cases （48.31% vs 32.56%， P=0.033）. Compared with the control group， the experimental group had a significantly greater change in TCM syndrome score at weeks 1-4 of treatment and after drug withdrawal for 8 weeks （P<0.05）. At week 4 of treatment， the experimental group had a significantly greater reduction in alanine aminotransferase than the control group （P<0.05）， and at week 8 after drug withdrawal， the experimental group had a significantly greater reduction in alanine aminotransferase and aspartate aminotransferase than the control group （all P<0.05）. One patient in the experimental group （1.12%） experienced an adverse event， which was determined to be associated with the progression of viral hepatitis. ConclusionShuganning injection can effectively improve liver injury， with good long-term efficacy and a low incidence rate of adverse reactions.

ObjectiveTo explore the ethical issues and coping strategies in the diagnosis and treatment of 46, XY disorders of sex development （46, XY DSD） under a multidisciplinary team （MDT） model， and to deepen the understanding of relevant ethical issues while accumulating experience. MethodsA retrospective collection was conducted on the data of children with 46, XY disorders of sex development under a single-center MDT model from January 2017 to December 2024， and the ethical issues involved in the diagnosis and treatment process were analyzed. ResultsA total of 24 MDT discussion documents involving 20 children were collected， including 17 with female social gender and 3 with male social gender. The age ranged from 0.5 to 15.8 years， with children under 8 years accounting for 75%. The ethical issues primarily involved how to ensure adequate informed consent， whether surgery was necessary and its timing， and whether gender selection is reasonable and its timing. Among the 10 children raised as females who completed the gender role psychological scale assessment， 5 demonstrated masculine tendencies， all of whom had pathogenic genetic variants. Furthermore， only 1 patient changed the social gender to male. ConclusionThe diagnosis and treatment of 46, XY DSD is relatively complex， requiring multidisciplinary collaboration. This process involves numerous ethical issues and necessitates adhering to medical ethical principles. Ensuring adequate informed consent is crucial， especially for younger children. The surgery and its timing selection must adhere to the principles of beneficence， non-maleficence， and leaving room for the future. Gender selection constitutes a focus and challenge in diagnosis and treatment. Psychological scales and genetic etiologies are conducive to judgments regarding gender selection， yet multiple factors in the real world may interfere with the gender selection.

OBJECTIVE To investigate the mechanism of Shenling baizhu san （SLBZS） in ameliorating ulcerative colitis （UC）. METHODS Mice were randomly divided into normal group， model group， and low-， medium-， high-dose SLBZS groups （3， 6， 12 g/kg）， with 10 mice in each group. Except for the normal group， mice in other groups were given free access to 3% dextran sulfate sodium solution for 7 days to establish UC models. Simultaneously， mice in each group were intragastrically administered corresponding medicinal liquid or double-distilled water once a day for consecutive 7 days. After the last administration， the histopathological morphology of colon tissues was observed； the serum levels of inflammatory factors including tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β） and IL-18 were detected； the total bile acid contents in liver， gallbladder and colon tissues were determined； the expression level of NOD-like receptor family pyrin domain-containing protein 3 （NLRP3） in colon tissues was tested. The expression levels of flora metabolism-related proteins [G protein-coupled receptor 41（GPR41），GPR43，farnesoid X receptor （FXR）， G protein-coupled bile acid receptor 1 （TGR5）， flavin-containing monooxygenase 3 （FMO3）， cytochrome P450 family 27 subfamily A member 1 （CYP27A1）， CYP7A1] in liver or colon tissues， as well as the proteins related to Toll-like receptor 4 （TLR4）/nuclear factor-κB （NF-κB）/NLRP3 inflammatory axis [TLR4， NF-κB， phosphorylated NF-κB （p-NF-κB）， myeloid differentiation factor 88 （MyD88）， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， caspase-1] in colon tissues were measured. RESULTS Compared with the model group， the colon tissue injury and inflammatory cell infiltration were alleviated in medium- and high-dose SLBZS groups. The serum levels of TNF-α， IL-1β and IL-18， total bile acid contents in liver and colon tissues， as well as the protein expression levels of FMO3 and CYP7A1 in liver tissues， TLR4， p-NF-κB， NF-κB， MyD88， NLRP3， ASC and caspase-1 in colon tissues were significantly decreased （P＜0.05）. Meanwhile， the total bile acid content in gallbladder， the protein expression levels of GPR41 and GPR43 in colon tissues， along with CYP27A1， FXR and TGR5 in liver tissues were significantly increased （P＜0.05）. CONCLUSIONS SLBZS may relieve colonic inflammatory injury and ameliorate UC by regulating the expression of flora metabolism-related proteins and inhibiting the abnormal activation of TLR4/NF-κB/NLRP3 inflammatory axis.

Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

Objective:The aim of this study is to evaluate the clinical efficacy of Oxalicao Formula combined with transacu-puncture in the treatment of chronic nonbacterial prostatitis(CNP)characterized by dampness-heat stasis.Methods:A total of 70 patients diagnosed with CNP and characterized by dampness-heat stasis were randomly divided into control group and treatment group,with 35 cases in each group.The patients in control group received Qianlie Beixi capsules.While the patients in treatment group were administered with oxalis decoction in conjunction with acupuncture therapy which lasted for 8 weeks.Pre-and post-treatment evalua-tions for NIH-Chronic Prostatitis Symptom Index(NIH-CPSI),Traditional Chinese Medicine(TCM)symptom scores,urodynamic pa-rameters,immune cell subsets and inflammatory factors were performed.Results:Ultimately,65 patients completed the study with 33 in the treatment group and 32 in the control group.After 8 weeks of intervention,the patients in both of groups demonstrated signifi-cant improvements(P＜0.05).Specifically,remarkable reductions in the NIH-CPSI total score including pain score,urination score,quality of life impact score,TCM symptom score and inflammatory cytokine levels were observed.Additionally,there were upward trends in maximum and average urinary flow rates as well as the CD4+/CD8+ratio of immune cells(P＜0.05).Compared to the con-trol group,the treatment group exhibited superior outcomes in reducing the NIH-CPSI total score,pain score,urination score,quality of life impact score,TCM symptom score,and inflammatory cytokine levels,and increasing in CD4+/CD8+ratios,maximum and av-erage urine flow rates(P＜0.05).Conclusion:The combination of Oxalicao Formula and transacupuncture for treating CNP charac-terized by dampness-heat stasis demonstrates significant therapeutic benefits,which has considerable clinical application value.

Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Glycoengineering was carried out in the mammalian cell line CHO for the production of pro-tein-based drugs.Firstly,the genome sequence of the Rosa26 locus of CHO cells was determined,the gRNA sequences were designed,and the landing pad was integrated into the Rosa26 locus of CHO cells by CRISPR/Cas9 technology.Three targeting vectors co-expressed by glycosyltransferases,which are β-1,4 galactosyltransferase(B4GALT1),α-2,6-sialyltransferase 1(ST6GAL1)and N-acetaminoglycosyl-transferase Ⅲ(GnT Ⅲ),were constructed by overlapping PCR and seamless ligation technology,and the three glycosyltransferase genes were integrated into the CHO Rosa26 locus by Cre enzyme-mediated cassette exchange technology.PCR confirmed that three glycosyltransferases had been successfully site-directed integrated into the Rosa26 site.The mRNA expression levels of the three glycosyltransferases were more than 50 000-fold by qRT-PCR,and the protein expression levels of the three glycosyltrans-ferases were more than 4-fold via western blotting(P＜0.001).A CHO-engineered cell line with three glycosyltransferases integrated into Rosa26 site was successfully constructed.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.