The disease-syndrome combination animal model in traditional Chinese medicine （TCM） andrology serves as an important bridge linking TCM theory with modern medical research， providing a key experimental platform for elucidating the 'syndrome-disease' correlation mechanism in male-specific diseases and for screening effective prescriptions. This article reviews recent progress in animal model research on common TCM andrological diseases， including prostatic diseases， sexual dysfunction， and male infertility， with a focus on analyzing the application， advantages， and disadvantages of various modeling strategies， such as immune induction， hormonal intervention， and multi-factor combination across different syndrome types. However， despite breakthroughs in model construction techniques， current research still faces several challenges， including insufficient standardization of syndrome differentiation and difficulties in quantifying TCM-specific indicators. Future studies need to optimize model evaluation systems by integrating modern technologies， in order to promote the standardization and internationalization of TCM andrology research.

Objective: This prospective study evaluated the feasibility of chemical exchange saturation transfer (CEST) MRI for assessing disease activity in thyroid-associated ophthalmopathy (TAO). Materials and Methods: A total of 88 patients with active TAO, 76 with inactive TAO, and 30 healthy controls were enrolled. CEST MRI-derived magnetization transfer ratio (MTR) and MTR asymmetry (MTRasym) at 1 ppm, 2 ppm, and 3.5 ppm were calculated. Clinical data, MTR, and MTRasym values for the extraocular muscles (one representative muscle per eye, yielding two measurements per participant) were compared among the groups. Spearman’s correlation was used to examine associations between imaging parameters and the clinical activity score (CAS) in patients with TAO. Logistic regression analysis was used to identify independent associations between imaging parameters and disease activity in patients with TAO (active vs. inactive). Receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic performance for discriminating active from inactive TAO. Results: Patients with active TAO showed lower MTR values (P < 0.001) and higher MTRasym (1 ppm), MTRasym (2 ppm), and MTRasym (3.5 ppm) (all P < 0.001) compared with those with inactive TAO. MTR was negatively correlated with CAS (r = -0.402; P < 0.001), while MTRasym (1 ppm), MTRasym (2 ppm), and MTRasym (3.5 ppm) were positively correlated with CAS (r = 0.369, 0.350, and 0.349, respectively;all P < 0.001). MTR and MTRasym (1 ppm) were independently associated with TAO activity. The areas under the ROC curve (AUCs) for MTR and MTRasym (1 ppm) in discriminating active from inactive TAO were 0.772 and 0.730, respectively. Combining MTR with MTRasym (1 ppm) significantly improved diagnostic performance compared with either parameter alone, achieving an AUC of 0.805 (P = 0.029 and 0.001). Conclusion MTR and MTRasym (1 ppm) were independently associated with TAO activity. Their combination further enhanced diagnostic performance in distinguishing active from inactive TAO, suggesting their potential as quantitative imaging biomarkers to guide treatment in patients with TAO.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective: To examine the prevalence of e-cigarette use and associated factors among primary and secondary school students in Pudong New Area, Shanghai, and to explore the pathways linking harm perception of e-cigarettes, interpersonal social influence, and attitudes toward e-cigarette use with experimentation behavior, in order to provide scientific basis for further optimizing the prevention and control strategies of e-cigarette among adolescents. Methods: From September to October 2025, a multi stage cluster random sampling method was used to select 5 144 primary and secondary school students aged 8-19 years from 47 primary and secondary schools in Pudong New Area, Shanghai, to conduct an anonymous questionnaire survey. The questionnaire collected information on e-cigarette use, harm perception of e-cigarette, interpersonal social influence, and attitudes toward e-cigarette use. The Chi-square test was applied to analyze the differences in cigarette and e-cigarette use behavior among primary and secondary school students with different demographic characteristics. Structural equation modeling (SEM) was constructed using Mplus 8.3 software, and the bootstrap method was utilized to test the mediating effects. Results: The reported rates of cigarette experimentation, e-cigarette experimentation, and current e-cigarette use among primary and secondary school students were 1.85%, 2.10%, and 0.70%, respectively. Higher reporting rates of e-cigarette experimentation were observed among boys (2.82%), secondary school students aged 16-19 (3.39%), senior high school students (3.05%), and those with weekly pocket money >100 yuan ( 6.11% ) ( χ 2=11.67, 8.61, 8.00, 54.18, all P <0.05). Structural pathway analysis results demonstrated that e-cigarette harm perception positively predicted interpersonal social influence ( β =0.61) and e-cigarette use attitude ( β = 0.53 ), and interpersonal social influence ( β =0.65) and e-cigarette use attitude ( β =0.25) further promoted e-cigarette experimentation behavior among primary and secondary school students (all P <0.01), with interpersonal social influence playing a major mediating role (indirect effect=0.40). Conclusions E-cigarette experimentation behavior among primary and secondary school students in Pudong New Area is jointly influenced by multiple psychosocial factors. Intervention strategies should strengthen interpersonal social factors such as family and peers on the basis of health education, thereby constructing a comprehensive prevention and control strategy for e-cigarette use among primary and secondary school students.

Objective This study conducted molecular biological identification of the viruses carried by Culex pipiens pallens specimens collected in Shunyi District, Beijing in 2023, and observed the changes in the infection rate of the viruses carried by Cx. pipiens pallens at different collection times. Methods Cx. pipiens pallens were collected using carbon dioxide mosquito traps. The mosquito samples were ground in batches and analyzed by molecular biology technologies. The virus infection rate at different collection times was analyzed statistically. Results 17 strains of Culex pipiens pallens densovirus(CppDNV)were identified from Cx. pipiens pallens samples collected in Shunyi District, Beijing, in 2023. The nucleotide sequence analysis of the virus genome coding region showed that CppDNV was a single-stranded DNA virus with a total length of 3 335 nt, encoding 2 non-structural proteins(NS1, NS2)and 1 capsid protein(VP). The nucleotide(amino acid)sequence lengths of the three proteins were 2 376 nt(791 aa),1 092 nt(363 aa)and 1 071 nt(356 aa), respectively. Phylogenetic analysis showed that CppDNV was located in genus Brevihamaparvovirus. Statistical analysis showed no significant difference in infection rates across collection times(χ2=4.429, P=0.194). Conclusions CppDNV was identified in Cx. pipiens pallens in Beijing, and it was stably maintained in this natural population.

Objective:To study echinococcosis in the population and canine Echinococcus infection in Yushu City, Qinghai Province, and to explore the current epidemic situation and main transmission species of Echinococcus. Methods:In June 2023, a multi-stage sampling method was used to select 2 villages each in Shanglaxiu Township and Longbao Town, Yushu City, Qinghai Province. Each village included at least 100 permanent residents who had lived locally for at least 1 year and were 2 years old or older as the survey subjects. Enzyme-linked immunosorbent assay (ELISA) was used to detect serum antibodies against Echinococcus larvae in the population, and B-mode ultrasound was used for abdominal organ scanning. Meanwhile, on the main roads of Shanglaxiu Township and Longbao Town, canine feces were collected in designated areas at intervals. ELISA was used to detect the antigen of canine fecal Echinococcus, and PCR was used to detect the types of parasites ( Echinococcus multilocularis, Echinococcus granulosus and Echinococcus shiquicus). Results:A total of 511 residents were investigated in Yushu City, and the positive rate of serum Echinococcus larvae antibodies in the population was 26.22% (134/511), and the detection rate of echinococcosis B-mode ultrasound was 1.37% (7/511). Among them, the detection rates of B-mode ultrasound for cystic echinococcosis (CE) and alveolar echinococcosis (AE) were 1.17% (6/511) and 0.20% (1/511), respectively. The positive rate of Echinococcus antigen in 543 canine feces detected by ELISA was 12.89% (70/543). PCR was used to test 497 canine feces, and the detection rate of Echinococcus was 3.02% (15/497). Among them, the detection rate of Echinococcus multilocularis was higher than that of Echinococcus granulosus [2.82% (14/497) vs 0.20% (1/497)], and the difference was statistically significant (χ 2 = 11.44, P < 0.001). No Echinococcus shiquicus was detected. Conclusions:The positive rates of Echinococcus larvae antibodies in the population and canine Echinococcus antigen in Yushu City, Qinghai Province are both relatively high. There is a mixed epidemic of CE and AE, with Echinococcus multilocularis being the main species.

Objective:To analyze the risk factors of Mycoplasma pneumoniae（MP）lobar pneumonia with plastic bronchitis（PB）in pediatric patients，and to establish a risk nomogram prediction model.Methods:The medical informations were collected from pediatric patients diagnosed with MP lobar pneumonia who performed bronchoscopy during hospitalization in the Department of Pediatrics at the Second Affiliated Hospital of Air Force Military Medical University from April 2023 to December 2023.According to the bronchoscopic findings，the patients were divided into PB group and non-PB group.The clinical medical records and ancillary diagnostic findings were retrospectively analyzed.A multivariate Logistic regression model was used to analyze the independent risk factors for children with MP lobar pneumonia complicated with PB.A nomogram model was constructed to predict the risk of PB occurrence. Calibration curves and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the predictive value of the nomogram model for MP lobar pneumonia with PB. The receiver operating characteristic (ROC) curve was used to assess the diagnostic efficacy.Results:A total of 357 pediatric patients diagnosed with MP lobar pneumonia were included，with 92 cases in PB group and 265 cases in non-PB group. No statistically significant differences in gender and age were observed between the two groups（ P>0.05）.The duration of fever and the hospitalization time in PB group were longer than those in non-PB group. The incidences of pleural effusion，consolidation area of a single lung lobe ≥2/3 and atelectasis on chest CT were higher in PB group compared to non-PB group. Additionally,the levels of neutrophil/lymphocyte ratio，C-reactive protein，procalcitonin，D-dimer（D-D），alanine aminotransferase(ALT)，aspartate aminotransferase，lactate dehydrogenase，α-hydroxybutyrate dehydrogenase，interferon-γ（IFN-γ），interleukin（IL）-6，IL-10 and IFN-γ/IL-4 ratio in PB group were higher than those in non-PB group（all P<0.05）.Logistic regression analysis showed elevated D-D, ALT and IFN-γ, pleural effusion and consolidation area of a single lung lobe ≥2/3 were independent risk factors for PB.The nomogram prediction model constructed by the model demonstrated good goodness-of-fit (χ 2=11.316， P=0.184) and provided significant clinical net benefits within a risk threshold range of 0.09–0.65. The area under the ROC curve for combined prediction was 0.771（95% CI 0.716-0.826），with a sensitivity of 0.707 and specificity of 0.706. Conclusion:In children with MP lobar pneumonia, elevated laboratory markers (D-D, ALT, IFN-γ) and imaging features (pleural effusion, consolidation area of a single lung lobe ≥2/3) are critical predictors for early diagnosis of PB.The nomogram prediction model can be used to predict MP lobar pneumonia with PB in early stage.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Persistent atrial fibrillation and other factors can cause mitral and tricuspid annular dilation and leaflet regurgitation,leading to severe functional mitral and tricuspid regurgitation.Patients often experience significant heart failure symptoms and poor prognosis.For patients with severe mitral or tricuspid regurgitation who are at high risk or contraindicated for surgical procedures,transbronchial repair(TEER)is an important alternative therapy that can effectively reduce valve regurgitation and improve cardiac function;Although there is a lack of large-scale data on atrial functional reflux,existing experience still shows that TEER can significantly reduce reflux and improve patients'quality of life.However,double valve intervention therapy poses challenges,especially when combined with TEER repair,which is technically more complex,time-consuming,and carries higher risks.Foreign data shows that simultaneous or staged double valve intervention can safely improve cardiac function and increase survival rates,but the optimal intervention strategy still needs further research.Due to the fact that tricuspid TEER devices have not yet been launched in China,only staged treatment can be adopted at present.This case report shows a patient with severe atrial functional mitral and tricuspid regurgitation who underwent staged transcatheter edge to edge repair surgery successfully.During a 1-year follow-up,bilateral valve regurgitation continued to improve,indicating that staged repair of bilateral atrioventricular valve regurgitation through the catheter margin is a feasible and effective treatment option.

Metastasis is a pivotal and intricate process in the progression of malignant tumors,strongly correlating with poor prognosis.Approximately 90%of cancer-related mortality is attributed to metastasis,with the five-year survival rate for patients with metastatic solid tumors ranging from 5%to 30%.Consequently,a comprehensive understanding of the underlying biological mechanisms driving metastasis is essential for unraveling its core processes and developing novel therapeutic strategies.The metastatic cascade involves tumor cells navigating numerous biological barriers,including detachment from the primary tumor,invasion of blood vessels or lymphatics,survival in circulation,extravasation into distant organs and subsequent adaptation to the microenvironment.To surmount these challenges,tumor cells undergo phenotypic changes,genetic mutations and dysregulating signaling pathways.Additionally,microenvironmental factors(such as angiogenesis,matrix remodeling and immune evasion)play a critical role,orchestrating the initiation and growth of metastatic lesions in an interdependent manner.Organ-specific metastasis,a distinct subset of metastasis,involves dynamic bidirectional interactions between tumor cells and the microenvironment of target organs.These interactions determine the selectivity of metastatic spread and drive the adaptive evolution of both the tumor and the organ,which encompasses multiple layers of cellular interactions,including cell-cell and cell-matrix signaling.Tumor cell mutations,the release of specific signaling molecules,the capacity to withstand circulatory pressures,and signaling exchanges with target organs collectively govern the selective nature of organ-specific metastasis.Furthermore,factors intrinsic to the target organ-such as its regenerative potential,metabolic profile,immune surveillance mechanisms and matrix stiffness-further facilitate the adaptive remodeling of metastatic cells within these environments.Thus,the bidirectional selection and adaptation between tumor cells and target organs form a dynamic,complex system that reshapes our understanding of metastatic tumor development.While current research emphasizes shared biological features in metastasis,the successful formation of metastatic tumors depends not only on these common mechanisms but also on the unique characteristics governing organ-specific metastasis.The interplay between generalizable and organ-specific mechanisms profoundly influences the metastatic outcome.This review aimed to consolidate our current knowledge of these shared and distinct processes,analyze the evolving understanding of the bidirectional selection between tumor cells and target organs,and assess the current status of metastatic risk prediction models for patients without metastasis.Furthermore,the paper discussed the challenges and opportunities in managing advanced-stage metastatic tumors,offering new insights and potential clinical strategies to improve prognosis and treatment outcomes.