ObjectiveTo investigate the effect of Zishen Huoxue Formula （ZSXHF） on molecular-targeted therapy-associated proteinuria in patients with primary liver cancer （PLC）， to assess the efficacy of ZSXHF in the treatment of molecular-targeted therapy-associated proteinuria， and to provide a basis for clinical medication. MethodsA retrospective cohort study was conducted among the PLC patients with molecular-targeted therapy-associated proteinuria who were diagnosed and treated in The Department of Hepatology of Chinese PLA General Hospital， from January 1， 2022 to July 1， 2025. With ZSXHF treatment as the exposure factor， the patients with a cumulative treatment duration of ≥9 weeks were enrolled as traditional Chinese medicine （TCM） group， while those without TCM treatment were enrolled as control group. Propensity score matching was performed for the two groups at a ratio of 1∶1 based on sex， age， 24-hour urinary protein， blood urea nitrogen， and serum creatinine. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between groups. Univariate and multivariate Logistic regression analyses were used to investigate the influencing factors for promoting the improvement of targeted-therapy-associated proteinuria. ResultsA total of 137 PLC patients with targeted-therapy-associated proteinuria were enrolled， with 34 patients in the TCM group and 103 in the control group. After follow-up for 6 months， the TCM group had a significant improvement in urinary protein grade compared with the control group （χ²=9.261， P=0.016）. There were 25 patients in each group after propensity score matching， and after follow-up for 6 months， there were significant differences between the two groups in urinary protein grade （χ²=15.689， P<0.001） and 24-hour urinary protein （Z=-3.075， P=0.002）. After cumulative treatment with ZSXHF for ≥9 weeks， the TCM group had a significantly greater change in 24-hour urinary protein from baseline compared with the control group （t=-2.514， P=0.016）， while there were no significant differences in the changes in liver and renal function after ZSXHF intervention between the two groups （all P>0.05）. The multivariate Logistic regression analysis showed that ZSXHF treatment （odds ratio=2.901， 95% confidence interval： 1.135 — 7.417， P=0.026） was an independent influencing factor for improvement in molecular-targeted therapy-associated proteinuria. ConclusionZSHXF can effectively alleviate molecular-targeted therapy-associated proteinuria in PLC patients with a favorable safety profile， which provides a new reference for TCM prevention and treatment of molecular-targeted therapy-associated adverse reactions in PLC patients.

Objective:Ménière's disease（MD） is a common disorder of the inner ear. The fluctuating clinical symptoms and the absence of gold standards for diagnosis have posed serious problems for clinical diagnosis and treatment over the years. With the development of science and technology, artificial intelligence （AI） has been widely used in the field of medicine, and the potential of AI application to MD is demonstrated. The purpose of this review is to outline the use of AI in MD. Initially, specific instances where AI aids in differentiating MD from other causes of vertigo are presented. Furthermore, the role of AI in the evaluation of Endolymphatic Hydrops （EH）, particularly through imaging and biochemical assays, is highlighted due to its correlation with MD. Additionally, the effectiveness of AI in managing MD patients and forecasting disease progression is examined. In conclusion, the prevalent challenges hindering the clinical integration of AI in MD treatment are discussed, alongside potential strategies to surmount these barriers.
Humans ; Meniere Disease/diagnosis* ; Artificial Intelligence ; Endolymphatic Hydrops/diagnosis*

OBJECTIVES: To investigate the association of NUF2 expression with tumor prognosis and its regulatory role in tumor microenvironment. METHODS: We analyzed NUF2 expression, its prognostic value, and is immune-related functions across different cancer types using datasets from the Human Protein Atlas (HPA), TCGA, GTEx, CCLE, and TIMER. RT-qPCR, Western blotting, and immunohistochemistry were used to detect NUF2 expression in liver cancer cell lines and tissue and blood samples from patients with liver cancer. GO, KEGG, and GSEA analyses were conducted to explore the molecular mechanisms of NUF2 and its related genes, and a competitive endogenous RNA (ceRNA) network for NUF2 in liver cancer was constructed. RESULTS: NUF2 expression was upregulated in the tumor tissues of 27 cancers and was associated with clinical stages in several cancers. High NUF2 expressions were correlated with poor overall survival, disease-specific survival, progression-free survival, and disease-free survival of cancer patients. NUF2 expression levels were positively correlated with tumor mutational burden, microsatellite instability, infiltrating immune cells, immune cell marker genes and immune checkpoint genes in different cancers. RT-qPCR, Western blotting, and immunohistochemistry confirmed that NUF2 expression was upregulated in liver cancer cell lines and tumor tissues and blood samples of liver cancer patients, and was decreased significantly after operation. GO, KEGG and GSEA analyses indicated that NUF2 was involved in chromosome segregation and cell cycle and was associated with glycine, serine and threonine metabolism. CONCLUSIONS NUF2 expression is upregulated in 27 cancers and is associated with clinical stage and poor prognosis in some malignancies. NUF2 expression is closely correlated with immune cell infiltration in different cancers, suggesting its potential value for predicting immunotherapy response in these cancers.
Humans ; Prognosis ; Immunotherapy ; Tumor Microenvironment ; Liver Neoplasms/metabolism* ; Cell Line, Tumor ; Neoplasms/genetics* ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics*

Objective:To explore the feasibility of automatic target volume tracing in the Elekta Unity magnetic resonance imaging (MRI)-guided radiotherapy system and to further enhance the real-time target volume tracing performance of MRI-guided radiotherapy by introducing the deep learning technology based on a large Transformer model.Methods:A total of 4 661 frames of cine MRI binary images from 75 patients with malignant tumors in the chest/abdomen who were treated with MRI-guided radiotherapy were retrospectively collected as a training set. Another 500 frames of cine MRI binary images from 10 patients were collected as an independent test set. A module for medical image format conversion was developed to convert binary images into medical meta-images. The outer contours of tumor target volumes in the cine MRI images of the test set were manually delineated as actual control labels. With the first frame of the cine MRI images of each patient as the reference image and the other frames as motion images, a Transformer-based deep learning model was constructed to describe the deformable vector field (DVF) of motion images relative to the reference image. The Dice similarity coefficient (DSC), the 95% Hausdorff distance (HD 95), the negative Jacobian determinant (NegJ), and the average processing time per frame of cine MRI images were calculated. These values were compared to those of the conventional B-Spline scheme to quantitatively assess the target volume tracing accuracy, DVF physical plausibility, and execution efficiency of the Transformer-based deep learning model. Results:The Transformer-based deep learning model constructed in this study delivered superior target volume tracing performance, with improved DSC [(0.84 ± 0.05) vs. (0.74 ± 0.16), t = 11.44, P < 0.05] and HD 95 [(9.25 ± 2.98) vs. (14.70 ± 8.55) mm, t = -11.83, P < 0.05]. Furthermore, this model reduced the average image processing time from 1.95 s to 30.99 ms, enhancing the efficiency by two orders of magnitude. Besides, this model yielded NegJ similar to that of the B-Spline scheme. This suggests that the DVF extracted using this model had comparable physical plausibility with that obtained using the B-Spline scheme. Conclusions:The Transformer-based deep learning model for automatic target volume tracing fills the functional gap of the Elekta Unity MRI-guided radiotherapy system, facilitating relatively accurate, efficient automatic tracing of moving tumor targets in the chest and abdomen.

Objective:To systematically analyze the revisions content and technological development trends of microbiological standards in the Chinese Pharmacopoeia(ChP)2025 Edition,and explore its novel requirements in risk-based pharmaceutical product lifecycle management.Methods:A comprehensive review was conducted on 26 microbiological-related standards to summarize the revision directions and scientific implications from perspectives including the revision overview,international harmonization of microbiological standards,risk-based quality man-agement system,and novel tools and methods with Chinese characteristics.Results:The ChP 2025 edition demon-strates three prominent features in microbiological-related standards:enhanced international harmonization,intro-duced emerging molecular biological technologies,and established a risk-based microbiological quality control sys-tem.Conclusion:The new edition of the Pharmacopoeia has systematically constructed a microbiological standard system,which significantly improves the scientificity,standardization and applicability of the standards,providing a crucial support for advancing the microbiological quality control in pharmaceutical industries of China.

Objective: To systematically analyze the revisions content and technological development trends of microbiological standards in the Chinese Pharmacopoeia (ChP) 2025 Edition, and explore its novel requirements in risk-based pharmaceutical product lifecycle management.
Methods: A comprehensive review was conducted on 26 microbiological-related standards to summarize the revision directions and scientific implications from perspectives including the revision overview, international harmonization of microbiological standards, risk-based quality management system, and novel tools and methods with Chinese characteristics.
Results: The ChP 2025 edition demonstrates three prominent features in microbiological-related standards: enhanced international harmonization, introduced emerging molecular biological technologies, and established a risk-based microbiological quality control system.
Conclusion: The new edition of the Pharmacopoeia has systematically constructed a microbiological standard system, which significantly improves the scientificity, standardization and applicability of the standards, providing a crucial support for advancing the microbiological quality control in pharmaceutical industries of China.

ObjectiveTo investigate the recurrence of ischemic stroke (IS) and to analyze the association between four indices of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) and the risk of IS recurrence by analyzing the follow-up data related to IS in the community-based natural population of Songjiang District, Shanghai, so as to provide a scientific basis for improving the prognosis of stroke patients in the community and controlling IS recurrence. MethodsA prospective follow-up study was conducted among the IS patients in the community-based cohort population, collecting data about patient’s age, gender, disease history, biochemical indicators, and etc. Cox regression model and restricted cubic spline model were used to analyze the relationship between different levels of plasma lipids and the recurrence of IS in these patients. ResultsA total of 1 368 patients with IS were included. The total follow-up duration was 7 171.46 person-years, with a median follow-up time of 6.24 years. There were 420 cases of IS recurrence, resulting in a cumulative recurrence rate of 30.70%. The results of multivariate Cox regression analysis showed that the recurrence risk of IS was reduced when the baseline TC and LDL-C levels of IS patients were in the ranges of 4.65‒5.67 mmol·L^-1 and 2.52‒3.46 mmol·L^-1, respectively. The results of restricted cubic spline analysis showed a U-shaped relationship between baseline TC and LDL-C levels and the recurrence risk in IS patients. ConclusionThe cumulative recurrence rate of patients with IS in the community of Songjiang District in Shanghai is high, and the levels of TC and LDL-C at baseline survey are correlated with the recurrence of IS in these patients. It is suggested to pay more attention to the levels of LDL-C and TC in patients with IS, so as to improve the prognosis.

ObjectiveTo develop a nomogram-based risk prediction model for chronic obstructive pulmonary disease (COPD) incidence among the community-dwelling population aged 40 years old and above, so as to provide targeted references for the screening and prevention of COPD. MethodsBased on a natural population cohort in suburban Shanghai, a total of 3 381 randomly selected participants aged ≥40 years underwent pulmonary function tests between July and October 2021. Cox stepwise regression analysis was used to develop overall and gender-specific risk prediction models, along with the construction of corresponding risk nomograms. Model predictive performance was evaluated using the C-indice, area under the curve (AUC) values, and Brier score. Stability was assessed through 10-fold cross-validation and sensitivity analysis. ResultsA total of 3 019 participants were included, with a median follow-up duration of 4.6 years. The COPD incidence density was 17.22 per 1 000 person-years, significantly higher in males (32.04/1 000 person-years) than that in females (7.38/1 000 person-years) (P<0.001). The overall risk prediction model included the variables such as gender, age, education level, BMI, smoking, passive smoking, and respiratory comorbidities. The male-specific model incorporated the variables such as age, BMI, respiratory comorbidities, and smoking, while the female-specific model included age, marital status, respiratory comorbidities, and pulmonary tuberculosis history. The C-indices for the overall, male-specific, and female-specific models were 0.829, 0.749, and 0.807, respectively. The 5-year AUC values were 0.785, 0.658, and 0.811, with Brier scores of 0.103, 0.176, and 0.059, respectively. Both 10-fold cross-validated C-indices and sensitivity analysis (excluding participants with a follow-up duration of <6 months) yielded C-indices were above 0.740. ConclusionThis study developed concise and practical overall and gender-specific COPD risk prediction models and corresponding nomograms. The models demonstrated robust performance in predicting COPD incidence, providing a valuable reference for identifying high-risk populations and formulating targeted screening and personalized management strategies.

Age-related macular degeneration(ARMD)is a common multifactorial disease among the elderly, which may lead to irreversible vision loss; however, the pathogenesis of ARMD is still unclear. Metabolomics is a relatively new “omics” technique that can provide qualitative and quantitative information about low molecular weight metabolites that make up biological systems, thereby revealing the physiological or pathological state of cell or tissue samples at specific time points. In recent years, increasing evidence suggests that metabolic dysfunction plays an important role in the development and progression of ARMD. Metabolic pathway dysregulation involves lipid metabolism, nucleotide metabolism, amino acid metabolism, and energy metabolism, which may play important roles in the occurrence and development of ARMD. The retina is one of the most metabolically active tissues in the human body, so using metabolomics techniques to measure molecular changes in ARMD will further enhance our understanding of the pathogenesis. This will provide important insights for the prevention and treatment of ARMD, This article reviews the application of metabolomics in ARMD.

Ferroptosis, a programmed cell death modality discovered and defined in the last decade, is primarily induced by iron-dependent lipid peroxidation. At present, it has been found that ferroptosis is involved in various physiological functions such as immune regulation, growth and development, aging, and tumor suppression. Especially its role in tumor biology has attracted extensive attention and research. Breast cancer is one of the most common female tumors, characterized by high heterogeneity and complex genetic background. Triple negative breast cancer (TNBC) is a special type of breast cancer, which lacks conventional breast cancer treatment targets and is prone to drug resistance to existing chemotherapy drugs and has a low cure rate after progression and metastasis. There is an urgent need to find new targets or develop new drugs. With the increase of studies on promoting ferroptosis in breast cancer, it has gradually attracted attention as a treatment strategy for breast cancer. Some studies have found that certain compounds and natural products can act on TNBC, promote their ferroptosis, inhibit cancer cells proliferation, enhance sensitivity to radiotherapy, and improve resistance to chemotherapy drugs. To promote the study of ferroptosis in TNBC, this article summarized and reviewed the compounds and natural products that induce ferroptosis in TNBC and their mechanisms of action. We started with the exploration of the pathways of ferroptosis, with particular attention to the System X_c^--cystine-GPX4 pathway and iron metabolism. Then, a series of compounds, including sulfasalazine (SAS), metformin, and statins, were described in terms of how they interact with cells to deplete glutathione (GSH), thereby inhibiting the activity of glutathione peroxidase 4 (GPX4) and preventing the production of lipid peroxidases. The disruption of the cellular defense against oxidative stress ultimately results in the death of TNBC cells. We have also our focus to the realm of natural products, exploring the therapeutic potential of traditional Chinese medicine extracts for TNBC. These herbal extracts exhibit multi-target effects and good safety, and have shown promising capabilities in inducing ferroptosis in TNBC cells. We believe that further exploration and characterization of these natural compounds could lead to the development of a new generation of cancer therapeutics. In addition to traditional chemotherapy, we discussed the role of drug delivery systems in enhancing the efficacy and reducing the toxicity of ferroptosis inducers. Nanoparticles such as exosomes and metal-organic frameworks (MOFs) can improve the solubility and bioavailability of these compounds, thereby expanding their therapeutic potential while minimizing systemic side effects. Although preclinical data on ferroptosis inducers are relatively robust, their translation into clinical practice remains in its early stages. We also emphasize the urgent need for more in-depth and comprehensive research to understand the complex mechanisms of ferroptosis in TNBC. This is crucial for the rational design and development of clinical trials, as well as for leveraging ferroptosis to improve patient outcomes. Hoping the above summarize and review could provide references for the research and development of lead compounds for the treatment for TNBC.