Objective To explore the clinical features, diagnosis, treatment and prognosis of testicular Ewing sarcoma(ES), so as to enhance the understanding and differential diagnosis of this disease. Methods A retrospective analysis was conducted on the clinical manifestations, auxiliary examinations and pathological findings of a case of left-sided ES treated at our hospital, supplemented by a review of relevant literature. Results A 24-year-old male patient presented with left testicular enlargement with pain. Magnetic resonance imaging suggested a neoplastic lesion, with a high likelihood of testicular germ cell tumor. A radical left orchiectomy was performed. Postoperative pathology revealed testicular ES. The patient subsequently completed 8 cycles of VDC/IE chemotherapy and showed no evidence of recurrence at 10 months of follow-up. Analysis of literature on 5 previously reported cases of testicular ES had testicular enlargement as the primary presentation. All patients underwent surgery and 4 received adjuvant therapy. Follow-up revealed 1 death at 9 months postoperatively, with no recurrence or progression seen in the remainder. Conclusion ES occurring in the testis is extremely rare.Testicular ES is a poorly differentiated small round-cell malignant tumor that primarily presents as testicular enlargement. Current treatment mainly surgical resection combined with adjuvant chemotherapy.

ObjectiveTo investigate the protective effect of Rehmanniae Radix iridoid glycosides （RIG） on the kidney tissue of streptozotocin （STZ）-induced diabetic mice and explore the underlying mechanism. MethodsTwelve of 72 male C57BL/6J mice were randomly selected as the normal group， and the remaining 60 mice were fed with a high-fat diet for six weeks combined with injection of 60 mg·kg^-1 STZ for 4 days to model type 2 diabetes mellitus. The successfully modeled mice were randomized into model， metformin （250 mg·kg^-1）， catalpol （100 mg·kg^-1）， low-dose RIG （RIG-L， 200 mg·kg^-1） and high-dose RIG （RIG-H， 400 mg·kg^-1） groups （n=11）. Mice in each group were administrated with corresponding drugs， while those in the normal group and model group were administrated with the same dose of distilled water by gavage once a day. After 8 weeks of intervention， an oral glucose tolerance test （OGTT） was performed， and the area under the curve （AUC） was calculated. After mice were sacrificed， both kidneys were collected. The body weight， kidney weight， and fasting blood glucose （FBG） were measured. Biochemical assays were performed to measure the serum levels of triglycerides （TG）， total cholesterol （TC）， serum creatinine （SCr）， and blood urea nitrogen （BUN）. Enzyme-linked immunosorbent assay （ELISA） was employed to determine the serum level of fasting insulin （FINS）， and the insulin sensitivity index （ISI） and homeostatic model assessment for insulin resistance （HOMA-IR） were calculated. The pathological changes in kidneys of mice were observed by hematoxylin-eosin staining and Masson staining. The immunohistochemical method （IHC） was employed to assess the expression of interleukin-1 （IL-1）， interleukin-6 （IL-6）， tumor necrosis factor-α（TNF-α）， transforming growth factor-β₁ （TGF-β₁）， and collagen-3 （ColⅢ） in the kidney tissue. The protein levels of TGF-β₁， cell signal transduction molecule 3 （Smad3）， matrix metalloproteinase-9 （MMP-9）， and ColⅢ in kidneys of mice were determined by Western blot. ResultsCompared with the normal group， the model group showcased decreased body weight and ISI （P<0.01）， increased kidney weight， FBG， AUC， FINS， HOMA-IR， TC， TG， SCr， and BUN （P<0.01）， glomerular hypertrophy， capsular space narrowing， and collagen deposition in the kidney， up-regulated protein levels of IL-1， IL-6， TNF-α， TGF-β₁， ColⅢ， and Smad3 （P<0.01）， and down-regulated protein level of MMP-9 （P<0.01） in the kidney tissue. Compared with the model group， the treatment groups had no significant difference in the body weight and decreased kidney weight （P<0.05， P<0.01）. The FBG level declined in the RIG-H group after treatment for 4-8 weeks and in the metformin， catalpol， and RIG-L groups after treatment for 6-8 weeks （P<0.01）. The AUC in the RIG-L， RIG-H， and metformin groups decreased （P<0.05， P<0.01）. The levels of TC， SCr， and BUN in the serum of mice in each treatment group became lowered （P<0.05， P<0.01）. The level of TG declined in the RIG-L， RIG-H， and metformin groups （P<0.05， P<0.01）. The serum level of FINS declined in the catalpol， RIG-L， and metformin groups （P<0.01）. Compared with the model group， the treatment groups showed decreased HOMA-IR （P<0.01）， increased ISI （P<0.01）， alleviated pathological changes in the kidney tissue， and down-regulated expression of IL-1 and TGF-β₁. In addition， the protein levels of IL-6， TNF-α， and ColⅢ in the RIG-H and metformin groups and IL-6 and TNF-α in the RIG-L group were down-regulated （P<0.05， P<0.01）， and the protein levels of IL-6， TNF-α， and ColⅢ in the catalpol group and ColⅢ in the RIG-L group showed a decreasing trend without statistical difference. The protein levels of TGF-β₁， Smad3， and ColⅢ in the RIG-H and metformin groups were down-regulated （P<0.01）. Compared with that in the model group， the protein level of MMP-9 was up-regulated in each treatment group （P<0.01）. ConclusionRIG can improve the renal structure and function of diabetic mice by regulating the TGF-β₁/Smads signaling pathway.

Objective: To assess the association between the metabolic score for insulin resistance index (METS-IR) and overactive bladder (OAB) in the US population，so as to explore the potential of METS-IR as a predictive tool for OAB risk and to provide insights for early screening and intervention strategies. Methods: Based on the data from the National Health and Nutrition Examination Survey (NHANES) 2005-2018，a cross-sectional design was employed，and multivariate logistic regression models were used to analyze the association between METS-IR and OAB. METS-IR was analyzed both as a continuous variable and categorized into quartiles. To further validate the association between METS-IR and OAB across diverse populations，subgroup analyses were conducted in participants stratified by clinical characteristics. Smooth curve fitting was employed to test the linearity of the METS-IR-OAB relationship. Results: Elevated METS-IR was associated with an increased risk of OAB (P<0.001)，and this positive correlation remained stable when METS-IR was categorized into quartiles (P<0.001). Subgroup analyses revealed that the association between METS-IR and OAB was more pronounced in females，participants younger than 55 years，and non-diabetic individuals (P<0.05). Furthermore，smooth curve fitting confirmed a linear positive correlation between METS-IR and OAB，with this linear relationship observed in both diabetic and non-diabetic groups. Conclusion: This study，based on the NHANES 2005-2018 database，found a linear positive correlation between METS-IR and OAB.

Parkinson's disease (PD), a chronic and common neurodegenerative disease, is characterized by the progressive loss of dopaminergic neurons in the dense part of the substantia nigra and abnormal aggregation of alpha-synuclein. Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by chronic insulin resistance and deficiency in insulin secretion. Extensive evidence has confirmed shared pathogenic mechanisms underlying PD and T2DM, such as oxidative stress caused by insulin resistance, mitochondrial dysfunction, inflammation, and disorders of energy metabolism. Conventional drugs for treating T2DM, such as metformin and glucagon-like peptide-1 receptor agonists, affect nerve repair. Even drugs for treating PD, such as levodopa, can affect insulin secretion. This review summarizes the relationship between PD and T2DM and related therapeutic drugs from the perspective of insulin signaling pathways in the brain.
Humans ; Parkinson Disease/drug therapy* ; Diabetes Mellitus, Type 2/pathology* ; Signal Transduction/physiology* ; Receptor, Insulin/metabolism* ; Animals ; Insulin Resistance/physiology* ; Insulin/metabolism*

Neurodegenerative diseases encompass a diverse array of disorders that have a profoundly detrimental impact on human health, characterized by their intricate and multifaceted pathogenesis. In the recent past, a growing body of scientific research has begun to shed light on the critical involvement of the neuro-immune axis in the onset and advancement of these debilitating conditions. This comprehensive review article delves into the intricate composition of the neuro-immune axis, elucidating the complex mechanisms through which it exerts its influence in the context of neurodegenerative diseases. Furthermore, it explores the potential therapeutic applications of targeting the neuro-immune axis for the management and treatment of these diseases. This extensive examination aims to offer new perspectives and innovative strategies that could pave the way for more effective treatments for neurodegenerative diseases, thereby providing hope for those afflicted by these challenging conditions.
Humans ; Neurodegenerative Diseases/metabolism* ; Animals ; Neuroimmunomodulation/physiology*

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 research of damp-heat syndrome in modern TCM mainly focuses on inflammatory response, water metabolism, lipid metabolism, hemorheology, intestinal flora and so on. Modern omics techniques such as metabolomics and genomics provide a new perspective for the exploration of the micro-mechanism of damp-heat syndrome. The study found that the abnormal expression of aquaporin is closely related to the formation of "dampness" in damp-heat syndrome, and the release of inflammatory factors reflects the pathological characteristics of "heat". Damp-heat syndrome is often accompanied by dyslipidemia, hemorheological changes and intestinal flora imbalance, showing characteristic changes in urine, blood and saliva metabolomics, and there are differences in gene expression between damp-heat constitution and gentleness constitution. In the future, the pertinence and systematicness of research should be strengthened, the relationship between indicators should be deeply explored, build a biomarker system should be built, the immune-metabolic regulation mechanism should be explored, the multi-target mechanism of heat-clearing and dampness-removing Chinese materia medica should be clarified to further improve the damp-heat syndrome system, and provide theoretical support for clinical treatment.

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.

Cancer incidence in China has been rising steadily,with a particularly heavy burden from several high-prevalence malignancies.Medical examination for cancer plays a critical role in the early detection of cancer,precancerous lesions,and precursor conditions,thereby facilitating timely diagnosis and intervention.Such examination also addresses the growing demand for person-alized cancer screening services among diverse population groups.The development of evidence-based,context-specific cancer screening guidelines is essential to enhance the standardization,quality,and equity of preventive screening practices across the country,ultimately improving out-comes in early cancer detection and treatment.Guided by the Department of Medical Emergency Response of the National Health Commission,the Guidelines for Medical Examination for Cancer in Health Examination Agency(2025 Edition)were developed under the leadership of the National Cancer Center.A multidisciplinary panel of experts formulated the guidelines in accordance with the principles and methodology of the World Health Organization Handbook for Guideline Deve-lopment.The guidelines provide evidence-based recommendations on key clinical domains:target cancers and populations,overall screening workflow,screening protocols,diagnostic technolo-gies,result interpretation,follow-up procedures,and quality control.The primary objective is to standardize cancer screening practices in health examination agency and strengthen China's ca-pacity for prevention and control of high-burden cancers.

Cancer stands as a significant global public health challenge,and cancer screening serves as a pivotal strategy for reducing its mortality.Presently,only a limited number of cancer types have appropriate screening methods available.Traditional single-cancer screen-ing approaches are fraught with limitations,including invasiveness,low accuracy,and poor patient compliance.Multi-cancer early detection(MCED)leveraging liquid biopsy technology enables non-invasive and efficient early detection of multiple cancers by analyzing biomarkers such as cell-free DNA,cell-free RNA,proteins,and metabolites in blood and other bodily fluids.This innovative approach substantially broadens the spectrum of detectable cancers and enhances population coverage,showcasing immense potential for improving existing can-cer screening strategies.This expert consensus comprehensively reviews the progress of liquid biopsy-based MCED,biomarker selection and detection technologies,the criteria for cancer type selection,research design and clinical utility evaluation,as well as implementation path-ways.The overarching goal of this consensus is to offer scientific guidance for further research and the widespread adoption of MCED,thereby facilitating the continuous optimization of cancer screening strategies.