Definitive chemoradiotherapy (dCRT) has become a cornerstone in the treatment of locally advanced esophageal cancer; however, local control remains suboptimal, and persistent lesions or locoregional recurrences after treatment are not uncommon. For patients without distant metastases but with local failure, whether surgical intervention can still offer curative potential remains a major clinical dilemma. Salvage esophagectomy (SE) offers potential long-term survival for selected patients, but this procedure is performed in the context of severe fibrosis, impaired local blood supply, and obscured anatomical planes following chemoradiotherapy, resulting in significantly higher perioperative risk compared to primary esophagectomy. Consequently, controversies exist regarding patient selection, preoperative restaging, choice of surgical approach, extent of lymphadenectomy, gastrointestinal reconstruction, and perioperative management. In recent years, with the refinement of restaging modalities such as PET/CT, the accumulation of experience in high-volume centers, and emerging evidence from clinical studies, the clinical role of SE has gradually shifted from a "high-risk salvage measure" to a "selective curative strategy aimed at achieving long-term survival in carefully selected patients". Nevertheless, standardized guidelines for patient selection, technical approaches, and perioperative management are still lacking. Based on current evidence and clinical experience, experts organized by the Integrated Esophageal Cancer Committee of Chinese Anti-Cancer Association systematically reviewed key issues regarding SE, including its definition, indications, preoperative evaluation, choice of surgical approach, lymphadenectomy, gastrointestinal reconstruction, and perioperative management, and formulated a Chinese expert consensus. This consensus aims to provide guidance for standardized assessment, appropriate referral, individualized surgical decision-making, and optimized perioperative management of patients with locoregional failure after dCRT. Ultimately, this will increase the likelihood of R0 resection, reduce the risk of severe complications, and promote the safer, more judicious, and standardized implementation of SE in high-risk scenarios.

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.

Objective To explore the characteristics and temporal trends of renal cancer burden among Chinese adolescents and young adults(AYA), and predict its future epidemic situation, so as to provide evidence for formulating public health policies and reducing the disease burden.Methods Epidemiological data of renal cancer in Chinese AYA(15-39 years)from 1990 to 2021 were obtained from the Global Burden of Disease(GBD)2021 database. The current status and trends of renal cancer in this population were analyzed. The incidence and mortality trends of renal cancer in AYA over the next 15 years was predicted with ARIMA model. The impacts of smoking and body mass index(BMI)on the disease burden were evaluated. Results In 2021, the incidence(1.12/100000), prevalence(8.61/100000), mortality(0.19/100000), and disability-adjusted life years(DALYs)(11.37/100000 person-years)of renal cancer in Chinese AYA were significantly higher than those in 1990 and above the global average. All burden indicators and their increases were higher in males than in females, and the burden accelerated after 30 years of age. The incidence rose steadily from 2003 to 2021(APC=3.95), while the mortality increased significantly from 2013 to 2021(APC=2.40). The burden attributed to high BMI continued to rise, and the smoking-attributable fraction in males first increased then decreased. ARIMA predicted that the incidence of renal cancer in Chinese AYA males would accelerate, while the mortality in both genders would stabilize. Conclusion The burden of renal cancer in Chinese AYA shows a significant upward trend. Males and people over 30 years old are key targets for prevention and control. Strengthening the management of risk factors(smoking, high BMI)and promoting early diagnosis and treatment are essential to reduce the disease burden.

Objective To screen the aging-related genes associated with prognosis in clear cell renal cell carcinoma (ccRCC), construct a prognostic model to optimize risk stratification, explore the underlying mechanisms, and provide reference for individualized diagnosis and treatment of this disease.Methods RNA sequencing data, clinical data, and aging-related genes of ccRCC were obtained from databases.After differentially expressed genes were screened, a prognostic model was constructed using Cox regression.The performance of the model was evaluated with Kaplan-Meier curves and receiver operating characteristic (ROC) curves, and validated with clinical features.Key genes were verified with immunohistochemistry.Results A total of 70 differentially expressed genes were identified, including IFI16, PECAM1, and ABCB1 as key genes, and a model formula was constructed.Internal and external validations showed that the survival rate of the high-risk group was significantly lower, with the area under the ROC curve (AUC)＞0.6.The risk score was correlated with clinical features.IFI16 was highly expressed in cancer tissues and associated with poor prognosis.Conclusion The prognostic model constructed in this study has good performance.Integrating clinical features, it can improve the accuracy of prognosis assessment of ccRCC.

To investigate the malignant progression and molecular mechanism of KHSRP regulating esophageal squamous cell carcinoma(ESCC) through the JAK1/STAT3 signaling axis.

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.

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*

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.

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

OBJECTIVES: This study aimed to explore the active components, potential targets, and mechanism of Cnidii Fructus in the treatment of periodontitis with osteoprosis through network pharmacology, molecular docking, and molecular dynamics simulation technology. METHODS: The main chemical constituents and targets of Cnidii Fructus were screened using the TCMSP and SwissTargetPrediction databases, as well as literature reports. Targets of periodontitis and osteoporosis were predicted using different databases. The intersection targets of Cnidii Fructus, periodontitis, and osteoporosis were obtained using Venny 2.1. The protein-protein interaction network was formed on the STRING platform. Cytoscape 3.9.1 was used to construct the active component-intersection target interaction network, perform the topological analysis, and screen key targets and core active components. Furthermore, the Metascape database was used to perform gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis on the intersection targets. The top five key targets and core active components were selected as receptor proteins and ligand small molecules. Discovery Studio 2019 was used to dock ligands and receptors and visualize the docking results. Molecular dynamics simulation was conducted using Gromacs2022.3 to assess the stability of the interactions between the core active components and the main targets. RESULTS: A total of 20 potential active ingredients of Cnidii Fructus were screened, and 116 targets of Cnidii Fructus were obtained for treating periodontitis and osteoporosis. GO and KEGG analyses of the 116 targets showed that Cnidii Fructus may play a therapeutic role through the phosphoinositide 3-kinase-protein kinase B (PI3K-Akt) and advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE) signaling pathways. Molecular docking showed that the core constituents were well bound to the main targets. Molecular dynamics simulations confirmed the stability of the Diosmetin-AKT1 complex system. CONCLUSIONS The preliminary discovery of the potential molecular pharmacological mechanism of Cnidii Fructus extract in the targeted treatment of periodontitis with osteoporosis through a multi-component, multitarget, and multi-pathway approach can serve as a theoretical foundation for future drug-development research and clinical application.
Molecular Docking Simulation ; Molecular Dynamics Simulation ; Network Pharmacology ; Periodontitis/complications* ; Drugs, Chinese Herbal/chemistry* ; Osteoporosis/complications* ; Humans ; Protein Interaction Maps ; Cnidium/chemistry*