Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

Diabetes, hyperlipidemia, and hyperuricemia have emerged as global chronic metabolic disorders posing significant public health challenges. Edible medicinal plants demonstrate multitarget regulatory effects and favorable safety profiles in the management of chronic metabolic diseases, owing to their bioactive compounds and nutritional synergy. Radix Astragali (RA), a traditional Chinese medicine with medicine–food homology, has a long history of application in China. Based on a review of relevant literature, this article discusses the historical background, modern research, therapeutic functions, and mechanisms of RA in the treatment of chronic metabolic diseases. Modern studies have shown that RA contains a variety of bioactive components and demonstrates efficacy in managing diabetes, hyperlipidemia, and hyperuricemia. Its mechanisms of action involve modulation of the insulin signaling pathway, regulation of lipid metabolism-related enzyme activity, promotion of uric acid excretion, and other processes. Notably, RA exhibits certain common therapeutic effects across hyperglycemia, hyperlipidemia, and hyperuricemia. Through modulation of the gut microbiota and inhibition of inflammatory pathways, RA exerts synergistic, multitarget effects that improve metabolic disorders associated with chronic metabolic diseases.

Objective To explore the relationship between PANoptosis and hepatic ischemia-reperfusion injury (HIRI), and to screen the key genes of PANoptosis in HIRI. Methods PANoptosis-related differentially expressed genes (PDG) were obtained through the Gene Expression Omnibus database and GeneCards database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the biological pathways related to PDG. A protein-protein interaction network was constructed. Key genes were selected, and their diagnostic value was assessed and validated in the HIRI mice. Immune cell infiltration analysis was performed based on the cell-type identification by estimating relative subsets of RNA transcripts. Results A total of 16 PDG were identified. GO analysis showed that PDG were closely related to cellular metabolism. KEGG analysis indicated that PDG were mainly enriched in cellular death pathways such as apoptosis and immune-related signaling pathways such as the tumor necrosis factor signaling pathway. GSEA results showed that key genes were mainly enriched in immune-related signaling pathways such as the mitogen-activated protein kinase (MAPK) signaling pathway. Two key genes, DFFB and TNFSF10, were identified with high accuracy in diagnosing HIRI, with areas under the curve of 0.964 and 1.000, respectively. Immune infiltration analysis showed that the control group had more infiltration of resting natural killer cells, M2 macrophages, etc., while the HIRI group had more infiltration of M0 macrophages, neutrophils, and naive B cells. Real-time quantitative polymerase chain reaction results showed that compared with the Sham group, the relative expression of DFFB messenger RNA in liver tissue of HIRI group mice increased, and the relative expression of TNFSF10 messenger RNA decreased. Cibersort analysis showed that the infiltration abundance of naive B cells was positively correlated with DFFB expression (r=0.70, P=0.035), and the infiltration abundance of M2 macrophages was positively correlated with TNFSF10 expression (r=0.68, P=0.045). Conclusions PANoptosis-related genes DFFB and TNFSF10 may be potential biomarkers and therapeutic targets for HIRI.

Objective To explore the safety of Yttrium-90 resin microsphere selective internal radiation therapy (⁹⁰Y-SIRT) on malignant liver tumors. Methods A retrospective analysis was conducted on 64 patients with malignant liver tumors who underwent ⁹⁰Y-SIRT from February 2023 to November 2024 at Weifang People’s Hospital. The clinical characteristics of the patients and the occurrence of adverse reactions after treatment were analyzed to assess the safety of ⁹⁰Y-SIRT. Results Among the 64 patients, there were 52 males (81.25%) and 12 females (18.75%); the average age was (56.29±11.08) years. Seven patients (10.94%) had tumors with maximum diameter of less than 5 cm, 38 patients (59.38%) had tumors with maximum diameter of 5-10 cm, and 19 patients (29.68%) had tumors with maximum diameter of greater than 10 cm. There were 47 cases (73.44%) of solitary lesions and 17 cases (26.56%) of multiple lesions; 53 cases (82.81%) were primary liver cancers and 11 cases (17.19%) were metastatic liver cancers. Of the 64 patients, 63 successfully completed the Technetium-99m macroaggregated albumin (^99mTc-MAA) perfusion test and received the ⁹⁰Y-SIRT; one patient received ⁹⁰Y-SIRT after the second ^99mTc-MAA perfusion test due to a work error. The most common adverse reactions included grade 1 alanine aminotransferase (ALT) elevation in 26 cases (40.62%) and grade 2 in 2 cases (9.37%), grade 1 aspartate aminotransferase (AST) elevation in 27 cases (42.18%) and grade 2 in 7 cases (10.93%); grade 1 nausea in 17 cases (26.56%) and grade 2 in 6 cases (9.37%); grade 1 abdominal pain in 12 cases (18.75%), grade 2 in 5 cases (7.81%), and grade 3 in 1 case (1.56%); grade 1 vomiting in 11 cases (17.18%), grade 2 in 5 cases (7.81%), and grade 3 in 1 case (1.56%). Conclusion The adverse reactions of ⁹⁰Y-SIRT for treating malignant liver tumors are mild, indicating good safety.

Objective To construct and validate the efficacy of a risk prediction model for pneumothorax after CT-guided percutaneous pulmonary puncture biopsy(CT-PCNB)based on nomograms.Methods A total of 246 patients who underwent CT-PCNB examination in the hospital from October 2020 to October 2023 were selected and divided into training set(n=144)and validation set(n=102)using a random sampling method.In the training set,univariate and multivariate logistic regression analyses were performed to identify risk factors for pneumothorax after CT-PCNB.A nomogram model was constructed based on the identified risk factors,and its accuracy was validated using the validation set.Results Multifactorial logistic regression analysis showed that age≥60 years,concomitant underlying lung disease,lesion diameter<2 cm,distance from lesion to pleura≥10 mm,puncture through interlobular pleura,and≥2 pleural punctures were the risk factors for pneumothorax after CT-PCNB in the training set(P<0.05).A nomogram model was constructed based on these six factors.The ROC curve results for the training set showed an AUC of 0.852,sensitivity of 84.50%,and specificity of 67.50%.The nomogram model was validated using the validation set,with ROC curve results showing an AUC of 0.845,sensitivity of 83.00%,and specificity of 69.50%.There was no statistically significant difference between the predicted and actual values in both the training and validation sets(χ2=1.803,1.225;P>0.05),indicating clinical validity.Conclusion The nomogram model constructed based on the risk factors for pneumothorax after CT-PCNB has high predictive efficacy and is clinically meaningful.

Objective To evaluate the association between self-reported neighborhood walkability environments and mortality in China.Methods The Prospective Urban Rural Epidemiology study in China(PURE-China)recruited 47 931 participants aged 35-70 from 12 provinces in China between 2005 and 2009.Neighborhood environmental indicators were collected using the Neighborhood Environment Walkability Scale(NEWS)questionnaire,with higher scores indicating better walkable environments.The primary outcomes were all-cause mortality and cardiovascular mortality,using Cox fragile model to evaluate the association between community walkability and outcomes,as well as exploring mediating pathways.Results Of 35 490 participants included in this study,60%were female,with a mean(SD)age of 51.5(9.6)years.The median follow-up was 11.7 years.This study found an association between higher community walkability score and reduced risk of all-cause mortality,with the total score(HR=0.85;95%CI,0.80-0.89),land-use mix(HR=0.84;95%CI,0.79-0.88),and crime safety(HR=0.84;95%CI,0.80-0.89)showing the most significant associations.NEWS can affect long-term adverse outcomes through lifestyle.Conclusions In the Chinese population,favorable community walkability is associated with lower all-cause mortality risk,which may support policymakers to take actions to mitigate the adverse effects of poor community en-vironments on health.

Objective To explore the mediating effect of sleep quality between somatic symptoms and severity of depression in patients with depression. Methods A total of 384 drug-naive patients diagnosed with depression were recruited from the Department of Psychological Medicine of Zhongshan Hospital, Fudan University, during the period from February to August 2024. The severity of depression, somatic symptoms, and sleep quality were assessed using Patient Health Qusetionaire (PHQ)-9, PHQ-15, and Pittsburgh sleep quality index (PSQI), respectively. Based on the PHQ-15 scores, all participants were stratified into two groups: a mild somatic symptoms group（＜10 points, n＝136）and a moderate-to-severe somatic symptoms group（≥10 points, n＝248）. Comparisons of sleep quality between the two groups were conducted, and partial correlation analysis was performed to examine the correlation between sleep quality and somatic symptoms. Additionally, linear regression and mediation analyses were conducted to investigate the mediating effect of sleep quality between somatic symptoms and severity of depression. Results The PSQI scores in moderate-to-severe somatic symptoms group were significantly higher than those in mild somatic symptoms group (P＜0.001). Partial correlation analysis indicated that, after controlling for depression severity, the positive correlation between PSQI and PHQ-15 scores remained significant in both groups (P＜0.01). Regression analysis identified both sleep quality and somatic symptoms as predictors of severity of depression (P＜0.001). Additionally, mediation analysis demonstrated that sleep quality partially mediated the relationship between somatic symptoms and severity of depression, accounting for 26.63% (0.090/0.338) of the total effect. Conclusions In patients with depression, sleep quality is associated with somatic symptoms, and both contribute to an increased risk of the severity of depression. Moreover, sleep quality plays a partial mediating effect between somatic symptoms and severity of depression, highlighting the importance of addressing sleep-related issues in the management of depression.

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

OBJECTIVES: To explore the correlation of ELFN1 expression level with prognosis of colorectal cancer and its regulatory role in colorectal cancer cell proliferation and metastasis. METHODS: We analyzed the expression levels of ELFN1 across 33 cancer types using publicly available databases and identified differential genes related to ELFN1 in colorectal cancer. Gene function annotation and enrichment analysis were used to identify the involved signaling pathways. Logistic analysis, Kaplan-Meier analysis and Cox regression analysis were performed to evaluate the correlation between ELFN1 expression and clinicopathological parameters and survival of colorectal cancer patients. qPCR and Western blotting were used to validate the expression levels of ELFN1 in different colorectal cancer cell lines and tissues, and Transwell and EDU experiments were carried out to assess the effect of ELFN1 knockdown on biological behaviors of SW480 cells. RESULTS: ELFN1 was highly expressed in 14 cancers, and its expression was significantly higher in colon cancer tissues than in adjacent tissues. A high expression of ELFN1 mRNA was associated with a poorer overall survival of colorectal cancer patients. Cox regression analysis indicated that ELFN1 expression was an independent prognostic factor for overall survival of the patients. ELFN1 was significantly enriched in tumor metastasis and proliferation and participated in several tumor signaling pathways. The colon cancer cell lines showed significantly higher expression levels of ELFN1 than normal cells, ELFN1 knockdown obviously inhibited proliferation and migration of SW480 cells in vitro. CONCLUSIONS ELFN1 is overexpressed in colorectal cancer and is associated with poor clinical prognosis of the patients. A high ELFN1 expression is associated with malignant phenotypes of colorectal cancer and promotes cancer cell proliferation and metastasis, suggesting its potential as a prognostic biomarker for colorectal cancer.
Humans ; Colorectal Neoplasms/diagnosis* ; Cell Proliferation ; Prognosis ; Cell Line, Tumor ; Biomarkers, Tumor/metabolism* ; Neoplasm Metastasis ; Gene Expression Regulation, Neoplastic ; Nerve Tissue Proteins/metabolism* ; Female ; Male

This review summarizes recent advances in neoadjuvant immunotherapy for advanced gastric cancer.Through literature search in PubMed,Web of Science,and CNKI databases from 2020 to 2023,we systematically analyzed the mechanisms,clinical applications,and bio-marker research.Programmed death-1(PD-1)inhibitors combined with chemotherapy significantly improve patient outcomes,while mi-crosatellite instability(MSI),programmed death-ligand 1(PD-L1)expression,and tumor mutational burden(TMB)have been identified as important predictive biomarkers.Multi-omics analysis shows great potential in identifying optimal responders,with pyroptosis-related gene scoring system(PRS)positively correlating with anti-tumor immune infiltration.Metabolic reprogramming and epigenetic regulation in the tumor microenvironment play key roles in immune evasion,while emerging targets such as Claudin 18.2 and combination targeting strategies further enhance therapeutic efficacy.Despite significant progress,precise patient selection and overcoming resistance mechan-isms remain major challenges.Future research should focus on biomarker validation,personalized treatment strategy development,tumor microenvironment dynamic analysis,and novel combination therapy exploration to improve clinical outcomes.