OBJECTIVE To investigate the mechanism of Xihuang pill （XHP） against diffuse large B-cell lymphoma （DLBCL）. METHODS The active ingredients of XHP and potential therapeutic targets for DLBCL were identified using TCMSP， GeneCards and DisGeNET databases. Protein-protein interaction networks were constructed using the String database and Cytoscape software to screen core components and core targets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were then performed. The clinical relevance of core targets was analyzed using the GEPIA and PanCanSurvPlot databases. Molecular docking and molecular dynamics （MD） simulation were conducted to verify the interactions between core components and core targets， and the binding free energy was calculated using the molecular mechanics Poisson-Boltzmann surface area （MM-PBSA） method. The effects of XHP on DLBCL and the related molecular mechanisms were validated using CCK-8 assay， flow cytometry and Western blot. RESULTS Network pharmacology analysis identified 108 active ingredients of XHP and 410 potential therapeutic targets for DLBCL. Six core components （e.g.， 17 beta-estradiol， quercetin） and ten core targets [e.g.， tumor protein 53 （TP53）， proto-oncogene tyrosine-protein kinase Src （SRC）] were obtained. Enrichment analysis indicated that the anti-DLBCL effects of XHP were primarily associated with the apoptotic signaling pathway， the phosphoinositide 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway and so on. Clinical correlation analysis revealed that TP53 and SRC expression were significantly up-regulated in DLBCL tissues and associated with poor patient prognosis （P＜0.05）. Molecular docking， MD simulations and MM-PBSA calculations confirmed that the SRC-quercetin complex had a mail：stronger and more stable binding affinity. In vitro experiments demonstrated that XHP concentration-dependently inhibited the proliferation of DLBCL cells； compared with control group， XHP medium- and high-dose groups could significantly induce the apoptosis of SU-DHL2 and SU-DHL4 cells， and significantly down- regulated the expressions of SRC protein， phosphorylated （p）-PI3K/PI3K and p-Akt/Akt in SU-DHL4 cells （P＜0.05）. CONCLUSIONS XHP may inhibit the proliferation and induce the apoptosis of DLBCL cells by regulating the SRC/PI3K/Akt signaling pathway.

OBJECTIVE:Patients with osteoporotic vertebral compression fractures still have residual back pain after vertebral augmentation.The current research is characterized by limited sample size,complex confounding factors,and inconsistent research results.To gain a deeper understanding of this phenomenon,the aim of this study was to identify and evaluate the risk factors for residual back pain after surgery through a systematic review and meta-analysis.METHODS:A comprehensive search was conducted in CNKI,VIP,WanFang,CBMdisc,PubMed,The Cochrane Library,Embase,and Web of Science for case-control studies on residual back pain after vertebral body augmentation for osteoporotic vertebral compression fractures from database inception to July 2024.The search terms were a combination of subject terms and free terms.The basic information,patient characteristics,surgical-related indicators,and risk factors for surgical back pain of the included studies were extracted.After evaluating the bias risk of all included studies,a meta-analysis was conducted using Stata 14.0 software on the relevant indicators.RESULTS:(1)21 case-control studies with a total of 8 043 patients were included.Among them,965 patients developed back pain.The quality score of all 21 studies was ≥7.(2)The meta-analysis results showed that age(WMD=0.98,95％CI:0.40-1.56,P=0.010),bone mineral density(WMD=-0.28,95％CI:-0.34 to-0.21,P=0.000),the number of vertebral fractures(OR=3.50,95％CI:2.65-4.62,P=0.000),thoracolumbar fracture index(OR=3.65,95％CI:2.61-5.11,P=0.000),cement volume(OR=6.89,95％CI:2.62-18.17,P=0.000),and cement distribution(OR=2.38,95％CI:1.93-2.93,P=0.000)were risk factors for the development of back pain after vertebral body augmentation in patients with osteoporotic vertebral compression fractures.CONCLUSION:Current evidence indicates that age,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,bone cement injection volume,and the distribution of bone cement are risk factors for low back pain.Specifically,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,and non-uniform distribution of bone cement are identified as independent risk factors for low back pain.Patients exhibiting these high-risk factors require vigilant monitoring and prompt intervention to mitigate the occurrence of clinical low back pain,thereby enhancing patient outcomes and quality of life.

OBJECTIVE:Patients with osteoporotic vertebral compression fractures still have residual back pain after vertebral augmentation.The current research is characterized by limited sample size,complex confounding factors,and inconsistent research results.To gain a deeper understanding of this phenomenon,the aim of this study was to identify and evaluate the risk factors for residual back pain after surgery through a systematic review and meta-analysis.METHODS:A comprehensive search was conducted in CNKI,VIP,WanFang,CBMdisc,PubMed,The Cochrane Library,Embase,and Web of Science for case-control studies on residual back pain after vertebral body augmentation for osteoporotic vertebral compression fractures from database inception to July 2024.The search terms were a combination of subject terms and free terms.The basic information,patient characteristics,surgical-related indicators,and risk factors for surgical back pain of the included studies were extracted.After evaluating the bias risk of all included studies,a meta-analysis was conducted using Stata 14.0 software on the relevant indicators.RESULTS:(1)21 case-control studies with a total of 8 043 patients were included.Among them,965 patients developed back pain.The quality score of all 21 studies was ≥7.(2)The meta-analysis results showed that age(WMD=0.98,95％CI:0.40-1.56,P=0.010),bone mineral density(WMD=-0.28,95％CI:-0.34 to-0.21,P=0.000),the number of vertebral fractures(OR=3.50,95％CI:2.65-4.62,P=0.000),thoracolumbar fracture index(OR=3.65,95％CI:2.61-5.11,P=0.000),cement volume(OR=6.89,95％CI:2.62-18.17,P=0.000),and cement distribution(OR=2.38,95％CI:1.93-2.93,P=0.000)were risk factors for the development of back pain after vertebral body augmentation in patients with osteoporotic vertebral compression fractures.CONCLUSION:Current evidence indicates that age,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,bone cement injection volume,and the distribution of bone cement are risk factors for low back pain.Specifically,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,and non-uniform distribution of bone cement are identified as independent risk factors for low back pain.Patients exhibiting these high-risk factors require vigilant monitoring and prompt intervention to mitigate the occurrence of clinical low back pain,thereby enhancing patient outcomes and quality of life.

Primary biliary cholangitis （PBC） is a cholestatic autoimmune liver disease characterized by the injury of small intrahepatic bile ducts， and at present， the pathogenesis of PBC remains unclear. Recent studies have shown that bile acid metabolism disorder and gut microbiota imbalance play a key role in the development and progression of PBC， and they form a complex and dynamic interaction network via the “gut-liver axis” and regulate core physiopathological processes such as immune response， metabolic homeostasis， and inflammatory response in a synergistic manner. This article systematically elaborates on the abnormal features of bile acid metabolism and gut microbiota in PBC， discusses their synergistic mechanisms in PBC， and then proposes a combined strategy of targeting bile acid receptors and modulating gut microbiota， in order to overcome the limitations of current treatment modalities and provide new insights and directions for the clinical management of PBC.

Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

ObjectiveTo investigate the distribution characteristics and correlation of paired oral and esophageal microbiota in patients with esophageal squamous cell carcinoma （ESCC）. MethodsA total of 46 pathologically confirmed ESCC patients were enrolled， and oral swabs and cancerous tissue samples were collected. The microbial composition was analyzed using 16S rRNA gene sequencing， and bioinformatics methods were employed to compare the microbiota diversity， compositional differences， and correlations between the two sites. ResultsThe Alpha diversity of the oral microbiota in ESCC patients was significantly higher than that of their esophageal microbiota， though the overall community structure was similar between the two sites. LEfSe analysis revealed that Spirochaetes， Prevotella， Catonella， Enterococcus， Fusobacterium periodonticum， Veillonella parvula， Capnocytophaga sputigena， and Neisseria subflava were significantly enriched in ESCC tumor tissues. In contrast， Actinobacteria， Proteobacteria， Streptococcus， Neisseria， Haemophilus， Streptococcus mitis， Haemophilus parainfluenzae， Porphyromonas gingivalis， Haemophilus haemolyticus， and Prevotella tannerae were significantly enriched in the oral cavity. Correlation analysis demonstrated significant associations between the two sites at taxonomic levels such as Spirochaetes， Treponema， Granulicatella elegans， Neisseria macacae， Treponema amylovorum， Fusobacterium periodonticum， and Neisseria meningitidis （P<0.05）. ConclusionThe oral and esophageal microbiota in ESCC patients exhibit both distinct differences and similarities. Oral microbiota may influence the esophageal microenvironment through migration or local immune modulation.

ObjectiveTo construct and validate a predictive model for hepatic encephalopathy （HE） in patients with end-stage liver disease （ESLD） by combining key indicators such as cytokines and Model for End-Stage Liver Disease （MELD） score， and to provide evidence-based support for the early identification of high-risk populations and the optimization of intervention strategies in clinical practice. MethodsA retrospective analysis was performed for 2 167 patients with ESLD who were admitted to The Third People’s Hospital of Kunming from January 2022 to December 2024， the patients were divided into a training set with 1 517 patients and a validation set with 650 patients at a ratio of 7∶3 using the stratified random sampling method. A univariate analysis and a least absolute shrinkage and selection operator （LASSO） regression analysis were performed for the training set to identify potential influencing variables， and then a binary Logistic regression model was constructed to investigate the independent influencing factors for HE in ESLD patients. A nomogram prediction model was established based on this regression model， and the Hosmer-Lemeshow goodness-of-fit test was performed. The receiver operating characteristic （ROC） curve， calibration curves， decision curve analysis， and clinical impact curve were used to comprehensively evaluate the degree of fit， accuracy， consistency， and clinical practicability of the predictive model derived from the training set. The Mann-Whitney U test was used for comparison of non-normally distributed quantitative data between two groups. The chi-square test or Fisher exact test was used for comparison of categorical data between two groups. ResultsThe univariate analysis showed that there were significant differences between the study group and the control group in age， sex， blood ammonia， lymphocytes， hemoglobin， platelet count， prothrombin time， fibrinogen， international normalized ratio， total bilirubin， aspartate aminotransferase， total protein， albumin， prealbumin， alkaline phosphatase， cholinesterase， total bile acid， triglyceride， total cholesterol， high-density lipoprotein， low-density lipoprotein， blood glucose， CD3⁺ T cells， CD4⁺ T cells， CD8⁺ T cells， high-sensitivity C-reactive protein， carcinoembryonic antigen， triiodothyronine， thyroxine， free triiodothyronine， free thyroxine， interleukin-1β， interleukin-5， interleukin-6， interleukin-8， interleukin-12p70， interleukin-17， interferon-γ， and MELD score （all P<0.05）. The LASSO regression analysis identified nine variables of age， blood ammonia， albumin， prealbumin， cholinesterase， total cholesterol， thyroxine， interleukin-17， and MELD score， and the binary Logistic regression analysis showed that blood ammonia， interleukin-17， and MELD score were independent risk factors for HE in ESLD patients， while age and thyroxine were independent protective factors （all P<0.05）. A nomogram model was constructed based on these five variables. The Hosmer-Lemeshow goodness-of-fit test in the training set showed a good degree of fit （P=0.207）， with a McFadden’s pseudo-R² of 0.184， suggesting that the model had acceptable explanatory power； the Hosmer-Lemeshow goodness-of-fit test in the internal validation set further confirmed the calibration stability of the model （P=0.067）， suggesting that the model had a good degree of fit in independent data. The model had an area under the ROC curve of 0.784 in the validation set， with a sensitivity of 0.710 and a specificity of 0.752. The mean absolute error of the calibration curve was 0.067， and decision curve analysis and clinical impact curve showed that the nomogram model had positive net benefit and good clinical practicability. ConclusionThe nomogram model constructed based on age， blood ammonia， thyroxine， interleukin-17， and MELD score for predicting HE in patients with ESLD has a good degree of fit， high accuracy and consistency， and excellent clinical practicability.

Psychological problems are significant factors that contribute to the occurrence of postoperative complications, reduced quality of life, and prolonged hospital stays. Lung cancer patients who require surgery often experience high psychological stress related to both the disease and the treatment, making them more susceptible to depression and anxiety. Therefore, timely intervention to address these psychological issues is crucial for promoting postoperative recovery and improving patients' quality of life. This paper will review the current status, impact, and interventions for depression and anxiety-related psychological problems in lung cancer surgery patients.

Mild cognitive impairment （MCI） is a critical precursor stage of Alzheimer’s disease， and the development of MCI is closely associated with liver function. As a core metabolic organ， the liver regulates cognitive function through the liver-brain axis. Epidemiological studies have shown the association between liver function parameters and MCI. The liver affects cognitive function by clearing peripheral β-amyloid （mainly via the PPARα-LRP-1 pathway）， secreting hepatic factors such as FGF21 and sEH to modulate neuronal energy metabolism， and mediating oxidative stress and systemic inflammatory responses. Anti-inflammatory/antioxidant measures， dietary pattern adjustments， and aerobic exercise are currently effective strategies for regulating liver function and mitigating mild cognitive impairment. This article systematically reviews the role and mechanism of the liver-brain axis in MCI， in order to provide new perspectives for developing liver-targeted therapies for cognitive impairment.

ObjectiveTo investigate the independent risk factors for concurrent myocardial injury before intervention in patients with primary hepatic carcinoma （PHC）， to construct and validate a nomogram prediction model for the early identification of such patients， and to provide a basis for early clinical prevention and management. MethodsA retrospective analysis was performed for the clinical data of 1 822 patients with PHC in two medical institutions from January 2019 to June 2025， among whom the 1 518 patients from The Third People’s Hospital of Kunming were used as the training set to construct the nomogram prediction model， and the 304 patients from The Second People’s Hospital of Baoshan were used as the validation set. According to the result of high-sensitivity cardiac troponin T measurement before intervention， the patients with PHC were divided into myocardial injury group and non-myocardial injury group. The univariate analysis， the LASSO regression analysis， and the multivariate Logistic regression analysis were used to identify the risk factors of concurrent myocardial injury in PHC patients before intervention， and then a nomogram prediction model was constructed based on these findings. The independent data of the 304 patients in the validation set were used for external validation， and the area under the receiver operating characteristic curve （AUC）， the Hosmer-Lemeshow test， the calibration curve， decision curve analysis， and clinical impact curve were used to assess model performance. ResultsIn the training set， there were 346 patients in the myocardial injury group and 1 172 patients in the non-myocardial injury group， and the prevalence rate of concurrent myocardial injury was 22.79%. The mean age was 56.44±9.45 years， and the male patients accounted for 81.49%. The patients with drinking， smoking， chronic hepatitis B， chronic hepatitis C， hypertension， diabetes， Child-Pugh class A liver disease， BCLC stage B liver disease， or abnormal electrocardiographic findings accounted for 49.34%， 57.44%， 61.73%， 32.48%， 22.66%， 17.19%， 50.53%， 33.86%， and 62.12%， respectively. The univariate analysis， the LASSO regression analysis， and the multivariate Logistic regression analysis showed that age （odds ratio ［OR］=1.051， 95% confidence interval ［CI］： 1.036 — 1.067， P<0.001）， history of diabetes （OR=1.862， 95%CI： 1.346 — 2.574， P<0.001）， electrocardiographic findings （OR=1.446， 95%CI： 1.077 — 1.940， P=0.014）， hemoglobin （HGB） （OR=0.993， 95%CI： 0.988 — 0.998， P=0.003）， albumin （Alb） （OR=0.951， 95%CI： 0.930 — 0.972， P<0.001）， and creatinine （Cr） （OR=1.021， 95%CI： 1.016 — 1.026， P<0.001） were independent influencing factors for myocardial injury in PHC patients. A regression equation was established as Logit（Y）=-3.406+0.050×age+0.621×history of diabetes+0.369×electrocardiogram－0.007×HGB－0.051×Alb+0.021×Cr， and a nomogram prediction model was constructed based on these factors. The Bootstrap internal validation of the model yielded an AUC of 0.743 （95%CI： 0.707 — 0.778）， and the model had an AUC of 0.766 （95%CI： 0.738 — 0.794） in the training set and 0.730 （95%CI： 0.688 — 0.793） in the validation set. The Hosmer-Lemeshow goodness-of-fit test yielded a P value of 0.716 for the training set and 0.408 for the validation set， suggesting that the model had a good degree of fitting. Calibration curves， decision curve analysis， and clinical impact curves in both the training set and the validation set showed that the model had a good clinical application value. ConclusionAge， history of diabetes， electrocardiographic findings， HGB， Alb， and Cr are independent influencing factors for myocardial injury in patients with PHC， and the nomogram prediction model constructed based on these factors has good discriminatory ability， accuracy， and clinical practicability.