Objective:To analyze the role of interleukin-33(IL-33)in the occurrence and development of glioma and its related mechanism by bioinformatics technology,and to validate it through histopathological experiments,and to discuss the possibility of IL-33 as an auxiliary marker for the diagnosis and treatment of brain glioma.Methods:The glioblastoma multiforme/lower grade glioma(GBMLGG)case data were downloaded from the UCSC XENA database,including data of 689 glioma samples,5 paracancerous samples,and 1 152 normal brain tissue samples;Mann-Whitney U test was used to analyze the difference in the expression of IL-33 mRNA between the GBMLGG samples and the normal brain tissues;according to the expression level of IL-33 in GBMLGG tissue,the tumor samples were divided into IL-33 low expression group and IL-33 high expression group;the Human Protein Atlas(HPA)was used to validate the difference in the protein expression of IL-33 in the GBMLGG samples;the R language DESeq2(v.1.36.0)package was used to screen the differentially expressed genes(DEGs)in the GBMLGG tumor case samples;Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis were used to perform pathway analysis on the DEGs;Gene Set Enrichment Analysis(GSEA)was used to discuss the pathways significantly enriched by IL-33 in the GBMLGG tissues;GSVA package was used to analyze the immune infiltration in the GBMLGG samples;survival package and survminer package were used to analyze the effect of IL-33 expression level on the survival of the patients in different clinical subgroups of GBMLGG;univariate and multivariate Cox proportional hazards regression models were used to analyze the relationship between IL-33 expression and the clinicopathological characteristics of the GBMLGG patients;the GBMLGG and control tissue samples were collected;immunohistochemical staining was used to detect the expression levels of IL-33 and its receptor suppression of tumorigenicity 2(ST2)in the GBMLGG and normal brain tissue samples.Results:The expression levels of IL-33 mRNA and protein in the GBMLGG tissues were significantly increased compared with those in normal brain tissues;there were 634 DEGs in total between the IL-33 low and high expression groups,including 283 up-regulated DEGs and 351 down-regulated DEGs;the GO functional enrichment analysis and KEGG signaling pathway enrichment analysis results showed that the DEGs were associated with biological behaviors such as activation of the classical pathway of complement,immunoglobulin complex formation,and mediated immunoglobulin receptor binding;in the course of GBMLGG development,high expression of IL-33 could degrade valine,leucine,and isoleucine,induce limonene and pinene degradation,promote propanoate metabolism,and simultaneously activate the Leishmania infection pathway,NOD-like receptor signaling pathway,and allograft rejection pathway;the infiltration levels of dendritic cell(DC)and mast cell in the IL-33 high expression group were higher than those in IL-33 low expression group;the infiltration levels of eosinophil,helper T cell,and central memory T cell(Tcm)were lower than those in IL-33 low expression group;the expression level of IL-33 was positively correlated with the infiltration of γδT cell(Tgd),helperT cell,macrophage,eosinophil,Tcm,and effector memory T cell(Tem)(P＜0.05);it was negatively correlated with the infiltration levels of DC,natural killer cell(NK),CD8+T cell,and CD56bright NK cell(P＜0.05).There were no significant differences in the overall survival(OS),disease-specific survival(DSS),and disease-free interval(DFI)of the GBMLGG patients between IL-33 high expression group and IL-33 low expression group(P＞0.05);the clinical subgroup analysis results showed that the expression level of IL-33 in oligodendrocytoma tissues was lower than those in astrocytoma and oligoastrocytoma tissues,and the expression level of IL-33 in glioblastoma tissues was higher than that in oligodendroglioma tissues.World Health Organization(WHO)stage and age were risk factors affecting the prognosis of the GBMLGG patients,and IDH mutation and primary treatment effect were protective factors affecting the prognosis;The immunohistochemical staining results showed that compared with normal brain tissues,the expression levels of IL-33 and its receptor ST2 proteins in the malignant glioma tissues were significantly increased(P＜0.05),and their expression levels were positively correlated in both normal brain tissues and malignant glioma tissues(P＜0.05).Conclusion:The expression level of IL-33 in the glioma tissue is significantly increased,and high expression of IL-33 may be a potential factor for poor prognosis in the glioma patients.

Objective:To develop and validate a deep learning-based multimodal model integrating clinical and radiomic features for predicting acute kidney injury（AKI）risk after partial nephrectomy.Methods:A retrospective analysis was conducted on 416 patients who underwent partial nephrectomy at Zhongshan Hospital，Fudan University from January 2023 to January 2025. The cohort included 100 AKI patients［defined by a ≥ 25% reduction in postoperative evaluated glomerular filtration rate（eGFR）within 48 hours sustained for >24 hours］and 316 non-AKI patients（1∶3 ratio，randomly matched with 16 additional cases for redundancy）. Clinical and radiomic features were extracted from preoperative contrast-enhanced CT scans using PyRadiomics. Demographics included 259 males and 158 females，with a median age of 57（49，65）years，body mass index of（24.1 ± 3.3）kg/m2，preoperative eGFR of（88.5 ± 18.3）ml/（min·1.73 m2），postoperative eGFR（48-hour）of（76.0 ± 21.9）ml/（min·1.73 m2），Zhongshan Score（ZSscore）of 7.34 ± 2.01，and R.E.N.A.L. score of 7.50 ± 1.71. All tumors were T 1a stage. Patients were divided into training（n = 312）and test（n = 104）sets（3∶1 ratio）. A clinical model was constructed via multivariate logistic regression，while radiomic and combined（clinical + radiomic）models utilized an artificial neural network（ANN）with 1 input layer，5 hidden layers，1 output layer，and 10 5 training epochs. Model performance was evaluated by using receiver operating characteristic（ROC）curves and area under the curve（AUC），and was compared to the Martini model. Feature contributions were interpreted via SHapley Additive exPlanations（SHAP）. Results:In the test set，the results of multivariate logistic regression showed that patient’s weight，preoperative eGFR，R.E.N.A.L. score，surgical approach，and operation time were risk factors for AKI（ P < 0.05）. The AUC of the clinical feature prediction model constructed based on the above factors was 0.852（95% CI 0.775?0.929）. In the test set，the AUC of the Martini model was 0.725（95% CI 0.565?0.791）. The radiomic model，trained on 1 315 imaging features，achieved an AUC of 0.898（95% CI 0.804?0.993）with 94.2%（98/104）accuracy. The combined clinical and radiomic model，integrating 1 315 radiomic features and clinical features，demonstrated superior performance with an AUC of 0.946（95% CI 0.887?1.000）and 96.2%（100/104）accuracy，outperforming both the clinical model（ P = 0.03）and the Martini model（ P < 0.01）. SHAP analysis identified the top five predictors in the combined model：ZSscore（SHAP value：0.78），long-run low gray-level emphasis（SHAP value：0.61），run-length non-uniformity（SHAP value：0.58），size-zone non-uniformity（SHAP value：0.46），and gray-level co-occurrence matrix joint energy（SHAP value：0.36）. Conclusions:The deep learning-based multimodal model integrating clinical and radiomic features accurately predicts AKI risk after partial nephrectomy，offering a novel strategy for preoperative risk stratification and personalized intervention.

Objective:To investigate the clinical efficacy of posterior midline incision combined with anteromedial approach in the treatment of complex olecranon fracture-dislocation.Methods:A retrospective analysis was performed on 26 patients (15 males and 11 females) with olecranon fracture-dislocation who were admitted from January 2020 to January 2024, including 5 cases of anterior transolecranon fracture-dislocation (2 cases of upper ulnar-radial joint dislocation), 21 cases of posterior transolecranon fracture-dislocation (5 cases of them were accompanied by upper ulnar-radial joint dislocation). Among them, there were 13 cases of traffic accidents, 7 cases of falling from heights, and 6 cases of walking falls. The average age is 45.1±15.3 years old (21-84 years old).Results:The operation time was 151.2±41.9 minutes, average tourniquet time was 93.7±22.6 minutes, and the intraoperative blood loss was 76.2±20.2 ml. The average follow-up was 16(12, 23) months, and the VAS score decreased significantly and the MEPS score increased significantly over time. At the last follow-up, the VAS score was 2(1, 2), and the MEPS score was 86.5±10.3, with 16 cases excellent, 7 cases good, and 3 cases medium, with an excellent rate of 89%. The range of motion of flexion-extension and pronation-supination were 119.3°±13.5°and 138.6°±15.2° respectively. Complications included 16 cases of ectopic ossification, of which 4 patients with significant effects on elbow function underwent surgical release 3-6 months after surgery. 1 case of ulnar nerve injury symptoms improved after emergency ulnar nerve release, and 1 case of elbow subluxation due to inaccurate coronoid process reduction and fixation. There were no serious complications such as vascular injury, internal fixation failure, fracture nonunion, and incision infection.Conclusion:The posterior midline incision combined with anteromedial approach can effectively treat complex olecranon fracture-dislocation and meet the requirements of early postoperative elbow rehabilitation.

ObjectiveTraditional Chinese medicine (TCM) constitutes a valuable cultural heritage and an important source of antitumor compounds. Poria (Poria cocos (Schw.) Wolf), the dried sclerotium of a polyporaceae fungus, was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia. Traditionally recognized for its diuretic, spleen-tonifying, and sedative properties, modern pharmacological studies confirm that Poria exhibits antioxidant, anti-inflammatory, antibacterial, and antitumor activities. Pachymic acid (PA; a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid), isolated from Poria, is a principal bioactive constituent. Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms, though these remain incompletely characterized. Neuroblastoma (NB), a highly malignant pediatric extracranial solid tumor accounting for 15% of childhood cancer deaths, urgently requires safer therapeutics due to the limitations of current treatments. Although PA shows multi-mechanistic antitumor potential, its efficacy against NB remains uncharacterized. This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking, dynamic simulations, and in vitro assays, aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays. MethodsThis study employed network pharmacology to identify potential targets of PA in NB, followed by validation using molecular docking, molecular dynamics (MD) simulations, MM/PBSA free energy analysis, RT-qPCR and Western blot experiments. Network pharmacology analysis included target screening via TCMSP, GeneCards, DisGeNET, SwissTargetPrediction, SuperPred, and PharmMapper. Subsequently, potential targets were predicted by intersecting the results from these databases via Venn analysis. Following target prediction, topological analysis was performed to identify key targets using Cytoscape software. Molecular docking was conducted using AutoDock Vina, with the binding pocket defined based on crystal structures. MD simulations were performed for 100 ns using GROMACS, and RMSD, RMSF, SASA, and hydrogen bonding dynamics were analyzed. MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex. In vitro validation included RT-qPCR and Western blot, with GAPDH used as an internal control. ResultsThe CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability. GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress, vesicle lumen, and protein tyrosine kinase activity. KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT, MAPK, and Ras signaling pathways. Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1, EGFR, SRC, and HSP90AA1. RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1, EGFR, and SRC while increasing the HSP90AA1 mRNA and protein levels. ConclusionIt was suggested that PA may exert its anti-NB effects by inhibiting AKT1, EGFR, and SRC expression, potentially modulating the PI3K/AKT signaling pathway. These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

Objective:To compare the consistency of microarray chemiluminescent protein chip and immunoblotting in the autoantibody spectrum of patients and the diagnostic efficacy of systemic lupus erythematosus(SLE), and to explore the correlation between the detection results of protein microarray and clinical indicators and lymphocyte subsets.Methods:Serum autoantibodies in 649 samples collected between December 2023 and December 2024 in Nanjing Drum Tower Hospital were analyzed using the microarray chemiluminescent protein chip method, with 401 samples simultaneously tested by immunoblotting. Kappa coefficient assessed inter-method consistency. Diagnostic performance was compared via ROC curves. Spearman correlation analysis evaluated relationships between autoantibody levels and SLEDAI-2000 scores, clinical parameters, and lymphocyte subsets.Results:The two methods demonstrated good consistency across 14 antinuclear antibodies, with optimal agreement for anti-SSA/Ro ( Kappa=0.845, P<0.001), anti-SSB ( Kappa=0.825, P<0.001), and anti-CENP B ( Kappa=0.851, P<0.001). The protein chip method significantly improved SLE diagnostic efficacy, particularly for anti-dsDNA (AUC difference=0.291, P<0.001) and anti-Sm antibodies (AUC difference=0.295, P<0.001). Combined detection of anti-SSA/Ro and anti-nRNP/Sm antibodies achieved superior diagnostic performance (AUC=0.927). Anti-dsDNA, anti-histone, and anti-nucleosome antibodies positively correlated with SLEDAI-2000 ( r=0.408, 0410, 0.384, all P<0.001), complement ( P<0.001), and 24-hour urinary protein ( r=0.374, 0.387, 0.301, all P<0.001). Immunological analysis showed that the proportion of NK cells was generally negatively correlated with antinuclear antibodies such as anti-dsDNA ( r=-0.352, P<0.001) and anti-Sm ( r=-0.328, P<0.001) antibodies. Meanwhile, the proportion of CD8 + T cells was positively correlated with anti-nRNP/Sm ( r=0.229, P=0.002) and anti-Sm antibodies ( r=0.211, P=0.005). The proportion of CD4 + T cells was negatively correlated with anti-SSA/Ro ( r=-0.239, P<0.001), while the proportion of B cells was positively correlated with anti-dSDNA antibody ( r=0.300, P<0.001). Conclusion:The protein chip method showed strong consistency with immunoblotting for detecting 14 autoantibodies but demonstrated superior SLE diagnostic efficacy. The combined use of multiple detection methods can enhance the reliability of clinical diagnosis.