Objective To understand the status of body image disturbance and its influencing factors in patients with ankylosing spondylitis (AS), so as to provide a scientific basis for the clinical management of AS. Methods A total of 353 AS patients admitted from January 2022 to December 2024 were selected as research subjects. Chinese version of Body Image Disturbance Questionnaire (BIDQ) was used to investigate the body image disturbance in AS patients. Single factor analysis was performed by t test and analysis of variance, and multiple factors were analyzed by multivariate linear regression. Results The total score of BIDQ in 342 AS patients was (25.01±4.22). Multivariate linear regression analysis results showed that self-paid medical expense, nighttime VAS score and negative emotion PANAS score could positively predict body image disturbance in AS patients (standardized regression coefficient=0.413, 0.413, 0.460, P<0.05), and PSSS score, positive emotion PANAS score and exercise management CDSSM score could negatively predict body image disturbance (standardized regression coefficient=-0.245, -0.134, -0.247, P<0.05). Conclusion The body image disturbance in AS patients is worthy of clinical attention. Nighttime pain, negative emotion and self-paid medical treatment can increase the risk of body image disturbance. Positive emotion, social support and high self-management level of exercise behavior can reduce the formation of body image disturbance, which can provide new ideas for clinical management of AS patients.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective: To explore the feasibility, precision, and clinical value of a personalized primary repair approach centered on digital design, integrating 3D printing technology with multiple materials such as titanium mesh, polyetheretherketone (PEEK), and titanium plates, for complex craniofacial bone defects involving the skull, mandible, orbit, and zygoma resulting from traffic accidents, providing a reference for primary repair of clinically complex craniofacial bone defects. Methods: One patient who was admitted in September 2021 with multiple comminuted fractures of the right craniomaxillofacial region and large-area bone defects caused by a traffic accident was selected. Digital design was integrated throughout the entire repair process. First, preoperative computed tomography (CT) data were used for 3D reconstruction of the craniomaxillofacial region; then, based on the model, the anatomical contour of the healthy left side was reproduced via mirroring technology for the defects on the right side. A targeted repair plan was designed: 3D-printed PEEK material was used to reconstruct the right orbital floor and zygomaticomaxillary complex, a 0.6-mm-thick titanium mesh was adopted to repair the right skull defect, and a 2.0-mm-thick titanium plate was applied for rigid internal fixation of the mandibular fracture. A one-stage repair surgery was completed simultaneously. In addition, a literature review was conducted on studies related to the repair of complex combined craniomaxillofacial defects. Results: CT examination at 1 week postoperatively showed that the average fitting gap of the implants was 0.3 mm, and the symmetry difference of the facial contour was less than 5 mm. At 3 months postoperatively, the patient’s maximum mouth opening reached 38 mm, the occlusal relationship returned to normal, and the diplopia symptom completely disappeared. During the 6-month postoperative follow-up, no complications such as implant loosening, infection, or displacement occurred; the FACE-Q scale score was 91, indicating a high level of subjective patient satisfaction. The literature review indicated that digital design combined with 3D printing technology can significantly improve the accuracy of complex craniomaxillofacial bone defect reconstruction. PEEK material is suitable for the reconstruction of the orbital floor and zygomaticomaxillary complex. Titanium mesh and plates can ensure the stability of the reconstruction. Multi-materials combined reconstruction represents an important therapeutic strategy for such defects. Conclusion The individualized one-stage repair scheme, centered on digital design and combined with 3D printing technology and multi-materials (titanium mesh, PEEK, and titanium plates), can achieve precise anatomical reduction and simultaneous functional recovery for complex combined craniomaxillofacial bone defects caused by traffic accidents.

The application of immunosuppressants has significantly reduced the incidence rate of rejection reaction after liver transplantation， but the clinical efficacy of immunosuppressants is greatly affected by individual differences between patients. This article systematically reviews the recent research advances in the interaction between immunosuppressants and gut microbiota， with a focus on the regulatory role and mechanism of intestinal microflora communities on the efficacy of immunosuppressants. Studies have shown that intestinal microbiome is one of the key factors influencing the efficacy of immunosuppressive therapy after liver transplantation. This review aims to provide a theoretical basis for in-depth research in this field and provide new insights for developing individualized immunosuppressive treatment regimens based on the regulation of intestinal microflora.

Objective: To evaluate the diagnostic performance of magnetic resonance spectroscopy (MRS) in discriminating fumarate hydratase-deficient (FH-d) uterine leiomyomas (ULs) from FH-preserved ULs. Materials and Methods: This study consisted of three stages, with independent cohorts recruited for each stage: 1) sample-size estimation was retrospectively performed on UL specimens (diameter ≥3 cm; age, 20–40 years) from our database with immunohistochemistry (IHC) for 2-succinocysteine (2-SC) as the reference, without genetic testing, 2) MRS sequence optimization in confirmed FH germline mutation participants with ultrasound-detected ULs (diameter ≥3 cm), without IHC analysis, and 3) prospective diagnostic test accuracy was evaluated in consecutive participants with ultrasound-detected ULs (diameter ≥3 cm;age, 20–40 years), using IHC for 2-SC for determining the FH status and subsequent genetic testing in those with positive 2-SC results to identify whether FH mutations were germline or somatic in origin. The choline and fumarate peaks in MRS were classified as positive, negative, or technical failure (TF). TFs were analyzed separately and excluded from the primary diagnostic accuracy calculations. T1-, T2-, and diffusion-weighted images were interpreted as hyperintense or hypointense. The enhancement rate and apparent diffusion coefficient were also acquired. Diagnostic performance was compared between MRS and various magnetic resonance imaging (MRI) features. Results: The optimal MRS parameters for the fumarate peak were echo time (TE) = 140 ms and an average of 256. Among the 360 prospective participants, 37 were confirmed to have FH-dULs. MRS showed positive fumarate peaks in 35 of 37 FH-dULs.After excluding six TFs, the positive fumarate peak on MRS showed 94.6% (35/37) sensitivity, 99.7% (316/317) specificity, and 99.2% (351/354) accuracy, all of which were significantly superior to those of other MRI features (P ≤ 0.002). Conclusion A positive fumarate peak on MRS may be a useful imaging biomarker for diagnosing FH-dULs.

ObjectiveTo investigate the independent risk factors for decompensation in patients with liver cirrhosis， to construct a nomogram-based risk assessment model， and to assess its risk assessment performance and clinical value by comparing it with Model for End-Stage Liver Disease （MELD）， MELD combined with serum sodium concentration （MELD-Na）， and MELD 3.0 scoring system. MethodsA retrospective analysis was performed for 514 patients with liver cirrhosis who attended The First Hospital of Shanxi Medical University from January 2020 to May 2025， and related data were collected， including demographic data and laboratory markers. According to the presence or absence of decompensation， the patients were divided into compensation group with 275 patients and decompensation group with 239 patients. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator regression analysis was used for screening of variables， and the multivariate logistic regression analysis was used to identify independent risk factors for decompensated liver cirrhosis and construct a nomogram model. The receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis were used to assess the discriminatory ability， calibration， and net clinical benefit of the model. ResultsThe multivariate logistic regression analysis showed that low hemoglobin （odds ratio ［OR］=0.984， 95% confidence interval ［CI］： 0.969　—　0.999， P<0.05）， low lymphocytes （OR=0.564， 95%CI： 0.383　—　0.830， P<0.05）， high international normalized ratio （OR=3.131， 95%CI： 1.242　—　7.891， P<0.05）， and low serum sodium （OR=0.922， 95%CI： 0.872　—　0.975， P<0.05） were independent risk factors for decompensation events in patients with liver cirrhosis. Construct the prediction model equation： Logit（P）=intercept value－0.016×hemoglobin－0.573×lymphocytes+1.141×INR－0.081×serum sodium. The nomogram constructed based on these factors had a good discriminatory ability， with an area under the ROC curve （AUC） of 0.787， a specificity of 77.8%， and a sensitivity of 67.4%， and it had significantly better predictive performance than MELD score （AUC=0.718）， MELD-Na score （AUC=0.719）， and MELD 3.0 score （AUC=0.725）. The calibration curve showed good consistency between the probability of risk assessed by the model and the observed probability. The decision curve analysis showed that compared with the MELD-based scores， this model provided higher net clinical benefit across a wide range of risk thresholds. ConclusionThis study successfully constructed and validated a nomogram risk assessment model incorporating hemoglobin， international normalized ratio， lymphocytes， and serum sodium. This model has good clinical practicability and can help to achieve early identification of high-risk patients with decompensated liver cirrhosis and optimize intervention strategies in clinical practice.

ObjectiveTo construct and validate a risk assessment model for post-transplant diabetes mellitus （PTDM） using multiple machine learning algorithms， and to realize the early identification of PTDM. MethodsA retrospective analysis was performed for the clinical data of the patients who underwent allogeneic liver transplantation in The First Hospital of Shanxi Medical University from April 1， 2020 to December 31， 2024， and they were randomly divided into a training set and a validation set at a ratio of 7∶3. The LASSO regression analysis combined with 5-fold cross-validation was used for feature selection. Seven machine learning models were developed in the training set， i.e.， logistic regression （LR）， decision tree （DT）， Naive Bayes （NB）， random forest （RF）， K-nearest neighbor （KNN）， extreme gradient boosting （XGBoost）， and adaptive boosting （AdaBoost）. In the validation set， various methods were used to assess the predictive performance of each model， such as accuracy， precision， recall rate， specificity， F1 score， area under the receiver operating characteristic curve （AUROC）， and area under the precision-recall curve （AUPRC）. The Brier score and decision curve analysis were used to assess the calibration and clinical practicability of the models， and the SHAP method was used to analyze feature importance. The independent-samples t test or the Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. ResultsA total of 135 liver transplant recipients were enrolled， among whom 26 developed PTDM， and there were 94 patients in the training set and 41 in the validation set. Feature extraction and screening identified 7 key features of sex， overweight or obesity， anhepatic phase， time of operation， length of hospital stay， early postoperative hypomagnesemia， and impaired fasting glucose （IFG）. In the validation set， the XGBoost model showed the best predictive performance， with an AUROC of 0.907 （95% confidence interval ［CI］： 0.807 — 0.989）， an AUPRC of 0.649 （95%CI： 0.339 — 0.955）， an accuracy of 0.878， a precision of 0.667， a recall rate of 0.750， an F1-score of 0.706， a specificity of 0.909， and a Brier score of 0.104. The decision curve analysis showed that when the threshold probability was below 0.667， application of the XGBoost model in clinical decision-making provided relatively high net benefit. The SHAP analysis showed that the length of hospital stay ranked first in terms of feature importance， followed by time of operation， overweight or obesity， sex， IFG， anhepatic phase， and early postoperative hypomagnesemia. ConclusionThe risk assessment model for PTDM in liver transplant recipients based on XGBoost algorithm has excellent performance and can effectively identify high-risk individuals； however， multicenter large-sample data are needed for further validation.

ObjectiveTo investigate the effects of zinc finger protein 473 （ZNF473）， a member of the zinc finger protein family， on the proliferation and migration of liver hepatocellular carcinoma （LIHC） cells and to explore its potential underlying mechanism. MethodsThe expression levels of ZNF473 in pan-cancer and in HCC tissues versus normal tissues were analyzed using The Cancer Genome Atlas （TCGA） database. The prognostic significance of ZNF473 in patients with HCC was evaluated using the Kaplan-Meier Plotter database with the Log-rank test. Human HCC cell lines HepG2 and MHCC97-H were used as experimental models and divided into a negative control （NC） group and an experimental group （si-ZNF473）. ZNF473 interference vectors were transfected into HepG2 and MHCC97-H cells. CCK-8 assays， Transwell assays， and wound-healing assays were performed to assess the effects of ZNF473 on the proliferation and migration of HepG2 and MHCC97-H cells. Western blot was used to detect changes in the expression levels of phosphorylated protein kinase B （p-AKT）， c-Myc， AKT， and other related proteins in HCC cells after ZNF473 knockdown. All cell experiment data were analyzed using GraphPad Prism 9.0 software. ResultsTCGA database analysis showed that ZNF473 was highly expressed in 17 cancer types， including LIHC. Immunohistochemical （IHC） staining data from the Human Protein Atlas （HPA） database indicated that ZNF473 was highly expressed in LIHC tissues. Single-cell RNA sequencing analysis showed that ZNF473 expression was higher in the malignant cell subpopulation than in other cell subpopulations. Survival analysis of patients with LIHC using the Kaplan-Meier Plotter database demonstrated that the high-ZNF473-expression group had significantly shorter overall survival （OS）， disease-specific survival （DSS）， progression-free survival （PFS）， and recurrence-free survival （RFS） than the low-expression group. CCK-8 assays showed that knockdown of ZNF473 reduced the proliferative capacity of HepG2 and MHCC97-H cells. Wound-healing and Transwell assays demonstrated that ZNF473 knockdown suppressed the migratory ability of HepG2 and MHCC97-H cells. RT-qPCR results showed no statistically significant difference in phosphatase and tensin homolog （PTEN） relative expression in the si-ZNF473 group， whereas the expression levels of PIK3CA and PIK3R1 were significantly altered （P<0.05）. Western blot analysis showed that downregulation of ZNF473 significantly decreased the protein expression levels of p-AKT and c-Myc （P<0.05）. ConclusionZNF473 is highly expressed in LIHC cells and tissues. ZNF473 may promote the proliferation and migration of LIHC cells by regulating the PI3K/AKT/c-Myc signaling pathway.