Ischemic cholangiopathy （IC） is a severe clinical condition resulting from blood supply impairment in bile ducts， and its etiology includes vascular causes and non-vascular factors. The pathogenesis of IC mainly involves microcirculatory disturbance of the peribiliary blood plexus and biliary epithelial necrosis and fibrosis induced by ischemia-reperfusion injury. The clinical manifestations of IC are highly heterogeneous. The typical symptoms include jaundice， abdominal pain， and cholangitis， while non-specific symptoms， such as fatigue and fever， often cause delays in diagnosis. The diagnosis of IC mainly relies on radiological examination for identifying characteristic stenosis and microcirculatory disturbance， assisted by cholestasis biomarkers including alkaline phosphatase and gamma-glutamyl transferase. Therapeutic strategies include conventional pharmacotherapy， endoscopic intervention， and innovative techniques. Prognostic evaluation emphasizes the dynamic monitoring of alkaline phosphatase and bilirubin and classification based on radiological examination， and long-term management requires individualized monitoring regimens to prevent complications.

BackgroundNon-suicidal self-injury （NSSI） has become a public health concern of wide interest. Adolescents with depressive disorder are at high risk of NSSI behaviors. Previous studies have mostly focused on analyzing factors associated with NSSI behaviors， while research on developing visual models that are intuitive， quantifiable， and suitable for rapid clinical assessment is relatively limited. ObjectiveTo investigate the risk factors for NSSI behaviors in adolescents with depressive disorder， and to construct a nomogram prediction model for the risk of NSSI behaviors occurrence， so as to provide a quantitative tool for clinical rapid assessment of the occurrence risk of NSSI behaviors in this population. MethodsA cross-sectional study was conducted. A total of 448 adolescent patients diagnosed with depressive disorder who attended the Mental Health Department of The Second Hospital， Lanzhou University from February to December 2025 and met the diagnostic criteria for depressive disorder as specified in the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） were enrolled. All participants were randomly divided into the modeling group （n=313） and the validation group （n=135） at a ratio of 7∶3. Assessments were performed using the Self-rating Depression Scale （SDS）， the Self-rating Anxiety Scale （SAS）， the Barratt Impulsiveness Scale-11 （BIS-11）， the Childhood Trauma Questionnaire-short form （CTQ-SF）， and the Pittsburgh Sleep Quality Index （PSQI）. Binary Logistic regression analysis was applied to screen independent predictors of NSSI behaviors in the modeling group， and a nomogram was constructed accordingly. The receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis （DCA） were used to evaluate the discrimination， calibration and clinical utility of the model， respectively. ResultsBinary Logistic regression analysis revealed that younger age， female sex， higher SDS score， higher BIS-11 score， higher CTQ-SF score， and higher PSQI score were risk factors for NSSI behaviors in adolescent patients with depressive disorder （OR=0.816， 2.671， 1.131， 1.030， 1.064， 1.102， P<0.05 or 0.01）. The Hosmer-Lemeshow test of the nomogram prediction model constructed based on these six indicators showed a good fit （χ²=15.144， P=0.056）. Model validation showed that the AUC was 0.895 for the modeling group and 0.891 for the validation group. The calibration curves suggested that the mean absolute errors between the actual values and the predicted values were 0.043 and 0.023， respectively. Decision curve analysis indicated that the model provided a clear net clinical benefit when the risk threshold was greater than 0.11. ConclusionThe nomogram model based on six indicators （age， sex， depressive symptom severity， impulsivity， childhood trauma， and sleep quality） for predicting NSSI risk in adolescent patients with depressive disorder exhibits good discrimination， calibration， and clinical application value. ［Funded by National Natural Science Foundation of China （number， 82560281）； Gansu Provincial Science and Technology Program （number， 25JRRA618）； Gansu Provincial Health Industry Research Project （number， GSWSKY2025-13）； The College Students' Innovation and Entrepreneurship Training Program of the Second Clinical Medical College of Lanzhou University （number， 20250050060）］

Distal cholangiocarcinoma （dCCA） often has subtle symptoms in its early stage， and the optimal surgical timing is often missed at the time of diagnosis. This article reports a patient with dCCA who attended the hospital due to obstructive jaundice. Although the patient had a history of liver fluke infection and positive serological results， persistent stricture of the common bile duct remained unrelieved after adequate drainage and infection control. Due to the longitudinal growth pattern of the tumor， multiple endoscopic biopsies yielded false-negative results. Multidisciplinary consultation recommended a 1-month follow-up， but the patient failed to return to hospital for review and was found to have liver metastasis （stage Ⅳ） at the time of confirmed diagnosis， and thus the patient missed the opportunity for radical surgery. This article summarizes the common pitfalls and lessons in the early diagnosis of dCCA and proposes that for highly suspected cases in clinical practice， it is crucial to conduct multidisciplinary diagnosis and treatment， integrate dynamic imaging and tumor marker monitoring， and implement proactive structured evaluations， so as to avoid diagnostic and therapeutic delays and ultimately improve the prognosis of patients.

Objective: To investigate the aberrant alterations of microRNAs ( miRNAs) in exosomes derived from bone marrow stromal cells ( BMSCs) in the bone marrow supernatants of patients with acute myeloid leukemia (AML) and their impact on the prognosis of AML patients . Methods: Bone marrow supernatant samples were col- lected from three AML patients and three healthy donors . Exosomes were isolated using a commercial kit , identif- ying the morphology and marker expression , and subjected to miRNA sequencing to determine differentially ex- pressed miRNAs (DE-miRNAs) . The DE-miRNAs were then intersected with the exosomal miRNA expression pro- files of primary AML cells (GSE64029) to exclude AML cell - derived signals and to identify BMSC-derived DE - miRNAs . Subsequently , candidate miRNAs were identified through Cox regression and Lasso regression analyses based on data from The Cancer Genome Atlas (TCGA) . A prognostic risk model for AML was constructed , and pa- tients were stratified into high-risk and low-risk groups according to the median risk score . The prognostic value and clinical relevance of the model were further validated . Finally , the target genes of the candidate miRNAs were pre- dicted , followed by pathway enrichment analysis , construction of key regulatory networks , and correlation analysis between the expression levels of key miRNAs and their corresponding target genes . Results: Isolated exosomes ex- hibited a typical cup-shaped morphology with intact structures with particle size of 30 - 150 nm , and expressed exo- somal markers CD63 , ALIX , and TSG101 . miRNA sequencing identified 103 DE-miRNAs in AML patients com- pared with healthy donors; after intersection with the GSE64029 dataset , 83 BMSC-derived DE-miRNAs were re- tained . Among these , five candidate miRNAs ( miR-25-3p , miR-532-5p , miR-194-5p , miR-10a-5p , and miR- 20a-5p) were used to construct the prognostic model . Kaplan-Meier survival analysis demonstrated significantly lon- ger overall survival in the low-risk group compared with the high-risk group (P < 0. 05) . The areas under the ROC curve for the training/validation cohorts were 0. 80/0. 74 , 0. 80/0. 78 , and 0. 79/0. 64 at 1 , 2 , and 3 years , re- spectively . The prognostic model was significantly associated with risk stratification , patient age , and FAB classifi- cation (P < 0. 05) . KEGG pathway enrichment revealed that target genes of the candidate miRNAs were closely linked to cancer-related signaling pathways , including hepatocellular carcinoma , breast cancer , and non-small cell lung cancer. Correlation analysis indicated that the candidate miRNAs were significantly associated with key genes such as HIF1A , CREB1 , PIK3CA , IGF1R , PIK3R1 , TIAM1 , CRK , and PTEN (P < 0. 05) . Conclusion AML patients exhibit distinct miRNA expression profiles in BMSC-derived exosomes . A five-miRNA signature ( miR-25 - 3p , miR-532-5p , miR-194-5p , miR-10a-5p , and miR-20a-5p) demonstrates robust prognostic performance , sup- porting its potential clinical utility in risk stratification and outcome prediction for AML.

Objective:To investigate the association between different sleep patterns and anxiety and depression in hemodialysis patients, thereby providing a reference for improving their psychological and sleep conditions.Methods:This study was a cross-sectional survey.A convenience sampling method was used to select patients undergoing regular dialysis at a hemodialysis centre in the Sixth People's Hospital of Huizhou from May 2023 to May 2024. Sleep quality, anxiety and depression symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale for (SAS) and Self-rating Depression Scale (SDS). Potential profiles were analyzed using Mplus 8.3 and mixed-effects Logistic regression was used to explore the association between sleep pattern category and anxiety-depression.Results:A total of 264 valid questionnaires were returned, of which 142 were males and 122 were females, aged (56.61 ± 12.69) years old. The sleep patterns of hemodialysis patients were divided into three potential categories:patients with overall better sleep quality31.4%(83/264), patients with poor sleep using hypnotic medication12.9%(34/264), and patients with poor sleep without hypnotic medication 55.7%(147/264). Significant differences were found in age, education, anxiety and depression across different sleep categories ( χ2 values were 9.75-25.72, all P<0.05). Compared with the group with overall better sleep quality, the risk of anxiety was higher in the group with sleep difficulties without hypnotic medication ( OR=5.409, P<0.05), and the risk of anxiety and depression ( OR=5.010, 6.488, both P<0.05) was higher in the group with sleep disorders using hypnotic medication. Compared with patients with poor sleep without hypnotic medication, patients with poor sleep using hypnotic medication had a higher risk of depression ( OR=6.501, P<0.05). Conclusions:There are three potential categories of sleep patterns in hemodialysis patients and significant correlations between them and anxiety and depression, and precise screening and individualized interventions need to be implemented in the clinic to improve patients' quality of life.

Objective:To investigate the association between different sleep patterns and anxiety and depression in hemodialysis patients, thereby providing a reference for improving their psychological and sleep conditions.Methods:This study was a cross-sectional survey.A convenience sampling method was used to select patients undergoing regular dialysis at a hemodialysis centre in the Sixth People's Hospital of Huizhou from May 2023 to May 2024. Sleep quality, anxiety and depression symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale for (SAS) and Self-rating Depression Scale (SDS). Potential profiles were analyzed using Mplus 8.3 and mixed-effects Logistic regression was used to explore the association between sleep pattern category and anxiety-depression.Results:A total of 264 valid questionnaires were returned, of which 142 were males and 122 were females, aged (56.61 ± 12.69) years old. The sleep patterns of hemodialysis patients were divided into three potential categories:patients with overall better sleep quality31.4%(83/264), patients with poor sleep using hypnotic medication12.9%(34/264), and patients with poor sleep without hypnotic medication 55.7%(147/264). Significant differences were found in age, education, anxiety and depression across different sleep categories ( χ2 values were 9.75-25.72, all P<0.05). Compared with the group with overall better sleep quality, the risk of anxiety was higher in the group with sleep difficulties without hypnotic medication ( OR=5.409, P<0.05), and the risk of anxiety and depression ( OR=5.010, 6.488, both P<0.05) was higher in the group with sleep disorders using hypnotic medication. Compared with patients with poor sleep without hypnotic medication, patients with poor sleep using hypnotic medication had a higher risk of depression ( OR=6.501, P<0.05). Conclusions:There are three potential categories of sleep patterns in hemodialysis patients and significant correlations between them and anxiety and depression, and precise screening and individualized interventions need to be implemented in the clinic to improve patients' quality of life.

The Choledochal cyst is an extremely rare congenital anomaly of the bile duct. Early cyst resection and Roux-en-Y hepatojejunostomy are the primary surgical methods for treating choledochal cyst. With the emergence of enhanced recovery after surgery, laparoscopic surgery has effectively reduced the incidence of biliary complications and wound infections, but it still does not meet people's requirements for minimally invasive surgery. Robotic surgery system has the potential to enhance surgical precision and the maneuverability of surgeons due to clear surgical visualization and flexible mechanical arms. The authors review the relevant literatures and conduct a Meta-analysis to evaluate the efficacy of robot-assisted surgery and laparoscopic surgery for choledochal cyst.

Objective To explore the comparative study of constructing prediction models for muscle invasive of bladder cancer based on different machine learning algorithms combined with MRI radiomic.Methods A total of 187 bladder cancer patients who underwent MRI examination and were confirmed by pathology were retrospectively selected.Patients were randomly divided into a training set and a test set in a 7∶3 ratio.The patients were divided into muscle invasive bladder cancer(MIBC)group and non-muscle invasive bladder cancer(NMIBC)group according to the surgical pathology results.Tumor volume of interest(VOI)was outlined on the images of T2 WI,diffusion weighted imaging(DWI),and apparent diffusion coefficient(ADC),and the radiomic features were extracted by A.K software,and dimensionality reduction was performed using the maximum relevance minimum redundancy(mRMR)algorithm combined with least absolute shrinkage and selection operator(LASSO).Six machine learning algorithms,including K-nearest neighbor(KNN),decision tree(DT),support vector machine(SVM),logistic regression(LR),random forest(RF),and explainable boosting machine(EBM)were used to construct the radiomic model and calculate the corresponding area under the curve(AUC),accuracy,sensitivity,and specificity,respectively.Results Six machine learning algorithms,including KNN,DT,SVM,LR,RF,and EBM were used to construct the radiomic model,and the AUC values for predicting MIBC in the training set were 0.863,0.838,0.853,0.866,0.977,0.997,and in the test set were 0.748,0.833,0.860,0.868,0.870,0.900.Among them,the MRI radiomic model constructed based on EBM had the highest predictive efficacy for MIBC,with AUC values,accuracy,sensitivity and specificity of 0.997,0.977,0.957 and 0.981 in the training set,and 0.900,0.877,0.800,and 0.894 in the test set,respectively.Conclusion Multiple machine learning algorithms combined with MRI radiomic to construct models have good predictive efficacy for MIBC,and the model constructed based on EBM shows the highest predictive value.

Objective To study the relationship among the sense of organizational fairness, innovative self-efficacy (ISE) and innovative behavior in nurses. Methods A total of 392 nurses from a grade A tertiary hospital were selected as the research subjects using convenience sampling method. The Organizational Fairness Scale, Innovation Self-efficacy Scale, and Innovation Behavior Scale were used to evaluate the sense of organizational fairness, ISE, and innovation behavior, respectively. The mediate equation model was constructed, and Bootstrap analysis was applied for validation. Results The scores for organizational fairness, ISE, and innovative behavior among the nurses were (67.8±15.2), (23.9±3.5), and (30.5±6.7) points, respectively. Organizational fairness score was positively correlated with both innovative behavior and ISE scores ［correlation coefficients (r) were 0.38 and 0.36, respectively, both P<0.01］. ISE score was positively correlated with innovative behavior total score (r=0.51, P<0.01). The results of the mediation analysis indicated that the total effect of organizational fairness on innovation behavior was 0.34 (P<0.01),with a direct effect of 0.17 (P<0.01). ISE plays a mediating role between organizational fairness and innovation behavior among nurses(P<0.01) with standardized mediation effect of 0.17, accounting for 50.0% of the total effect. Conclusion Organizational fairness can influence the ability of innovative behavior directly or through the mediating role of ISE.

Humans ; East Asian People ; Psoriasis/drug therapy* ; Antibodies, Monoclonal, Humanized/therapeutic use* ; Treatment Outcome ; Severity of Illness Index