Objective: To investigate the efficacy and safety of robot-assisted modified Y-shaped ileal orthotopic neobladder reconstruction，so as to provide reference for clinical practice. Methods: The clinical data of 44 patients who underwent robot-assisted laparoscopic radical cystectomy，lymph node dissection，and modified Y-shaped ileal orthotopic neobladder reconstruction during Feb.2020 and Aug.2022 were retrospectively analyzed.The surgical position，Trocar position，and key surgical steps were reported.The perioperative conditions，postoperative complications，neobladder volume，maximum urinary flow rate，postvoid residual，renal function，and urinary control function were recorded. Results: All 44 surgeries were successfully completed，with operation time of (314.32±51.02) min，modified Y-shaped ileal orthotopic neobladder reconstruction time of (103.52±9.56) min，and bleeding volume of (128.18±57.27) mL.The postoperative time for fluid intake was (4.16±0.86) days，catheter indwelling time was (14.02±3.20) days，and patients were discharged 1 to 2 days after catheter removal.Clavien-Dindo grade Ⅱ and Ⅲ complications occurred in 15 and 2 patients，respectively.During the follow-up of (20.77±5.90) months，dysuria occurred in 1 case，urethral calculi in 2 cases，and incomplete bowel obstruction in 2 cases. The postoperative neobladder capacity was (195.75±15.51) mL，maximal urinary flow rate (20.30±2.05) mL/s，postvoid residual (19.86±13.80) mL and serum creatinine (81.98±25.97) μmol/L. The incidence of daytime and nocturnal urinary incontinence 3，6 and 12 months after operation were 20.45% and 29.55%，11.36% and 18.18%，and 4.55% and 9.09%，respectively. Conclusion: Robot-assisted modified Y-shaped ileal orthotopic neobladder reconstruction has favorable efficacy and safety，and low incidence of postoperative complications，which can be applied in clinical practice.

Objective:To evaluate the detection rate, anatomical distribution, and influencing factors of bone marrow edema-like signal (BMELS) in the knees of amateur marathon runners.Methods:This was a cross-sectional study. This study publicly recruited amateur marathon runners through the Hangzhou Long-distance Running Association from January 2019 to December 2024. Based on knee magnetic resonance imaging (MRI) results, participants were divided into a BMELS-positive group (BMELS present in at least one knee) and a BMELS-negative group. General clinical information was collected from participants using a questionnaire. All participants underwent a knee MRI scan to screen for the presence of BMELS and to assess its severity. Logistic regression models were used to analyze the associated factors of BMELS in amateur marathon runners, while Spearman′s correlation analysis assessed the correlation between BMELS grade and these factors.Results:A total of 60 subjects (120 knee joints) were enrolled, including 39 males (65%), aged (40.0±8.4) years. Of these, 39 were in the BMELS-positive group, and 21 were in the BMELS-negative group. The BMELS detection rate for the 120 knee joints of these 60 subjects was 53.3% (64/120). BMELS were detected in 43.3%(52/120) of the femur, 34.2% (41/120) of tibia and 17.5% (21/120) of patella. Multivariate logistic regression analysis showed an independent positive correlation between monthly running volume and knee BMELS in amateur marathon runners ( OR=1.007, 95% CI: 1.000-1.013, P=0.035). Spearman′s correlation analysis showed a weak positive correlation between the BMELS grade of the knees and the monthly running volume of amateur marathon runners ( r s=0.360, P=0.005). Conclusions:The detection rate of knee BMELS is high in amateur marathon runners, and they are distributed in a way that is characteristic of the region, with a higher incidence in the medial femoral condyle. In this population, monthly running volume is independently associated with knee BMELS, with a higher grade associated with greater monthly running volume.

Objective:To evaluate the diagnostic performance of the Vesical Imaging-Reporting and Data System（VI-RADS）based on multiparametric magnetic resonance imaging（mp-MRI）for muscle-invasive bladder cancer（MIBC）.Methods:A systematic search was conducted in PubMed，Web of Science，and Embase databases for studies published between September 2018 and December 2023 that investigated the use of VI-RADS for diagnosing MIBC. Inclusion criteria were studies utilizing mp-MRI-based VI-RADS scoring to determine MIBC. Exclusion criteria were studies with fewer than 10 patients，overlapping study populations，or those failing to assess the diagnostic performance of VI-RADS for MIBC. After quality assessment，RevMan 5.4 and Stata 15.1 were used to calculate pooled sensitivity and specificity，generate forest plots and summary receiver operating characteristic（SROC）curves，and determine the area under the curve（AUC）. Publication bias was assessed using Deeks funnel plot. Heterogeneity was evaluated using the I2 statistic，with meta-regression and subgroup analyses to explore its sources. Results:Twenty-nine studies involving 3 577 patients were included. At a VI-RADS cutoff of 3，the pooled sensitivity and specificity for MIBC diagnosis were 93%（95%CI 0.90-0.95）and 82%（95%CI 0.76-0.88），respectively. At a cutoff of 4，these values were 83%（95%CI 0.78-0.87）and 93%（95%CI 0.90-0.95）. The hierarchical SROC（HSROC）AUCs were 0.95 and 0.94 for cutoffs of 3 and 4，respectively. Subgroup and meta-regression analyses revealed that at a cutoff of 3，patient sample size，study design，MRI field strength，number of radiologists，surgical approach，and DWI/DCE imaging planes contributed to sensitivity heterogeneity（ P < 0.05）. All factors except study design and DWI plane were sources of specificity heterogeneity（ P < 0.05）. At a cutoff of 4，all factors significantly influenced heterogeneity in both sensitivity and specificity（ P < 0.05）. Meta-regression confirmed that both cutoffs（3 and 4）were significant sources of heterogeneity（ P < 0.05）. Conclusions:VI-RADS demonstrates excellent diagnostic performance for MIBC at both cutoffs（3 and 4），with VI-RADS ≥ 3 showing superior sensitivity and VI-RADS ≥ 4 offering higher specificity. The cutoff of 3 provides better overall diagnostic efficacy.

Objective:To address the inefficiency, safety hazards, and resource wastage in traditional medical laboratory management, this study proposes a digital laboratory information management system (LIMS) based on Total Quality Management (TQM) principles. The LIMS has been implemented at the National Center for Geriatric Medicine (Huashan) affiliated with Fudan University.Methods:Centered on the principles of " all-staff participation, whole-process control, and comprehensive management", a multidimensional management framework was developed to integrate personnel, equipment, reagents, and safety protocols. The system incorporated IoT, digital twin, and artificial intelligence (AI) technologies to achieve end-to-end digital control. A layered architecture (physical layer, data layer, model layer, etc.) was designed to integrate functional modules such as full lifecycle equipment management, dual-authentication for hazardous chemicals, and intelligent resource scheduling. A 3D digital twin model was deployed to visualize real-time laboratory operations.Results:Post-implementation, equipment reservation frequency and usage duration at the National Center increased by 114% and 124%, respectively, with no safety incidents reported. Equipment sharing utilization reached 85%, and reagent expiration waste decreased by 30%.Conclusions:The system transforms laboratory management from experience-driven to data-driven by breaking data silos and optimizing collaboration mechanisms. It provides a replicable technical pathway and practical insights for the healthcare industry′s digital transformation. However, further improvements are needed in mobile support and system scalability.

Objective To evaluate the value of radiomics models and comprehensive models based on multiparametric magnetic resonance imaging(mpMRI)in predicting the expression of human epidermal growth factor receptor 2(HER2)in bladder cancer(BCa).Methods A total of 76 pathologically confirmed BCa patients undergoing pelvic mpMRI during Jan.2022 and Nov.2024 at the Affiliated Zhongda Hospital of Southeast University were retrospectively included.After the volume of interest(VOI)was sketched,7 modal features were obtained,including T2WI,DWI,DCE,T2WI+DWI,T2WI+DCE,DWI+DCE,and T2WI+DWI+DCE,which were analyzed with logistic regression to obtain the predictive values.After that,the best sequences were screened with receiver operating characteristic(ROC)curves,and then combined with support vector machine,logistic regression,K-nearest neighbor,plain Bayes and adaptive enhancement,to construct the radiomics prediction models.Logistic regression analysis was used to screen the predictors of high HER 2 expression and to construct a comprehensive prediction model and a nomogram.Finally,decision curve analysis(DCA)was used to quantify the clinical benefits.Results Among the radiomics models based on the T2WI+DWI+DCE sequence,the AdaBoost model demonstrated the best predictive performance,with area under the ROC curve(AUC)being 0.863(95％CI:0.807-0.920)in the training set and 0.716(95％CI:0.601-0.830)in the validation set.Based on radiomics features and clinical imaging characteristics,logistic regression analysis identified tumor pedicle and risk group as the predictors of high HER2 expression.The comprehensive prediction model based on the two factors achieved the AUC of 0.869(95％CI:0.772-0.965)in the training set and 0.875(95％CI:0.712-0.986)in the validation set.Conclusion The radiomics model based on the T2WI+DWI+DCE sequence has high accuracy in predicting HER2 expression,outperforming single-sequence models.The nomogram based on the comprehensive prediction model has high clinical decision-making efficacy and is useful for non-invasive identification of HER2 expression.

Objective To evaluate the value of radiomics models and comprehensive models based on multiparametric magnetic resonance imaging(mpMRI)in predicting the expression of human epidermal growth factor receptor 2(HER2)in bladder cancer(BCa).Methods A total of 76 pathologically confirmed BCa patients undergoing pelvic mpMRI during Jan.2022 and Nov.2024 at the Affiliated Zhongda Hospital of Southeast University were retrospectively included.After the volume of interest(VOI)was sketched,7 modal features were obtained,including T2WI,DWI,DCE,T2WI+DWI,T2WI+DCE,DWI+DCE,and T2WI+DWI+DCE,which were analyzed with logistic regression to obtain the predictive values.After that,the best sequences were screened with receiver operating characteristic(ROC)curves,and then combined with support vector machine,logistic regression,K-nearest neighbor,plain Bayes and adaptive enhancement,to construct the radiomics prediction models.Logistic regression analysis was used to screen the predictors of high HER 2 expression and to construct a comprehensive prediction model and a nomogram.Finally,decision curve analysis(DCA)was used to quantify the clinical benefits.Results Among the radiomics models based on the T2WI+DWI+DCE sequence,the AdaBoost model demonstrated the best predictive performance,with area under the ROC curve(AUC)being 0.863(95％CI:0.807-0.920)in the training set and 0.716(95％CI:0.601-0.830)in the validation set.Based on radiomics features and clinical imaging characteristics,logistic regression analysis identified tumor pedicle and risk group as the predictors of high HER2 expression.The comprehensive prediction model based on the two factors achieved the AUC of 0.869(95％CI:0.772-0.965)in the training set and 0.875(95％CI:0.712-0.986)in the validation set.Conclusion The radiomics model based on the T2WI+DWI+DCE sequence has high accuracy in predicting HER2 expression,outperforming single-sequence models.The nomogram based on the comprehensive prediction model has high clinical decision-making efficacy and is useful for non-invasive identification of HER2 expression.

Objective:To evaluate the detection rate, anatomical distribution, and influencing factors of bone marrow edema-like signal (BMELS) in the knees of amateur marathon runners.Methods:This was a cross-sectional study. This study publicly recruited amateur marathon runners through the Hangzhou Long-distance Running Association from January 2019 to December 2024. Based on knee magnetic resonance imaging (MRI) results, participants were divided into a BMELS-positive group (BMELS present in at least one knee) and a BMELS-negative group. General clinical information was collected from participants using a questionnaire. All participants underwent a knee MRI scan to screen for the presence of BMELS and to assess its severity. Logistic regression models were used to analyze the associated factors of BMELS in amateur marathon runners, while Spearman′s correlation analysis assessed the correlation between BMELS grade and these factors.Results:A total of 60 subjects (120 knee joints) were enrolled, including 39 males (65%), aged (40.0±8.4) years. Of these, 39 were in the BMELS-positive group, and 21 were in the BMELS-negative group. The BMELS detection rate for the 120 knee joints of these 60 subjects was 53.3% (64/120). BMELS were detected in 43.3%(52/120) of the femur, 34.2% (41/120) of tibia and 17.5% (21/120) of patella. Multivariate logistic regression analysis showed an independent positive correlation between monthly running volume and knee BMELS in amateur marathon runners ( OR=1.007, 95% CI: 1.000-1.013, P=0.035). Spearman′s correlation analysis showed a weak positive correlation between the BMELS grade of the knees and the monthly running volume of amateur marathon runners ( r s=0.360, P=0.005). Conclusions:The detection rate of knee BMELS is high in amateur marathon runners, and they are distributed in a way that is characteristic of the region, with a higher incidence in the medial femoral condyle. In this population, monthly running volume is independently associated with knee BMELS, with a higher grade associated with greater monthly running volume.