[Objective]To evaluate the efficacy of ureteral dilation versus ureteral reimplantation in treating pediatric primary obstructive megaureter(POM).[Methods]A retrospective analysis was conducted on clinical data of 53 pediatric patients with POM treated in the Department of Urology,Anhui Provincial Children's Hospital from April 2019 to September 2023.The cohort included 37 boys and 16 girls with 5 bilateral and 48 unilateral cases.The age ranged from 1 to 157 months,with a median age of 17.00(5.50-48.00)months.Patients were assigned to 3 groups based on the management of the ureteral stricture segment:dilation group(18 cases,19 sides),Cohen group(20 cases,24 sides),and Lich-Gregoir group(15 cases,15 sides).The duration of the operations,postoperative hospital stays,postoperative indwelling catheters,postoperative D-J stents;changes in renal pelvis anteroposterior diameter and ureteral diameter;and postoperative complications were compared to evaluate the therapeutic effects.[Results]All 53 patients successfully underwent surgery.The dilation group showed significantly shorter operative time,postoperative hospital stay,and postoperative catheterization duration compared to the Cohen and Lich-Gregoir groups(P＜0.05).However,the postoperative D-J stent time was longer in the dilation group than in the other 2 groups(P＜0.05).Upon follow-ups for 6-12 months after stent removal,all groups demonstrated statistically significant reductions in renal pelvis anteroposterior diameter and ureteral diameter compared to preoperative values(P＜0.05).No significant differences were observed among the 3 groups in hydronephrosis resolution rates(P＞0.05).Additionally,the incidence of postoperative complications(urinary tract infection,vesicoureteral reflux,and reoperation for restenosis)did not differ significantly among the groups(P＞0.05).[Conclusions]Ureteral dilation demonstrated non-inferior short-term clinical efficacy compared to ureteral reimplantation in managing POM in pediatric patients.With reduced operative time,minimal invasiveness,and technical simplicity,ureteral dilation may be considered a preferential treatment option for children with POM.

Objective To propose a method for mass spectrometry imaging(MSI)super-resolution reconstruction based on a spatially multi-level and self-supervised deep learning network(SMSDL-Net),aiming to improve the resolution of mass spectrometry images.Methods SMSDL-Net firstly registered histological and mass spectrometry images using a nonlinear transformation.Then a multi-branch Vision Transformer(ViT)was utilized to extract hierarchical features of high-resolution histological images in a self-supervised manner.These features were subsequently combined with the paired low-resolution mass spectrometry data to construct a regression network,which could realize the prediction of high-resolution mass spectrometry information.To validate the performance of the proposed method,the results by the method were compared with those of the traditional bicubic interpolation(BI)methods based on interpolation processing and the deepFERE method based on convolution neural network(CNN)for super-resolution reconstruction of magnesium elemental image of metal mass spectrometry of human liver cancer samples,and the method was also applied to a mouse renal adenocarcinoma metabolite mass spectrometry imaging dataset.Results Compared with the traditional BI methods and the deepFERE multimodal method,the method proposed demonstrated the lowest root mean square error(RMSE=0.015),the highest structural similarity index measure(SSIM=0.84)and the highest R-squared value(R2=0.853)in reconstructing mass spectrometry images.The effectiveness of the method and its potential for precise tissue-specific distinction were validated using the mouse renal adenocarcinoma metabolite MSI dataset.Conclusion Compared with traditional single-modal and pixel-wise regression deep learning methods,the method proposed enhances the quality of high-resolution mass spectrometry image reconstruction and can serve as a novel method for super-resolution reconstruction in the field of mass spectrometry imaging.[Chinese Medical Equipment Journal,2025,46(4):1-8]

Objective To explore the feasibility of endovascular intervention technology for constructing canine models of hepatic vein(HV)obstruction type Budd-Chiari syndrome(BCS).Methods HV obstruction type BCS models were established through double approaches including external jugular vein and percutaneous transhepatic vein puncture,blocking the main HV with double balloon and injecting anhydrous ethanol using 10 Beagle dogs.Laboratory indexes before and after modeling were compared.Liver ultrasound was performed 2,4 and 8 weeks after modeling,respectively,while intravascular ultrasound(IVUS)and digital subtracting angiography(DSA)were performed after the last time ultrasound to observe the obstruction degree,lumen and blood flow of left hepatic vein(LHV).The stenosis rate of LHV was evaluated based on DSA findings,and successful establishment of HV obstruction type BCS canine model was regarded as stenosis rate＞30%and incomplete occlusion of LHV,then the success rate of modeling was recorded.Results No significant difference of laboratory indexes was found before and after modeling(all P＞0.05).Ultrasound,IVUS and DSA showed that after modeling,different degrees of LHV stenosis complicated with intrahepatic collateral circulation were found in 9 dogs,while complete occlusion of LHV was noticed in 1 dog.The success rate of modeling was 90.00%(9/10).Pathological results showed the target HV lumen narrow with thickened endovascular wall,and the latter mainly composed of fibroblast and collagen fiber proliferation.Conclusion Endovascular intervention technology was feasible for constructing canine models of HV obstruction type BCS.

AIM To investigate the tumor-suppressive mechanism of Guiqi Yiyuan Extract combined with cisplatin in Lewis lung cancer mice.METHODS Ten intact C57BL/6J mice were assigned to the blank group.Sixty additional mice were developed into Lewis lung cancer models bearing transplanted tumor and subsequently allocated into the model group,the cisplatin group(5 mg/kg),the high-dose Guiqi Yiyuan Extract group(6.6 g/kg),and the low-dose,medium-dose and high-dose Guiqi Yiyuan Extract combined with cisplatin group(1.6,3.3,6.6 g/kg+5 mg/kg),with 10 mice in each group.Mice in the blank and model groups received saline via daily gavage,while treatment groups were administered Guiqi Yiyuan Extract orally(once daily),and cisplatin injection intraperitoneally(once every other day).After 14 days of drug administration,mice were euthanized for endpoint analysis.The following assessments were conducted:general health status and body weight changes monitored throughout the study period;tumor excision and weighing for inhibition rate calculation;histopathological examination of tumors via hematoxylin-eosin(HE)staining;serum quantification of IL-1 β,IL-18 and HMGB1 by ELISA;ultrastructural analysis of tumor cell death using transmission electron microscopy(TEM);spatial localization of TXNIP and GSDMD-N in tumor sections via immunofluorescence(IF);and Western blot detection of TXNIP,NLRP3,Caspase-1,cleaved Caspase-1,GSDMD,GSDMD-N protein expressions in tumor tissues.RESULTS Compared to the model group,the cisplatin group and all combination therapy groups exhibited significant reduction in tumor weight(P＜0.05)and increased tumor suppression rate;enhanced tumor tissue necrosis with characteristic pyroptotic morphology;elevated serum levels of IL-1β,IL-18 and HMGB1(P＜0.05);and upregulated expressions of pyroptosis-associated proteins TXNIP,NLRP3,Caspase-1,cleaved Caspase-1,GSDMD and GSDMD-N(P＜0.05).The high dose combination group demonstrated optimal therapeutic efficacy(P＜0.05).CONCLUSION Guiqi Yiyuan Extract enhances cisplatin sensitivity,demonstrating synergistic anti-tumor effects in Lewis lung carcinoma-bearing mice.This combinatorial therapeutic effect likely involves modulation of the TXNIP/NLRP3/Caspase-1/GSDMD pathway.

Objective To propose a method for mass spectrometry imaging(MSI)super-resolution reconstruction based on a spatially multi-level and self-supervised deep learning network(SMSDL-Net),aiming to improve the resolution of mass spectrometry images.Methods SMSDL-Net firstly registered histological and mass spectrometry images using a nonlinear transformation.Then a multi-branch Vision Transformer(ViT)was utilized to extract hierarchical features of high-resolution histological images in a self-supervised manner.These features were subsequently combined with the paired low-resolution mass spectrometry data to construct a regression network,which could realize the prediction of high-resolution mass spectrometry information.To validate the performance of the proposed method,the results by the method were compared with those of the traditional bicubic interpolation(BI)methods based on interpolation processing and the deepFERE method based on convolution neural network(CNN)for super-resolution reconstruction of magnesium elemental image of metal mass spectrometry of human liver cancer samples,and the method was also applied to a mouse renal adenocarcinoma metabolite mass spectrometry imaging dataset.Results Compared with the traditional BI methods and the deepFERE multimodal method,the method proposed demonstrated the lowest root mean square error(RMSE=0.015),the highest structural similarity index measure(SSIM=0.84)and the highest R-squared value(R2=0.853)in reconstructing mass spectrometry images.The effectiveness of the method and its potential for precise tissue-specific distinction were validated using the mouse renal adenocarcinoma metabolite MSI dataset.Conclusion Compared with traditional single-modal and pixel-wise regression deep learning methods,the method proposed enhances the quality of high-resolution mass spectrometry image reconstruction and can serve as a novel method for super-resolution reconstruction in the field of mass spectrometry imaging.[Chinese Medical Equipment Journal,2025,46(4):1-8]

The scaphoid bone is one of the important bone of hand,which is frequently injured and difficult to treat in clinical practice.Therefore,it is very important to deeply study the microstructure and biomechanical characteristics of the scaphoid bone for understanding its injury mechanism and optimizing treatment scheme.Microcomputed tomography(micro-CT)provides high-resolution imaging of bone tissue,while finite element analysis can help to simulate the stress distribution and behavioral patterns of the scaphoid bone under various physiological and pathological states.The high-resolution three-dimensional image of the scaphoid bone obtained by micro-CT technology can be used to construct finite element models of real anatomical structure of the scaphoid bone,thus achieving accurate simulation of the mechanical properties of the scaphoid bone.The fusion of these two advanced technologies provides a new perspective for revealing the structural and functional relationships and injury mechanism of the scaphoid bone.Therefore,this paper reviews the anatomical characteristics of the scaphoid bone and its biomechanical behavior in different states,emphasizing the specific applications and advantages of micro-CT and finite element analysis techniques in the study of the scaphoid bone.By summarizing the research findings in recent years,this paper provides novel scientific basis and methods for the diagnosis,treatment,and prevention of scaphoid bone-related disorders.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

BACKGROUND: Tissue engineering holds promise for vascular repair and regeneration by mimicking the extracellular matrix of blood vessels. However, achieving a functional and thick vascular wall with aligned fiber architecture by electrospinning remains a significant challenge. METHODS: A novel electrospinning setup was developed that utilizes an auxiliary electrode and a spring. The impact of process parameters on fiber size and morphology was investigated. The structure and functions of the scaffolds were evaluated through material characterization and assessments of cellular biocompatibility. RESULTS: The new setup enabled controlled deposition of fibers in different designed orientations. The fabricated small-diameter vascular scaffolds consisted of an inner layer of longitudinally oriented fibers and an outer layer of circumferentially oriented fibers (L + C vascular scaffold). Key parameters, including rotational speed, the utilization of the auxiliary electrode, and top-to-collector distance (TCD) significantly influenced fiber orientation. Additionally, voltage, TCD, feed rate, needle size, auxiliary electrode and collector-auxiliary electrode distance affected fiber diameter and distribution. Mechanical advantages and improved surface wettability of L + C vascular scaffold were confirmed through tensile testing and water contact angle. Cellular experiments indicated that L + C vascular scaffold facilitated cell adhesion and proliferation, with human umbilical vein endothelial cells and smooth muscle cells attaching and elongating along the fiber direction of the inner and outer layer, respectively. CONCLUSION This study demonstrated the feasibility of fabricating fiber-aligned, thick-walled vascular scaffolds using a modified electrospinning setup. The findings provided insights into how the auxiliary electrode, specific collector influenced fiber deposition, potentially advancing biomimetic vascular scaffold engineering.