Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

The working principle of Bausch&Lomb BL11110 phacoemulsification system was described.Three cases of typical faults of the phacoemulsification system were introduced,and the causes were analyzed,then the maintenance measures were given accordingly.References were provided for diagnosing and eliminating the faults of the phacoemulsification system.[Chinese Medical Equipment Journal,2025,46(4):118-120]

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Objective To analyze the relationship between the visceral adiposity index(VAI)and nocturia in the US adult population.Methods A cross-sectional study was performed.Data from subjects aged≥20 years in the National Health and Nutrition Examination Survey(NHANES)database from 2007 to 2020 were collected,including waist circumference,triglyceride,body mass index(BMI),high-density lipoprotein,age,gender,race,poverty income ratio,education level,marital status,smoking,alcohol consumption,sleep disorders,depression,occupation,hypertension,diabetes,congestive heart failure,cancer,and nocturnal urination frequency.Weighted analysis,multivariate logistic regression,generalized additive model(GAM),and curve fitting were employed to evaluate the association between VAI and nocturia,adjusting for age,gender,race,poverty income ratio,education level,marital status,smoking,alcohol consumption,sleep disorders,depression,occupation,hypertension,diabetes,congestive heart failure,and cancer.Subgroup analyses were conducted based on age,gender,race,hypertension and diabetes to further evaluate the relationship between VAI and the risk of nocturia.Results A total of 29,196 American adults were included.All subjects were divided into 4 groups based on VAI quartiles:Q1 group(0.32≤VAI<1.01),Q2 group(1.01≤VAI<1.70),Q3 group(1.70≤VAI<2.95),and Q4 group(2.95≤VAI<13.59),with nocturia prevalence rates of 28.5%,31.4%,33.3%,and 34.9%,respectively.In subgroup analyses,the risk of nocturia significantly increased with higher VAI in the 20-40 age group,females and other Hispanics(OR=1.04,95%CI 1.01-1.08,P=0.006;OR=1.02,95%CI 1.00-1.04,P=0.035;OR=1.05,95%CI 1.01-1.09,P=0.026).GAM analysis results showed a nonlinear relationship between VAI and nocturia.Conclusion VAI is positively associated with the risk of nocturia,and may be an effective indicator for predicting the risk of nocturia occurrence.

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

Objective To investigate the factors influencing global longitudinal strain(GLS)measured by cardiac magnetic resonance(CMR)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods Clinical data of 315 hospitalized patients diagnosed with acute STEMI who underwent percutaneous coronary intervention(PCI)at the First Medical Center of Chinese PLA General Hospital from June 2016 to September 2021 were retrospectively collected.After analyzing CMR images of all patients,GLS and other strain parameters were obtained,and then the patients were divided into two groups according to the median GLS.In order to balance gender and age differences,1:1 propensity score matching was performed,and 206 patients were eventually included:GLS>-11.3%group(indicating severe GLS impairment,n=103)and GLS≤-11.3%group(n=103).Baseline characteristics,laboratory indicators,coronary angiographic parameters,electrocardiogram(ECG)features,and CMR parameters were compared between the two groups.Variables showing significant differences were analyzed for their correlation with GLS.Multivariate logistic regression and multiple stepwise linear regression analyses were performed to identify factors associated with GLS impairment.Results Compared with GLS≤-11.3%group,GLS>-11.3%group had significantly higher peak levels of creatine kinase-MB(CK-MB)and troponin T(TnT)(P<0.001).A higher proportion of patients in GLS>-11.3%group had the left anterior descending artery(LAD)as the culprit vessel,while a lower proportion had the right coronary artery(RCA)as the culprit vessel(P<0.001).Additionally,GLS>-11.3%group had longer QRS duration(P<0.001)and a higher incidence of pathological Q waves(P=0.001).Regarding CMR parameters,GLS>-11.3%group exhibited larger global circumferential strain(GCS),infarct size(IS),and left ventricular end-systolic volume(LVESV),as well as lower global radial strain(GRS)and left ventricular ejection fraction(LVEF)(P<0.001).Multivariate logistic regression indicated that peak TnT(OR=1.092,P=0.001),LAD culprit vessel(OR=3.744,P<0.001),and QRS duration(OR=1.026,P<0.001)were significantly associated with severely impaired GLS.Multiple stepwise linear regression analysis showed that the logarithmic value of peak TnT,LAD as the culprit vessel,and the square root of QRS duration were linearly correlated with GLS values(adjusted R2=0.301,P<0.001),and these independent variables explained 30.1%of the variation in GLS.Conclusion Elevated peak TnT,prolonged QRS duration,and LAD as the culprit vessel are significantly associated with severe GLS impairment in STEMI patients,indicating more severe myocardial infarction and worse left ventricular function.

Objective To explore the targeted regulatory relationship of miR-330-5p on OY-TES-1 in glioblastoma and the effect of miR-330-5p/OY-TES-1 axis on the migration ability of glioblastoma.Methods Bioinformatics analysis was performed to analyze the expression level of miR-330-5p in patients with glioblastoma and its influence on prognosis and survival of patients.The glioblastoma cells U251 were divided into miR-330-5p minics group,minics-NC group,and miR-330-5p+OY-TES-1 overexpression group(miR-330-5p minics+pcDNA3.1-OY-TES-1).The effect of miR-330-5p on the activity of OY-TES-1 3'UTR region was detected by double luciferase reporter gene experiment.The expression of OY-TES-1 mRNA was detected by qRT-PCR.The effect of miR-330-5p/OY-TES-1 axis on the migration ability of glioblastoma cells was detected by Transwell migration assay.Results The expression of miR-330-5p in glioblastoma tissue was significantly lower than those in non-tumor brain tissue and low-grade glioma tissue(P<0.05).The survival time of glioblastoma patients with high expression of miR-330-5p was significantly longer than that of patients with low expression of miR-330-5p(P<0.05).After overexpression of miR-330-5p,the activity of OY-TES-1 3'UTR region was decreased(P<0.05).Compared with minics-NC group,the expression levels of OY-TES-1 mRNA of U251 and U87MG cells in miR-330-5p minics group were significantly decreased(P<0.01).Compared with minics-NC group,the numbers of migrating cells in miR-330-5p minics group and miR-330-5p+OY-TES-1 overexpression group were significantly decreased(P<0.05).Compared with miR-330-5p minics group,the number of migrating cells in miR-330-5p+OY-TES-1 overexpression group was significantly increased(P<0.01).Conclusion MiR-330-5p targets OY-TES-1 to inhibit the migration of glioblastoma.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.