Molecularly imprinted polymers(MIPs)of colchicine(COL)were prepared by precipitation polymerization with COL as template,methacrylic acid(MAA)as monomer,ethylene glycol dimethacrylate(EGDMA)as crosslinking agent,azobisisobutyronitrile(AIBN)as initiator and chloroform as porogen.The basic structure of the prepared MIPs was characterized by scanning electron microscopy(SEM),transmission electron microscopy(TEM)and Fourier transform infrared(FT-IR)spectrometry,and the adsorption properties of MIPs were evaluated by kinetic adsorption,isothermal adsorption and selective adsorption experiments.The results indicated that the prepared MIPs had uniform particles size,with a maximum adsorption capacity of 62.61 mg/g for COL and an imprinting factor of 3.74,and was suitable for sample pretreatment in determination of COL blood concentration,enabling the enrichment and separation of trace or even ultra-trace target components,thereby improving analytical capabilities.

BACKGROUND:Carnosic acid,a bioactive compound found in rosemary,has been shown to reduce inflammation and reactive oxygen species(ROS).However,its mechanism of action in osteoclast differentiation remains unclear. OBJECTIVE:To investigate the effects of carnosic acid on osteoclast activation,ROS production,and mitochondrial function. METHODS:Primary bone marrow-derived macrophages from mice were extracted and cultured in vitro.Different concentrations of carnosic acid(0,10,15,20,25 and 30 μmol/L)were tested for their effects on bone marrow-derived macrophage proliferation and toxicity using the cell counting kit-8 cell viability assay to determine a safe concentration.Bone marrow-derived macrophages were cultured in graded concentrations and induced by receptor activator of nuclear factor-κB ligand for osteoclast differentiation for 5-7 days.The effects of carnosic acid on osteoclast differentiation and function were then observed through tartrate-resistant acid phosphatase staining,F-actin staining,H2DCFDA probe and mitochondrial ROS,and Mito-Tracker fluorescence detection.Western blot and RT-PCR assays were subsequently conducted to examine the effects of carnosic acid on the upstream and downstream proteins of the receptor activator of nuclear factor-κB ligand-induced MAPK signaling pathway. RESULTS AND CONCLUSION:Tartrate-resistant acid phosphatase staining and F-actin staining showed that carnosic acid dose-dependently inhibited in vitro osteoclast differentiation and actin ring formation in the cell cytoskeleton,with the highest inhibitory effect observed in the high concentration group(30 μmol/L).Carnosic acid exhibited the most significant inhibitory effect during the early stages(days 1-3)of osteoclast differentiation compared to other intervention periods.Fluorescence imaging using the H2DCFDA probe,mitochondrial ROS,and Mito-Tracker demonstrated that carnosic acid inhibited cellular and mitochondrial ROS production while reducing mitochondrial membrane potential,thereby influencing mitochondrial function.The results of western blot and RT-PCR revealed that carnosic acid could suppress the expression of NFATc1,CTSK,MMP9,and C-fos proteins associated with osteoclast differentiation,and downregulate the expression of NFATc1,Atp6vod2,ACP5,CTSK,and C-fos genes related to osteoclast differentiation.Furthermore,carnosic acid enhanced the expression of antioxidant enzyme proteins and reduced the generation of ROS during the process of osteoclast differentiation.Overall,carnosic acid exerts its inhibitory effects on osteoclast differentiation by inhibiting the phosphorylation modification of the P38/ERK/JNK protein and activating the MAPK signaling pathway in bone marrow-derived macrophages.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

This study investigated the impact of metabolic syndrome (MetS) and its components on prostate volume (PV) in the general Chinese population. In total, 43 455 participants in The First Medical Center of the Chinese PLA General Hospital (Beijing, China) from January 1, 2012, to December 31, 2022, undergoing health examinations were included in the study. Participants were categorized into four groups according to PV quartiles: Q1 (PV ≤24.94 ml), Q2 (PV >24.94 ml and ≤28.78 ml), Q3 (PV >28.78 ml and ≤34.07 ml), and Q4 (PV >34.07 ml), with Q1 serving as the reference group. Logistic regression analyses were used to examine the association between MetS and PV, with subgroup analyses conducted by age. Among the participants, 18 787 (43.2%) were diagnosed with MetS. In the multivariate analysis model, a significant correlation between MetS and PV was observed, with odds ratios (ORs) increasing as PV increased (Q2, OR = 1.203, 95% confidence interval [CI]: 1.139-1.271; Q3, OR = 1.300, 95% CI: 1.230-1.373; and Q4, OR = 1.556, 95% CI: 1.469-1.648). Analysis of MetS components revealed that all components were positively associated with PV, with abdominal obesity showing the most significant effect. The number of MetS components was identified as a dose-dependent risk factor for elevated PV. The impact of MetS, its components, and component count on PV exhibited a decreasing trend with advancing age. Overall, the influence of MetS, its components, and component count on PV was predominantly observed in the age groups of 40-49 years and 50-59 years. Early intervention targeting MetS can significantly alleviate the increase in PV, particularly benefiting individuals aged 40-59 years who have abdominal obesity.
Humans ; Male ; Metabolic Syndrome/complications* ; Middle Aged ; Cross-Sectional Studies ; Aged ; Prostate/diagnostic imaging* ; Adult ; Age Factors ; Organ Size ; China/epidemiology* ; Obesity, Abdominal ; Risk Factors

Surgical methods for varicocele remain controversial. This study intends to evaluate the efficacy and safety of different surgical approaches for treating varicocele through a network meta-analysis (NMA). PubMed, Embase, Cochrane, and Web of Science databases were thoroughly searched. In total, 13 randomized controlled trials (RCTs) and 24 cohort studies were included, covering 9 different surgical methods. Pairwise meta-analysis and NMA were performed by means of random-effects models, and interventions were ranked based on the surface under the cumulative ranking curve (SUCRA). According to the SUCRA, microsurgical subinguinal varicocelectomy (MSV; 91.6%), microsurgical retroperitoneal varicocelectomy (MRV; 78.2%), and microsurgical inguinal varicocelectomy (MIV; 76.7%) demonstrated the highest effectiveness in reducing postoperative recurrence rates. In this study, sclerotherapy embolization (SE; 87.2%), MSV (77.9%), and MIV (67.7%) showed the best results in lowering the risk of hydrocele occurrence. MIV (82.9%), MSV (75.9%), and coil embolization (CE; 58.7%) were notably effective in increasing sperm motility. Moreover, CE (76.7%), subinguinal approach varicocelectomy (SV; 69.2%), and SE (55.7%) were the most effective in increasing sperm count. SE (82.5%), transabdominal laparoscopic varicocelectomy (TLV; 76.5%), and MRV (52.7%) were superior in shortening the length of hospital stay. The incidence rates of adverse events for MRV (0), SE (3.3%), and MIV (4.1%) were notably low. Cluster analyses indicated that MSV was the most effective in the treatment of varicocele. Based on the existing evidence, MSV may represent the optimal choice for varicocele surgery. However, selecting clinical surgical strategies requires consideration of various factors, including patient needs, surgeon experience, and the learning curve.
Humans ; Male ; Embolization, Therapeutic/methods* ; Microsurgery/methods* ; Randomized Controlled Trials as Topic ; Sclerotherapy/methods* ; Treatment Outcome ; Urologic Surgical Procedures, Male/methods* ; Varicocele/surgery*

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

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.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.