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.

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.

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.

Thirty-one phenolic constituents were isolated and purified from the 95% ethanol extract of Sanguisorbae Radix by using various chromatographic techniques, including macroporous resin, silica gel, ODS, Sephadex LH-20 and semi-preparative HPLC. Their structures were elucidated by physicochemical properties, spectroscopic data (MS and NMR) and electronic circular dichroism (ECD) spectra, and identified as 3-methoxyl-2S,3S-epoxyflavanone (1a), 3-methoxyl-2R,3R-epoxyflavanone (1b), longifoin B (2), longifoin C (3), eriodictyol (4), naringenin (5), liquiritigenin (6), 5,3ʹ-dihydroxy-7,4ʹ-dimethoxyflavanone (7), naringenin-7-O-β-D-glucopyranoside (8), dihydroquercetin (9), dihydrokaempferol (10), (-)-garbanzol (11), (2R,3R)-4-methoxyl-distylin (12), kaempferol (13), quercetin (14), α,4,2′,4′-tetrahydroxydihydrochalcone (15), phloretin (16), (+)-catechin (17), ethyl (+)-cyanidan-3-ol-8-carboxylate (18), phyllocoumarin (19), methyl 3-methoxy-4,5-dihydroxybenzoate (20), 4,5-dimethoxy-3-hydroxybenzoic acid methyl ester (21), 3,4′-di-O-methylellagic acid (22), 3,4,3′-O-trimethylellagic acid (23), 3,3ʹ,4ʹ-O-trimethylellagic acid-4-O-β-D-xyloside (24), (3R)-thunberginol C (25), resveratrol (26), 1-hydroxypinoresinol (27), (7S,8S)-3-methoxy-3′,7-epoxy-8,4′-oxyneoligna-4,9,9′-triol (28), emodin-8-O-β-D-glucoside (29), phloracetophenone (30) and 4-(4′-hydroxyphenyl)-butan-2-one (31). Among them, compound 1a and 1b is a pair of new flavonoid enantiomers, compounds 2 and 3 are a pair of new epimers, while compounds 4, 5, 6, 9, 10, 13, 16 and 26 were obtained from S. officinalis for the first time, compounds 7, 8, 27, 30 and 31 were isolated for the first time from the S. officinalis genus, and compounds 11, 12, 15, 18, 19, 25, 28 and 29 were isolated for the first time from the Rosaceae. The antioxidant activities of compounds 1-24 were evaluated by activating the Nrf2 transcriptional pathway, which were measured by the dual-luciferase reporter gene assay in 293T cells. Compounds 4, 6-10, 12, 14, 17, 19, 20 and 22-24 showed significant Nrf2 agonistic effect compared with the control group at 25 μmol·L^-1, which provided reference for the research of their antioxidant activity.

Objective To discuss the feasibility and safety of simultaneous bilateral adrenal vein sampling(AVS)using two 4F-MPA1 catheters via right elbow vein access.Methods A total of 51 consecutive patients with primary aldosteronism,who received simultaneous bilateral AVS using two 4F-MPA1 catheters(one of the two catheters was shaped into pig tail figure)via right elbow vein access at Xiangyang Municipal Central Hospital between October 2021 and October 2022,were enrolled in this study.The used catheter,the success rate of simultaneous bilateral AVS,and the incidence of complications rate were calculated.Results The 4F-MPA1 catheter was used for all of the right AVS,while a specially shaped 4F-MPA1 catheter was used for the main trunk vein AVS of the left adrenal gland and the central vein AVS of the left adrenal gland.The success rate of simultaneous bilateral AVS was 92.2%(47/51).Adrenal hematoma occurred in one patient(1.96%).Conclusion The technique of simultaneous bilateral AVS using two 4F-MPA1 catheters via right elbow vein access is simple to operate,less traumatic,and clinically safe and feasible.However,due to the small sample used in this study,the clinical value of this technique still needs further investigation and verification.

Objective To investigate the infection status and changing trend of hepatitis C virus(HCV)infection in hospitalized patients in medical institutions,and provide reference for formulating HCV infection prevention and control strategies.Methods HCV infection surveillance results from cross-sectional survey data reported to China Healthcare-associated Infection(HAI)Surveillance System in 2020 were summarized and analyzed,HCV positive was serum anti-HCV positive or HCV RNA positive,survey result was compared with the survey results from 2003.Results In 2020,1 071 368 inpatients in 1 573 hospitals were surveyed,738 535 of whom underwent HCV test,4 014 patients were infected with HCV,with a detection rate of 68.93％and a HCV positive rate of 0.54％.The positive rate of HCV in male and female patients were 0.60％and 0.48％,respectively,with a statistically sig-nificant difference(x2=47.18,P＜0.001).The HCV positive rate in the 50-＜60 age group was the highest(0.76％),followed by the 40-＜50 age group(0.71％).Difference among all age groups was statistically signifi-cant(x2=696.74,P＜0.001).In 2003,91 113 inpatients were surveyed.35 145 of whom underwent HCV test,resulting in a detection rate of 38.57％;775 patients were infected with HCV,with a positive rate of 2.21％.In 2020,HCV positive rates in hospitals of different scales were 0.46％-0.63％,with the highest in hospital with bed numbers ranging 600-899.Patients'HCV positive rates in hospitals of different scales was statistically signifi-cant(X2=35.34,P＜0.001).In 2020,12 provinces/municipalities had over 10 000 patients underwent HCV-rela-ted test,and HCV positive rates ranged 0.19％-0.81％,with the highest rate from Hainan Province.HCV posi-tive rates in different departments were 0.06％-0.82％,with the lowest positive rate in the department of pedia-trics and the highest in the department of internal medicine.In 2003 and 2020,HCV positive rates in the depart-ment of infectious diseases were the highest,being 7.95％and 3.48％,respectively.Followed by departments of orthopedics(7.72％),gastroenterology(3.77％),nephrology(3.57％)and general intensive care unit(ICU,3.10％)in 2003,as well as departments of gastroenterology(1.35％),nephrology(1.18％),endocrinology(0.91％),and general intensive care unit(ICU,0.79％)in 2020.Conclusion Compared with 2003,HCV positive rate decreased significantly in 2020.HCV infected patients were mainly from the department of infectious diseases,followed by departments of gastroenterology,nephrology and general ICU.HCV infection positive rate varies with gender,age,and region.

Transcatheter aortic valve replacement(TAVR)has become one of the effective methods for treating patients with aortic valve disease.With the continuous maturity of technology,innovation of instruments and increasing experience,the indications for TAVR has been expanded.Following international trends,the number of TAVR in China has steadily increased with each passing year.In 2023,the long-term follow-up results of TAVR in low-risk AS patients further confirm the long-term benefits of TAVR.The relevant research on TAVR for patients with aortic regurgitation and patients with bicuspid aortic stenosis provide evidence support for the expansion of TAVR indications.At the same time,the development of valve devices and new technological innovations are emerging in an endless stream,and the new concept of full life cycle management is increasingly being valued.Especially in China,the development of local devices is progressing rapidly,and multiple devices have entered the clinical research stage.The clinical manifestations and research results are worth pursuing.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.