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.

Aim To anticipate the mechanism of zuka- mu granules (ZKMG) in the treatment of bronchial asthma, and to confirm the projected outcomes through in vivo tests via using network pharmacology and molecular docking technology. Methods The database was examined for ZKMG targets, active substances, and prospective targets for bronchial asthma. The protein protein interaction network diagram (PPI) and the medication component target network were created using ZKMG and the intersection targets of bronchial asthma. The Kyoto Encyclopedia of Genes and Genomics (KEGG) and gene ontology (GO) were used for enrichment analysis, and network pharmacology findings were used for molecular docking, ovalbumin (OVA) intraperitoneal injection was used to create a bronchial asthma model, and in vivo tests were used to confirm how ZKMG affected bronchial asthma. Results There were 176 key targets for ZKMG's treatment of bronchial asthma, most of which involved biological processes like signal transduction, negative regulation of apoptotic processes, and angiogenesis. ZKMG contained 194 potentially active components, including quercetin, kaempferol, luteolin, and other important components. Via signaling pathways such TNF, vascular endothelial growth factor A (VEGFA), cancer pathway, and MAPK, they had therapeutic effects on bronchial asthma. Conclusion Key components had strong binding activity with appropriate targets, according to molecular docking data. In vivo tests showed that ZKMG could reduce p-p38, p-ERKl/2, and p-I

Objective To investigate the effect of smoking on the semen quality in infertile men.Methods A total of 360 male infertility patients were enrolled and divided into the smoking group(n=190)and non-smoking group(n=170)based on whether they smoked or not.Furthermore,the smoking group was subdivided into group A(≤10 sticks/d,n=63),group B(11～20 sticks/d,n=80),and group C(>20 sticks/d group,n=47)according to the amount of smoking.Semen volume,liquefaction time,sperm concentration,motility,DNA fragmentation rate and normal morphological rate were observed and compared between and within the groups.Results There were significant differences in semen volume,liquefaction time,sperm motility,normal morphological rate and DNA fragmentation rate between the smoking group and the non-smoking group(P<0.05).The semen volume,sperm motility and normal morphological rate of the smoking group were lower than those in the non-smoking group,and the DNA fragmentation rate and semen liquefaction time were higher than those in the non-smoking group.And with the increase of smoking volume,sperm motility and normal morphological rate decreased,semen liquefaction time and DNA fragmentation rate increased,and there was no significant difference in the sperm concentration between the smoking group and non-smoking group(P>0.05).There was no significant difference in the semen volume between the three groups with different smoking amounts(P>0.05).Conclusion Smoking has a negative impact on the sperm quality parameters such as semen volume,sperm motility,normal morphological rate,sperm motility,liquefaction time and DNA fragmentation,and the effect of heavy smoking is particularly obvious.We should strengthen the comprehensive health education,promote the healthy lifestyles and reduce smoking.

Objective To investigate changes in coagulation function and inflammation levels during sepsis.Methods A rat model of sepsis was established using the multiple infection sepsis model(MIM)based on cecal ligation and puncture.Forty-eight male Sprague-Dawley rats were assigned randomly to the following groups:control group,sham group,4 h sepsis group,8 h sepsis group,12 h sepsis group,and 16 h sepsis group(n=8 per group).Inflammatory markers and coagulation-related indicators were measured by enzyme-linked immunosorbent assay and coagulation analysis.Results(1)Lipopolysaccharide(LPS)and interleukin-6(IL-6)levels were significantly higher in the model rats at all time points compared with the sham group(P＜0.001).LPS and IL-6 levels increased gradually with disease progression,with no further changes in LPS after 12 hours.(2)Prothrombin time(PT)was significantly prolonged in the middle and late stages of the sepsis model,starting from 8,compared with the sham group(P＜0.01).(3)Partially activated prothrombin time(APTT)time was significantly prolonged in the 8,12,and 16 h groups compared with the sham group(P＜0.05,P＜0.01).APTT gradually lengthened from 8 h,but approached control levels thereafter.(4)Fibrinogen(Fbg)content was significantly higher in all sepsis groups,except for the 8 h group,compared with the sham group(P＜0.01).(5)Fibrin degradation products(FDP)differed significantly between the control and sham groups(P＜0.01),but not between the sham and sepsis groups.(6)Antithrombin-Ⅲ(AT-Ⅲ)levels decreased significantly throughout each stage of sepsis progression compared with the sham group(P＜0.01),and AT-Ⅲ showed a downward trend with the course of disease,with significant differences among the 4,8,and 16 h groups.Conclusions The MIM rat model can reflect the development of inflammatory and blood coagulation disorders and their relationship during the course of sepsis,and may thus provide a good foundation for further research into the disease course of sepsis.

Objective:To investigate the relationship between intramuscular adipose tissue index (IATI) calculated from computed tomography images at transverse process of the first lumbar and all-cause mortality in maintenance dialysis patients, and to provide a reference for improving the prognosis in these patients.Methods:It was a multicenter retrospective cohort study. The clinical data of patients who received maintenance hemodialysis or peritoneal dialysis treatment from January 1, 2017 to December 31, 2019 in 4 grade Ⅲ hospitals including Zhongda Hospital Affiliated to Southeast University, Taizhou People's Hospital Affiliated to Nanjing Medical University, Affiliated Hospital of Yangzhou University, and the Third Affiliated Hospital of Soochow University were retrospectively collected. IATI was calculated by low attenuation muscle (LAM) density/skeletal muscle density. The receiver-operating characteristic curve was used to determine the optimal cut-off value of IATI, and the patients were divided into high IATI group and low IATI group according to the optimal cut-off value. The differences of baseline clinical data and measurement parameters of the first lumbar level between the two groups were compared. The follow-up ended on December 23, 2022. The endpoint event was defined as all-cause mortality within 3 years. Kaplan-Meier survival curve and log-rank test were used to analyze the survival rates and the differences between the two groups. Multivariate Cox regression analysis models were used to analyze the association between IATI and the risk of all-cause mortality in maintenance dialysis patients. Multivariate logistic regression analysis model was used to analyze the influencing factors of high IATI.Results:A total of 478 patients were eligibly recruited in this study, with age of (53.55±13.19) years old and 319 (66.7%) males, including 365 (76.4%) hemodialysis patients and 113 (23.6%) peritoneal dialysis patients. There were 376 (78.7%) patients in low IATI (<0.42) group and 102 (21.3%) patients in high IATI (≥0.42) group. The proportion of age ≥ 60 years old ( χ2=24.746, P<0.001), proportion of diabetes mellitus ( χ2=5.570, P=0.018), fasting blood glucose ( t=-2.145, P=0.032), LAM density ( t=-3.735, P<0.001), LAM index ( t=-7.072, P<0.001), and LAM area/skeletal muscle area ratio ( Z=-9.630, P<0.001) in high IATI group were all higher than those in low IATI group, while proportion of males ( χ2=11.116, P<0.001), serum albumin ( Z=2.708, P=0.007) and skeletal muscle density ( t=12.380, P<0.001) were lower than those in low IATI group. Kaplan-Meier survival analysis showed that the 3-years overall survival rate of low IATI group was significantly higher than that in high IATI group (Log-rank χ2=19.188, P<0.001). Multivariate Cox regression analysis showed that IATI<0.42 [<0.42/≥0.42, HR(95% CI): 0.50 (0.31-0.83), P=0.007] was an independent protective factor of all-cause mortality, and age ≥60 years old [ HR (95% CI): 2.61 (1.60-4.23), P<0.001], diabetes mellitus [ HR (95% CI): 1.71 (1.06-2.78), P=0.029] and high blood neutrophil/lymphocyte ratio [ HR (95% CI): 1.04 (1.00-1.07), P=0.049] were the independent risk factors of all-cause mortality in maintenance dialysis patients. Stepwise Cox regression analysis showed that IATI<0.42 was still an independent protective factor of all-cause mortality in maintenance dialysis patients [<0.42/≥0.42, HR (95% CI): 0.45 (0.27-0.76), P=0.003]. Multivariate logistic regression analysis showed that low skeletal muscle density [ OR (95% CI): 0.84 (0.81-0.88), P<0.001] and high serum triglyceride [ OR (95% CI): 1.39 (1.07-1.82), P=0.015] were the independent influencing factors of IATI≥0.42. Conclusion:IATI<0.42 of the first lumbar level is an independent protective factor of all-cause mortality in maintenance dialysis patients. Localized myosteatosis within high-quality skeletal muscle may reduce the risk of all-cause mortality in these patients.

ObjectiveTo observe the effectiveness and safety of the Chinese herbal medicine Mingshi Granules （明视方颗粒） for low myopia in children with heart yang insufficiency. MethodsA multicentre， prospective， double-blind randomised controlled study was conducted， in which 290 children with low myopia from 8 centres were randomly divided into 145 cases in the treatment group and 145 cases in the control group， and the treatment group was given education， dispensing glasses， and Chinese herbal medicine Mingshi Granules， while the control group was given education， dispensing glasses， and granules placebo. Both Mingshi Granules and placebo granules were taken orally， 1 bag each time， twice daily， 4 weeks of oral intake and 2 weeks of rest as 1 course of treatment， a total of 4 courses of treatment （24 weeks）. Equivalent spherical lenses， best naked-eye distance visual acuity， ocular axis， corneal curvature K1， adjustment amplitude， traditional Chinese medicine （TCM） symptom scores， calculate the amount of progression of equivalent spherical lenses， were observed at the 12th and the 24th week of treatment， at the 36th week and 48th week of follow-up， resectively， the control rate of myopia progression was evaluated at the 24th week， and safety indexes were observed before treatment. ResultsThe amount of progression of equivalent spherical lenses was lower in the treatment group than in the control group at the 48-week follow-up （P＜0.05）. The control rate of myopia progression at 24 weeks after treatment in the treatment group was higher （57.60%， 72/125） than that in the control group （44.63%， 54/121） （P＜0.05）. The best naked-eye distance visual acuity at 36-week follow-up in the treatment group was higher than that in the control group （P＜0.05）. Equivalent spherical lenses were significantly lower in both groups at all observation time points compared with pre-treatment （P＜0.05）， and were higher in the treatment group than in the control group at the 48-week follow-up （P＜0.05）. The ocular axes of both groups were significantly higher at each observation time point after treatment and at follow-up compared with before treatment （P＜0.05）. The amount of eye axis growth in the treatment group was lower than that in the control group at 24 weeks after treatment and at the 48-week follow-up （P＜0.05）. Corneal curvature K1 was significantly lower in the treatment group at the 24th week of treatment compared to pre-treatment （P＜0.05）. The magnitude of adjustment in the treatment group was significantly higher at the 36-week follow-up and at the 48-week follow-up than before treatment （P＜0.05）. The scores of white/dark complexion， white coating thin pulse， fatigue and total TCM symptom scores of children in both groups at the 12th， 24th， 36th and 48th weeks of follow-up were significantly lower than those before treatment （P＜0.05）； the scores of blurred vision at the 24th and 36th weeks of follow-up were significantly lower than those before treatment （P＜0.05）； and the scores of blurred vision in the treatment group at the 48th week of follow-up were signi-ficantly lower than those before treatment （P＜0.05）. In the treatment group， the score of fatigue was higher than that of the control group at the 36-week follow-up， and the score of blurred vision was lower than that of the control group at the 48-week follow-up （P＜0.05）. No adverse reactions or obvious abnormalities of the safety indexes were observed of the two groups during the treatment. ConclusionChinese herbal medicine Mingshi Granules showed the effect of controlling the progression of low myopia， improving the best naked eye distance visual acuity， slowing down the growth of the eye axis， improving some of the TCM symptoms， with good safety.