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 study the mechanism of the amelioration of renal injury in immunoglobulin A nephropathy(IgAN)rats by multi-glycosides of Tripterygium wilfordii(GTW)based on the sphingosine kinase 1(Sphk1)/sphingosine 1-phosphate receptor 2(S1PR2)signalling pathway.Methods An IgAN rat model was established by means of bovine serum albumin gavage+castor oil and carbon tetrachloride subcutaneous injection+lipopolysaccharide tail vein injection.The rats were randomly divided into the model,control and experimental groups,with 9 rats in each group,and 10 normal rats were taken as the blank group.In the control group,6.25 mg·kg-1·d-1 prednisone was given by gavage;in the experimental group,9.375 mg·kg-1·d-1GTW was given by gavage;and in the blank and model groups,0.5 mL·100 g-1·d-1 0.9％NaCl was given by gavage,and the drugs were administered to the rats once a day in each group.At the end of the 15th week,urine samples were collected and blood albumin(ALB),blood urea nitrogen(BUN),24 hour-urine protein quantification(24 h-UTP),and urine erythrocyte counts were determined in each group,and the expression levels of Sphk1/S1PR2 proteins in each group were detected by Western blotting.Results The renal pathological changes in the control and experimental groups were significantly reduced compared with those in the model group by hematoxylin-eosin staining and immunofluorescence.The levels of ALB in the blank,model,control and experimental groups were(32.49±2.23),(22.98±0.51),(26.01±1.33)and(26.53±1.92)g·L-1;the levels of BUN were(6.11±1.71),(13.75±2.96),(6.71±1.35)and(4.77±0.99)mmol·L-1;the levels of 24 h-UTP were(5.72±1.96),(9.12±2.15),(5.78±2.05)and(4.75±1.50)mg·24 h-1;the urine erythrocyte counts were(9.73±2.40),(14.62±2.60),(9.90±1.59)and(9.46±2.94)cell·μL-1;the relative expression levels of Sphk1 protein were 0.85±0.02,1.47±0.02,1.06±0.02 and 1.09±0.02;the relative expression levels of S1PR2 protein were 0.27±0.02,0.88±0.01,0.43±0.02,and 0.42±0.02,respectively.The above indexes in the model group were statistically significant when compared with those of the control group and the experimental group(all P＜0.01).Conclusion GTW may reduce the proliferation of mesangial cells by inhibiting the Sphk1/S1PR2 signalling pathway,thus attenuating kidney injury in IgAN rats.

Objective To analyze the effect of fisetin against venous thrombosis in rats.Methods Seventy SD rats were randomly divided into the following groups:sham-operation group,model group,fisetin 45 mg/kg,15 mg/kg,5 mg/kg groups,and aspirin group(47 mg/kg).The corresponding medication was administered by gavage once a day consecutively(the sham-operation group and the model group were given 0.5％carboxymethyl cellulose sodium solution with 10 mL/kg,respectively)for 7 consecutive days.One hour after the last administration,the rats were anesthetized,the lower part of the intersection of inferior vena cava and left renal vein was ligated with silk thread(no ligation in the sham-operation group),and the abdominal wall was sutured.Two hours later,the abdominal cavity was reopened,the other venous branches 1.5 cm away from the ligation site were closed with the artery clamp,and blood was collected from the abdominal aorta.The anticoagulant ratio of 3.8％sodium citrate∶whole blood was 1∶9.The venous thrombus 1 cm down from the ligation point of the intersection of inferior vena cava and left renal vein was cut and the thrombus was separated.The residual blood was dried with filter paper,weighed and recorded.Plasma was taken after anticoagulant blood centrifugation.The levels of plasma antithrombin-Ⅲ(AT-Ⅲ),protease C(PC),plasminogen(PLG),and plasminogen activator inhibitor(PAI-1)were detected by ELISA kits.Results Compared with the model group,the weight of thrombus in fisetin 45 mg/kg group and aspirin 47 mg/kg group decreased(P＜0.01).The content of AT-Ⅲ in three fisetin groups increased(all P＜0.05).The content of PC in fisetin 45 mg/kg increased(P＜0.05).The content of PLG and PAI-1 in fisetin 45 mg/kg group decreased(both P＜0.05).Conclusion Fisetin has the effect against venous thrombosis in vivo,and the effect is related to the upregulation of AT-Ⅲ and PC and the downregulation of PLG and PAI-1.

OBJECTIVE To observe the effect of Huangjing Jiangya Decoction on blood pressure and sleep in patients with hy-pertension of qi-deficiency type accompanied by insomnia.METHODS 73 patients with hypertension of qi-deficiency type accompa-nied by insomnia who met the inclusion criteria were selected and randomly divided into an observation group of 36 cases and a control group of 37 cases.The control group was treated with amlodipine besylate tablets,and the observation group was given Huangjing Jian-gya Decoction oral treatment on the basis of the control group.Both groups were treated continuously for 8 weeks.The changes in TCM syndrome scores,office blood pressure monitoring(OBPM),home blood pressure monitoring(HBPM),24-hour ambulatory blood pressure monitoring(ABPM),Pittsburgh Sleep Quality Index(PSQI)scores and clinical efficacy of the two groups of patients before and after treatment were observed.RESULTS After treatment,the TCM syndrome scores in the observation group were significantly decreased(P<0.05,P<0.01),which were better than the control group(P<0.01);OBPM and HBMP in both groups were signifi-cantly reduced(P<0.05,P<0.01),the observation group was better than the control group(P<0.05,P<0.01);the ABPM of the observation group was significantly reduced(P<0.01),which was better than the control group(P<0.05,P<0.01);the sleep quali-ty,sleep latency,sleep duration,daytime dysfunction score and PSQI total score of the observation group were significantly decreased(P<0.01),which were better than those in the control group(P<0.05,P<0.01);the clinical efficacy of hypertension and insomnia in the observation group was both better than the control group(P<0.01).CONCLUSION Huangjing Jiangya Decoction combined with amlodipine can improve the symptoms of patients with hypertension of qi-deficiency type accompanied by insomnia,lower blood pressure,improve sleep quality,shorten sleep latency,alleviate daytime dysfunction,and has good clinical efficacy.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.