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.

BACKGROUND:Carboxylesterase 1 and inflammatory factors play a crucial role in regulating lipid metabolism and glucose homeostasis.However,the effects of different exercise intensity interventions on carboxylesterase 1 and inflammatory factors in skeletal muscle of type 2 diabetic rats remain to be revealed. OBJECTIVE:To investigate the effects of different exercise intensity interventions on carboxylesterase 1 and inflammatory factors in skeletal muscle of type 2 diabetic rats. METHODS:Thirty-two 8-week-old male Sprague-Dawley rats were randomly divided into normal control group(n=12)and modeling group(n=20)after 1 week of adaptive feeding.Rat models of type 2 diabetes mellitus were prepared by high-fat diet and single injection of streptozotocin.After successful modeling,the rats were randomly divided into diabetic control group(n=6),moderate-intensity exercise group(n=6)and high-intensity intermittent exercise group(n=6).The latter two groups were subjected to treadmill training at corresponding intensities,once a day,50 minutes each,and 5 days per week.Exercise intervention in each group was carried out for 6 weeks.After the intervention,ELISA was used to detect blood glucose and blood lipids of rats.The morphological changes of skeletal muscle were observed by hematoxylin-eosin staining.The mRNA expression levels of carboxylesterase 1 and inflammatory cytokines were detected by real-time quantitative PCR.The protein expression levels of carboxylesterase 1 and inflammatory cytokines were detected by western blot and immunofluorescence. RESULTS AND CONCLUSION:Compared with the normal control group,fasting blood glucose,triglyceride,low-density lipoprotein cholesterol,insulin resistance index in the diabetic control group were significantly increased(P<0.01),insulin activity was decreased(P<0.05),and the mRNA and protein levels of carboxylesterase 1,never in mitosis gene A related kinase 7(NEK7)and interleukin 18 in skeletal muscle tissue were upregulated(P<0.05).Compared with the diabetic control group,fasting blood glucose,triglyceride,low-density lipoprotein cholesterol,and insulin resistance index in the moderate-intensity exercise group and high-intensity intermittent exercise group were down-regulated(P<0.05),and insulin activity was increased(P<0.05).Moreover,compared with the diabetic control group,the mRNA level of NEK7 and the protein levels of carboxylesterase 1,NEK7 and interleukin 18 in skeletal muscle were decreased in the moderate-intensity exercise group(P<0.05),while the mRNA levels of carboxylesterase 1,NEK7,NOD-like receptor heat protein domain associated protein 3 and interleukin 18 and the protein levels of carboxylesterase 1 and interleukin 18 in skeletal muscle were downregulated in the high-intensity intermittent exercise group(P<0.05).Hematoxylin-eosin staining showed that compared with the diabetic control group,the cavities of myofibers in the moderate-intensity exercise group became smaller,the number of internal cavities was reduced,and the cellular structure tended to be more intact;the myocytes of rats in the high-intensity intermittent exercise group were loosely arranged,with irregular tissue shape and increased cavities in myofibers.To conclude,both moderate-intensity exercise and high-intensity intermittent exercise can reduce blood glucose,lipid,insulin resistance and carboxylesterase 1 levels in type 2 diabetic rats.Moderate-intensity exercise can significantly reduce the expression level of NEK7 protein in skeletal muscle,while high-intensity intermittent exercise can significantly reduce the expression level of interleukin 18 protein in skeletal muscle.In addition,the level of carboxylesterase 1 is closely related to the levels of NEK7 and interleukin 18.

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.

ObjectiveThe U6 promoter is an essential element for driving sgRNA expression in the clustered regularly interspaced short palindromic repeat sequences/CRISPR-associated protein 9（CRISPR/Cas9）gene editing system in dicotyledonous plants. Endogenous U6 promoters typically exhibit higher transcriptional activity， which can significantly improve gene editing efficiency. This study aims to identify endogenous U6 promoters in Artemisia annua to optimize its CRISPR/Cas9 gene editing system， which holds significant importance for its molecular breeding. MethodsOn the basis of the highly conserved U6 snRNA sequences in Arabidopsis thaliana， endogenous U6 promoters were screened in the A. annua genome. Expression vectors were constructed with candidate AaU6 promoter driving the firefly luciferase （LUC） reporter gene， and then transiently transformed into Nicotiana benthamiana. Transcriptional activities of the promoters were measured and compared by in vivo imaging and the Dual Luciferase Reporter assay. ResultsEight endogenous U6 promoters were successfully cloned from A. annua. Sequences alignment revealed that all these promoters contained the two conserved cis-acting elements， upstream sequence element （USE） and TATA-box， which affected their transcriptional activity. Dual-luciferase activity assays indicated that the transcriptional activities of AaU6-3， AaU6-1， and AaU6-5 were significantly higher than that of the Arabidopsis AtU6-26 promoter， with AaU6-3 exhibiting the highest activity. ConclusionThis study identified three endogenous AaU6 promoters with high transcriptional activity in A. annua， providing key functional elements for establishing an efficient gene editing system in A. annua. These findings will contribute to advancing precision molecular breeding and high-quality germplasm innovation in A. annua.

Objective To investigate the effect of varying ureteral wall thickness(UWT)at the site of ureteral stones on the clinical efficacy of ureteroscopic lithotripsy(URL).Methods The clinical data of 164 patients with ureteral stones in our hospital were retrospectively analyzed.According to different UWT,the patients were divided into the mild thickening group(84 cases,UWT<3.16 mm),the moderate thickening group(31 cases,UWT 3.16 to 3.49 mm),and the severe thickening group(49 cases,UWT>3.49 mm),and the differences of clinical related indicators among the three groups were compared.Results The incidence of postoperative renal colic and leukocyte disorder in the mild thickening group and the moderate thickening group were lower than those in the severe thickening group,and the differences were statistically significant(P<0.05).The postoperative catheterization time in the mild thickening group and the moderate thickening group were shorter than that in the severe thickening group,and the incidences of secondary lithotripsy,residual stones and stone return to kidney in the mild thickening group and the moderate thickening group were lower than those in the severe thickening group,with statistically significant differences(P<0.05).The length of hospital stay and hospitalization cost in the mild thickening group and the moderate thickening group were shorter/less than those in the severe thickening group,with statistically significant differences(P<0.05).Conclusion With the increase of UWT(especially when UWT>3.49 mm),the incidence of postoperative complications and hospitalization cost of URL increase to varying degrees,and the surgical efficacy decreases.In clinical work,UWT measurement holds potential value in predicting the surgical efficacy and complications of URL.

Objective:To explore the effect of Annexin A1(ANXA1)peptidomimetic Ac2-26 on acute kidney injury(AKI)and neutrophil apoptosis in septic rats.Methods:Experimental groups included control group,Ac2-26 group,AKI group,AKI+Ac2-26 group,with 15 rats in each group.After cecal ligation and perforation were used to establish a sepsis-induced AKI model,Ac2-26 was intravenously infused for treatment,once a day for 14 days;after the end,ELISA was used to detect levels of serum creatinine(Scr),urea nitrogen(BUN),IL-1β,IL-6 and TNF-α;HE staining and periodic acid Schiff(PAS)staining were used to observe the pathological changes of rat kidney tissues in each group;immunohistochemical staining was used to detect expression of ANXA1 in kidney tissue of each group of rats;neutrophils were isolated from rat peripheral blood,Giemsa staining and trypan blue staining were used to detect cell purity and viability;Annexin V-FITC/PI double staining method and TUNEL staining were used to determine apop-tosis level of neutrophils in each group.Results:Compared with control group,levels of Scr and BUN in serum of rats in AKI group were increased(P<0.05),levels of IL-1β,IL-6 and TNF-α also increased(P<0.05),renal tubules and glomeruli in kidney tissue were both significantly damaged,accompanied by a large number of inflammatory cell infiltration,and pathological score increased(P<0.05),while proportion of ANXA1 positive staining area was decreased(P<0.05);neutrophils identified by Giemsa staining and trypan blue staining had complete morphology and high activity;compared with control group,apoptosis rate of neutrophils in AKI group was decreased(P<0.05),and the positive rate of TUNEL was decreased(P<0.05).Compared with AKI group,levels of Scr and BUN in serum of rats in AKI+Ac2-26 group were decreased(P<0.05),levels of IL-1β,IL-6 and TNF-α also decreased(P<0.05),pathological manifestations of renal tubules and glomeruli in renal tissue were significantly reduced,and pathological score was reduced(P<0.05),while the proportion of ANXA1 positive staining area was increased(P<0.05),at the same time,apoptosis rate of rat neu-trophils was increased(P<0.05),positive rate of TUNEL was also increased(P<0.05).Conclusion:ANXA1 peptidomimetic Ac2-26 can increase expression of ANXA1 in kidney tissue of AKI in septic rats,promote neutrophil apoptosis,and have a protective effect on kidney tissue damage in rats caused by sepsis.

Objective:To investigate the clinical value immature granulocyte percentage(IG％)and other inflammatory indicators in the severity of acute appendicitis.Methods:A total of 201 pediatric patients undergoing appendicitis surgery admitted to Zhoukou Central Hospital from June 2022 to August 2023 were included.Patients with pathologically confirmed actue appendici-tis were divided two subgroups:actue simple appendicitis(ASA)group and actue complicated ap-pendicitis(ACA)group,The variables that included IG％,white blood cell(WBC)count,absolute neutrophil count(ANC),absolute lymphocyte count(ALC),neutrophil to lymphocyte ratio(NLR),pro-calcitonin(PCT),C-reactive protein(CRP),platelet to lymphocyte(PLR)and other indexes were ana-lyzed between ASA and ACA group.The logistic regression model for diagnosis of ACA was es-tablished,and the diagostic value of this model and other inflammtory indicators for ACA was evaluated by receiver operating characteristic(ROC)curve analysis.Results:The levels of IG％,WBC,ANC,ALC,NLR,PCT and PLR were higher and the level of ALC was lower in ACA group than those in ASA group(all P＜0.05).Logistic regression analysis showed that IG％,NLR and CRP were three diagnostic determinants of ACA(all P＜0.05).The AUC of the established logistic model and IG％,NLR,CRP were 0.868,0.821,0.691 and 0.790(all P＜0.001).The logistic model was vali-dated by independent cohorts,and the AUC was 0.872,the sensitivity was 90.0％and the speci-ficity was 75.6％.Conlusions:The IG％value can early indicator for pediatric ACA,and the es-tablished logistic regression model based on biomarkers including IG％,NLR and CRP has clinical value in diagnosing ACA in children.