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:Multiple observational studies have suggested a potential association between telomere length and musculoskeletal diseases.However,the underlying mechanisms remain unclear. OBJECTIVE:To investigate the genetic causal relationship between telomere length and musculoskeletal diseases using two-sample Mendelian randomization analysis. METHODS:Genome-wide association study summary data of telomere length were obtained from the UK Biobank.Genome-wide association study summary data of 10 common musculoskeletal diseases(osteonecrosis,osteomyelitis,osteoporosis,rheumatoid arthritis,low back pain,spinal stenosis,gout,scapulohumeral periarthritis,ankylosing spondylitis and deep venous thrombosis of lower limbs)were obtained from the FinnGen consortium.Inverse variance weighting,Mendelian randomization-Egger and weighted median methods were used to evaluate the causal relationship between telomere length and 10 musculoskeletal diseases.Inverse variance weighting was the primary Mendelian randomization analysis method,and sensitivity analysis was performed to explore the robustness of the results. RESULTS AND CONCLUSION:(1)Inverse variance-weighted results indicated a negative causal relationship between genetically predicted telomere length and rheumatoid arthritis(odds ratio=0.78,95%confidence interval:0.64-0.95,P=0.015)and osteonecrosis(odds ratio=0.56,95%confidence interval:0.36-0.90,P=0.016).No causal relationship was found between telomere length and the other eight musculoskeletal diseases(all P>0.05).(2)Sensitivity analysis affirmed the robustness of these causal relationships,and Mendelian randomization-Egger intercept analysis found no evidence of potential horizontal pleiotropy(all P>0.05).(3)This Mendelian randomized study supports that telomere length has protective effects against rheumatoid arthritis and osteonecrosis.However,more basic and clinical research will be needed to support our findings in the future.

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::Pulmonary arterial hypertension (PAH) is characterized by excessive proliferation of small pulmonary arterial vascular smooth muscle cells (PASMCs), endothelial dysfunction, and extracellular matrix remodeling. G protein-coupled receptor kinase 2 (GRK2) plays an important role in the maintenance of vascular tone and blood flow. However, the role of GRK2 in the pathogenesis of PAH is unknown.Methods::GRK2 levels were detected in lung tissues from healthy people and PAH patients. C57BL/6 mice, vascular smooth muscle cell-specific Grk2-knockout mice ( Grk2?SM22), and littermate controls ( Grk2flox/flox) were grouped into control and hypoxia mice ( n = 8). Pulmonary hypertension (PH) was induced by exposure to chronic hypoxia (10%) combined with injection of the SU5416 (cHx/SU). The expression levels of GRK2 and Yes-associated protein (YAP) in pulmonary arteries and PASMCs were detected by Western blotting and immunofluorescence staining. The mRNA expression levels of Grk2 and Yes-associated protein ( YAP) in PASMCs were quantified with real-time polymerase chain reaction (RT-PCR). Wound-healing assay, 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide (MTT) assay, and 5-Ethynyl-2′-deoxyuridine (EdU) staining were performed to evaluate the proliferation and migration of PASMCs. Meanwhile, the interaction among proteins was detected by immunoprecipitation assays. Results::The expression levels of GRK2 were upregulated in the pulmonary arteries of patients with PAH and the lungs of PH mice. Moreover, cHx/SU-induced PH was attenuated in Grk2?SM22 mice compared with littermate controls. The amelioration of PH in Grk2?SM22 mice was accompanied by reduced pulmonary vascular remodeling. In vitro study further confirmed that GRK2 knock-down significantly altered hypoxia-induced PASMCs proliferation and migration, whereas this effect was severely intensified by overexpression of GRK2. We also identified that GRK2 promoted YAP expression and nuclear translocation in PASMCs, resulting in excessive PASMCs proliferation and migration. Furthermore, GRK2 is stabilized by inhibiting phosphorylating GRK2 on Tyr86 and subsequently activating ubiquitylation under hypoxic conditions. Conclusion::Our findings suggest that GRK2 plays a critical role in the pathogenesis of PAH, via regulating YAP expression and nuclear translocation. Therefore, GRK2 serves as a novel therapeutic target for PAH treatment.

Aim To investigate the effect of vanillin on the regulation of cyclic guanylate adenylate synthetase(cGAS)/stimulator of interferon gene(STING)signa-ling pathway on hepatic tissue injury in rats with intra-hepatic cholestasis(IC).Methods SD rats were randomly divided into normal group,IC group,vanillin group,cGAS overexpression group,and vanillin+cGAS overexpression group,with continuous adminis-tration for seven days.The body weight,liver weight and liver to body weight ratio of rats were measured.Liver function(ALT,AST,ALP,LDH),IC(TBIL,TBA)and liver fibrosis(HA,LN,PC Ⅲ)index were determined by ELISA.Liver pathology and fibrosis were observed using HE and Masson staining,and col-lagen volume fraction was calculated.The expression of cGAS/STING pathway related proteins in liver tissue was detected by Western blot.Results Vanillin could improve liver pathology and fibrosis,increase body weight,and decrease liver weight,ALT,AST,ALP,LDH,TBIL,TBA,HA,LN,PC Ⅲ,collagen volume fraction,cGAS,STING protein in IC rats(P＜0.05).Overexpression of cGAS could reverse the effects of vanillin on the above indicators in IC rats(P＜0.05).Conclusions Vanillin may improve liver function,IC,liver fibrosis,and liver tissue damage in IC rats by downregulating the cGAS/STING signaling pathway.

Objective:To evaluate the effects of different exercises on patients with chronic kidney disease based on network Meta-analysis so to provide a basis for medical and nursing staff to develop exercise programs.Methods:Randomized controlled trials on the effects of different exercises on patients with chronic kidney disease were electronically searched in databases such as Web of Science, PubMed, Embase, Cochrane Library, China Biology Medicine disc, WanFang Data, and China National Knowledge Infrastructure. The search period was from January 1, 2000, to March 1, 2023. Two researchers conducted literature screening, data extraction, and literature quality evaluation independently. The data was analyzed by using R software in conjunction with the gemtc package and plotted by using Stata software.Results:A total of 52 articles were included, with a cumulative sample size of 3 127 cases, involving three types of exercise (aerobic, resistance, and combined). The network Meta-analysis showed statistically significant differences between the three exercise methods and routine nursing in delaying renal function progression and improving cardiovascular endurance ( P<0.05). In controlling blood pressure and improving walking ability, the effects of three exercise methods were all superior to routine nursing, and the differences were statistically significant ( P<0.05). The area under the cumulative ranking probability curve showed that aerobic exercise>resistance exercise>combined exercise in terms of delaying renal function progression. In controlling blood pressure, resistance exercise>aerobic exercise>combined exercise. In terms of improving cardiovascular endurance, aerobic exercise>resistance exercise>combined exercise; In terms of improving walking ability, resistance exercise>combined exercise>aerobic exercise. Subgroup analysis showed that ≥ 24 weeks was the optimal intervention period for aerobic exercise. Resistance exercise intervention for ≥ 12 weeks could lower blood pressure, and intervention for ≤12 weeks could improve walking ability. However, when the intervention time was longer than 24 weeks, combined exercise had an excellent effect. Conclusions:Aerobic exercise is an excellent way to delay renal function progression and improve cardiovascular endurance. Resistance exercise is the optimal exercise method for controlling blood pressure and improving walking ability. Medical and nursing staff should choose appropriate types of exercise based on the patient's medical needs.

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.