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.

Background::Although overnight fasting is recommended prior to endoscopic retrograde cholangiopancreatography (ERCP), the benefits and safety of high-carbohydrate fluid diet (CFD) intake 2 h before ERCP remain unclear. This study aimed to analyze whether high-CFD intake 2 h before ERCP can be safe and accelerate patients’ recovery.Methods::This prospective, multicenter, randomized controlled trial involved 15 tertiary ERCP centers. A total of 1330 patients were randomized into CFD group ( n = 665) and fasting group ( n = 665). The CFD group received 400 mL of maltodextrin orally 2 h before ERCP, while the control group abstained from food/water overnight (>6 h) before ERCP. All ERCP procedures were performed using deep sedation with intravenous propofol. The investigators were blinded but not the patients. The primary outcomes included postoperative fatigue and abdominal pain score, and the secondary outcomes included complications and changes in metabolic indicators. The outcomes were analyzed according to a modified intention-to-treat principle. Results::The post-ERCP fatigue scores were significantly lower at 4 h (4.1 ± 2.6 vs. 4.8 ± 2.8, t = 4.23, P <0.001) and 20 h (2.4 ± 2.1 vs. 3.4 ± 2.4, t= 7.94, P <0.001) in the CFD group, with least-squares mean differences of 0.48 (95% confidence interval [CI]: 0.26–0.71, P <0.001) and 0.76 (95% CI: 0.57–0.95, P <0.001), respectively. The 4-h pain scores (2.1 ± 1.7 vs. 2.2 ± 1.7, t = 2.60, P = 0.009, with a least-squares mean difference of 0.21 [95% CI: 0.05–0.37]) and positive urine ketone levels (7.7% [39/509] vs. 15.4% [82/533], χ2 = 15.13, P <0.001) were lower in the CFD group. The CFD group had significantly less cholangitis (2.1% [13/634] vs. 4.0% [26/658], χ2 = 3.99, P = 0.046) but not pancreatitis (5.5% [35/634] vs. 6.5% [43/658], χ2 = 0.59, P = 0.444). Subgroup analysis revealed that CFD reduced the incidence of complications in patients with native papilla (odds ratio [OR]: 0.61, 95% CI: 0.39–0.95, P = 0.028) in the multivariable models. Conclusion::Ingesting 400 mL of CFD 2 h before ERCP is safe, with a reduction in post-ERCP fatigue, abdominal pain, and cholangitis during recovery.Trail Registration::ClinicalTrials.gov, No. NCT03075280.

Objective To explore clinical effect of modified Chinese-way technique under shoulder arthroscopy in treating massive rotator cuff tears.Methods From January 2019 to June 2022,22 patients with massive rotator cuff tears who underwent arthroscopic rotator cuff repair with improved Chinese-way technique,including 10 males and 12 females,aged from 46 to 76 years old with an average of(64.14±7.45)years old;the courses of disease ranged from 5 to 14 months with an average of(8.32±2.42)months;19 patients were complete repaired,and 3 patients were partial repaired.Visual analogue scale(VAS)and University of California at Los Angeles(UCLA)scale were used to evaluate pain and function of shoulder joint preopera-tively and 1 year postoperatively.Postoperative complications,the integrity of reconstructed tissue structure and the size of sub-acromial space were observed.Results All patients were followed up from 12 to 34 months with an average of(17.14±5.93)months.Re-tear were occurred in 4 patients during MRI follow-up,but clinical symptoms of patients were improved significant-ly and they were satisfied with the treatment,the others were no complications such as incision infection,peripheral nerve in-jury,loosening and falling off of internal fixation anchors.Preoperative and 1 year after operation VAS were(8.05±1.12)and(1.82±1.50),UCLA scores were(7.45±1.65)and(31.41±2.87)respectively,and the difference was statistically significant(P＜0.05).Conclusion The modified Chinese-way technique under shoulder arthroscopy for the massive rotator cuff tear could relieve pain obviously and recovery postoperative function well,with satisfactory curative effect.

Diabetes mellitus is a metabolic disease with high incidence and many complications,among which type 2 diabetes mellitus(T2DM)accounts for a large proportion.Current studies have shown that T2DM is accompanied by damage of liver,kidney,and other organs and its complications seriously en-danger human health.Ferroptosis generates many Reactive Oxygen Species(ROS)through the Fenton reaction,and the accumulation of ROS activates Hypoxia Inducible Factor-1(HIF-1α).As a result,the level of vascular endothelial growth factor(VEGF)is increased.Ferrostatin-1(Fer-1),a ferroptosis in-hibitor,has strong antioxidant capacity.Therefore,based on the hypoxia-inducible factor-1α/vascular endothelial growth factor(HIF-1α/VEGF)signaling pathway,we explored the therapeutic effect of Fer-1 on the liver and kidney tissues of diabetic mice.db/db mice(21～22 weeks old)were used as the model of diabetes mellitus.Ferroptosis inhibitor Fer-1 was used as the intervention drug.db/m mice served as the blank control group,and body weight and blood glucose were measured for 4 weeks.Food intake and water intake were recorded in each group.The levels of Alanine aminotransferase(ALT)and Aspartate aminotransferase(AST)in the serum were measured.ROS and Glutathione(GSH)activity in liver and kidney tissues and urinary protein content were measured.Liver and kidney tissue sections were stained with Hematoxylin-Eosin(HE),and the pathological morphology was observed under a light microscope.The protein levels of HIF-1α,VEGF,and glutathione peroxidase 4(GPX4)in liver and kidney tissues were detected by Western blot.In db/db mice,Fer-1(1 mg·kg-1,ig)could significantly reduce the a-mount of food and water intake,the levels of ALT and AST in serum,the ROS production in liver and kidney tissues,and the level of urine protein,but significantly increase the activity of GSH,thus improve the pathological conditions of liver and kidney.Fer-1 also significantly inhibited HIF-1α and VEGF pro-tein indexes and increased GPX4 protein levels in liver and kidney tissues.Although Fer-1 can not change the body weight and reduce blood glucose in diabetic mice,it can play a therapeutic role in the liver and kidney tissues of diabetic mice in the middle and late stages,and its mechanism may be related to HIF-1α/VEGF and GPX4.

Long non-coding RNA(lncRNA)PART1,a competing endogenous RNA(ceRNA),plays a crucial role in the occurrence and development of various tumors.However,research on PART1 in laryngeal squamous cell carcinoma(LSCC)remains scarce.Based on preliminary lncRNA sequencing data,we found that PART1 was sig-nificantly downregulated in LSCC tissues.Further analysis of sequencing and clinical data from public databases such as TCGA revealed 146 differentially expressed lncRNAs(95 upregulated and 51 downregulated)and 2 424 differentially expressed mRNAs when comparing LSCC tumor and adjacent tissues.The results showed that PART1 was generally downregulated in LSCC(P＜0.0001),and patients with high PART1 expression had significantly better prognosis(P＜0.05).We used bioinformatics methods to construct the ceRNA regulatory network of PART1 in LSCC and identified the miRNAs and mRNAs interacting with it.Under laboratory conditions,the im-portance of PART1 in LSCC cells was validated in vitro.Overexpression vectors significantly increased the expres-sion of PART1 in LSCC cells(P＜0.001).Experiments including 5-ethynyl-2'-deoxyuridine staining,apoptosis analysis,scratch healing assay,Transwell assay,and phalloidin staining showed that overexpression of PART1 sig-nificantly affected the proliferation,apoptosis,migration,and invasion of LSCC cells in vitro(P＜0.001).There-fore,PART1 may suppress the occurrence and development of LSCC.This study provides a theoretical basis for e-lucidating the role of PART1 in LSCC.

Objective:To analyze the efficiency of human resource allocation and its spatial distribution characteristics of district-level Center for Disease Control and Prevention(CDC)in city N of Jiangsu Province in 2020,in order to provide a strong decision-making reference for optimizing and strengthening the CDC talent team.Methods:The efficiency of human resources of district-level CDC of City N in2020 was measured using the Super-Efficiency SBM model,and the spatial association pattern was analyzed using the natural break point classification method and Moran's index,with the visualization presented through LISA maps.Results:The overall level of human resource efficiency in district-level CDC of City N is relatively high.However,spatially,there are significant differences among the regions,showing a trend of high efficiency in the central areas and low efficiency at the ends.Moran's index and LISA maps indicate a negative spatial correlation in efficiency,with a low-high(L-H)cluster centered on Area L and a high-low(H-L)cluster centered on Area J.The high-high(H-H)cluster pattern has not yet formed,exhibiting a characteristic of interspersed high and low efficiency.Conclusions:There are regional differences in the human resource efficiency of the Disease Control Center in City N,and the spatial cluster pattern needs to be optimized.It is recommended to focus on efficiency improvement in Areas P and L,formulate appropriate policies,and promote coordinated regional development.