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.

[Objective] To evaluate the efficacy and safety of sacral neuromodulation (SNM) in the treatment of patients with benign prostatic hyperplasia (BPH) complicated with underactive bladder (UAB) who respond poorly to transurethral resection of the prostate (TURP). [Methods] A retrospective analysis was performed on 10 patients with BPH and UAB treated with TURP by the same surgeon in Zhongda Hospital Southeast University during Jan.2018 and Jan.2023.The residual urine volume was not significantly relieved after operation, and the maximum urine flow rate and urine volume per discharge were not significantly improved.All patients underwent phase I SNM, and urinary diaries were recorded before and after surgery to observe the average daily frequency of urination, volume per urination, maximum urine flow rate, and residual urine volume. [Results] The operation time was (97.6±11.2) min.During the postoperative test of 2-4 weeks, if the residual urine volume reduction by more than 50% was deemed as effective, SNM was effective in 6 patients (60.0%). Compared with preoperative results, the daily frequency of urination [(20.2±3.8) times vs. (13.2±3.2) times], volume per urination [(119.2±56.7) mL vs. (246.5±59.2) mL], maximum urine flow rate [(8.7±1.5) mL/s vs. (16.5±2.6) mL/s], and residual urine volume [(222.5±55.0) mL vs. (80.8±16.0) mL] were significantly improved, with statistical significance (P<0.05). There were no complications such as bleeding, infection, fever or pain.The 6 patients who had effective outcomes successfully completed phase II surgery, and the fistula was removed.During the follow-up of 1 year, the curative effect was stable, and there were no complications such as electrode displacement, incision infection, or pain in the irritation sites.The residual urine volume of the other 4 unsuccessful patients did not improve significantly, and the electrodes were removed and the vesicostomy tube was retained. [Conclusion] SNM is safe and effective in the treatment of BPH with UAB patients with poor curative effects after TURP.

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.

Aim To investigate the mechanism of Banxia Baizhu Tianma Decoction(BBTD)in interfer-ing methamphetamine(MA)addiction using network pharmacology.Methods The mechanism of BBTD intervention in MA addiction was analyzed using net-work pharmacology,and MA-dependent SH-SY5Y cell model was further constructed to observe the effects of BBTD on cell model and PI3K-Akt pathway.Results A total of 88 active ingredients and 583 potential tar-gets of BBTD were screened.KEGG analysis showed that BBTD might intervene in MA addiction through PI3K-Akt,cAMP and other pathways.The molecular docking results showed that key active ingredients ex-hibited strong binding ability with core targets of PI3K-Akt pathway.In vitro experiments showed that MA-de-pendent model cells had shorter synapses,tended to be elliptical in morphology,had blurred cell boundaries,showed typical cell damage morphology,and had high intracellular expression of cAMP(P＜0.01)and low expression of 5-HT(P＜0.05).BBTD intervention could counteract the above morphology,cAMP,and 5-HT changes,suggesting that it had therapeutic effects on MA-dependent model cells.Western blot showed that MA modeling elevated the p-PI3K/PI3K(P＜0.05)and p-Akt/Akt(P＜0.01);BBTD inter-vention decreased their relative expression.Conclu-sions Gastrodin and other active ingredients in BBTD have therapeutic effects on MA addiction,and the mechanism may be related to regulation of PI3K-Akt pathway relevant targets.

Objective:To evaluate the safety and efficiency of robotic visualization system(RVS)-assisted microsurgical re-construction of the reproductive tract in male rats and the satisfaction of the surgeons.Methods:We randomly divided 8 adult male SD rats into an experimental and a control group,the former treated by RVS-assisted microsurgical vasoepididymostomy(VE)or vaso-vasostomy(VV),and the latter by VE or VV under the standard operating microscope(SOM).We compared the operation time,me-chanical patency and anastomosis leakage immediately after surgery,and the surgeons'satisfaction between the two groups.Results:No statistically significant difference was observed the operation time between the experimental and the control groups,and no anasto-mosis leakage occurred after VV in either group.The rate of mechanical patency immediately after surgery was 100％in both groups,and that of anastomosis leakage after VE was 16.7％in the experimental group and 14.3％in the control.Compared with the control group,the experimental group achieved dramatically higher scores on visual comfort(3.00±0.76 vs 4.00±0.53,P＜0.05),neck/back comfort(2.75±1.16 vs 4.38±1.06,P＜0.01)and man-machine interaction(3.88±1.55 va 4.88±0.35,P＜0.05).There were no statistically significant differences in the scores on image definition and operating room suitability between the two groups.Conclusion:RVS can be used in microsurgical reconstruction of the reproductive tract in male rats and,with its advantages over SOM in ergonomic design and image definition,has a potential application value in male reproductive system micosurgery.

The research and application progress of medical robots in oral surgery was introduced for soft tissue surgery,hard tissue surgery,passive dental implant,autonomous dental implant and etc.The deficiencies of oral surgical robots were analyzed in terms of high cost,long learning curve,limitations for adaptability,low patient acceptance and privacy and ethical issues.It's pointed out the future development trends of medical robots in the field of oral surgery invovled in multifunctionality,intelligence,telemedicine,cost reduction and popularization.[Chinese Medical Equipment Journal,2024,45(7):86-93]