[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.

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: Our understanding of the correlation between postdischarge cancer and mortality in patients with coronary artery disease (CAD) remains incomplete. The aim of this study was to investigate the relationships between postdischarge cancers and all-cause mortality and cardiovascular mortality in CAD patients. METHODS: In this retrospective cohort study, 25% of CAD patients without prior cancer history who underwent coronary artery angiography between January 1, 2011 and December 31, 2015, were randomly enrolled using SPSS 26.0. Patients were monitored for the incidence of postdischarge cancer, which was defined as cancer diagnosed after the index hospitalization, survival status and cause of death. Cox regression analysis was used to explore the association between postdischarge cancer and all-cause mortality and cardiovascular mortality in CAD patients. RESULTS: A total of 4085 patients were included in the final analysis. During a median follow-up period of 8 years, 174 patients (4.3%) developed postdischarge cancer, and 343 patients (8.4%) died. A total of 173 patients died from cardiovascular diseases. Postdischarge cancer was associated with increased all-cause mortality risk (HR = 2.653, 95% CI: 1.727-4.076, P < 0.001) and cardiovascular mortality risk (HR = 2.756, 95% CI: 1.470-5.167, P = 0.002). Postdischarge lung cancer (HR = 5.497, 95% CI: 2.922-10.343, P < 0.001) and gastrointestinal cancer (HR = 1.984, 95% CI: 1.049-3.750, P = 0.035) were associated with all-cause mortality in CAD patients. Postdischarge lung cancer was significantly associated with cardiovascular death in CAD patients (HR = 4.979, 95% CI: 2.114-11.728, P < 0.001), and cardiovascular death was not significantly correlated with gastrointestinal cancer or other types of cancer. CONCLUSIONS Postdischarge cancer was associated with all-cause mortality and cardiovascular mortality in CAD patients. Compared with other cancers, postdischarge lung cancer had a more significant effect on all-cause mortality and cardiovascular mortality in CAD patients.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Objective To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database,to explore research hotspots and developmen-tal trends.Methods A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the litera-ture measuring tool CiteSpace.The authors,institution,country(region),title,journal,keywords,cited references and other information of relevant literatures were analyzed.Results A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries(regions)were identi-fied,with the number of articles published showing an increasing trend year by year.Among them,the United States had the highest number of publications and China ranked the second.Academy of Forensic Science had the highest number of publications among the institutions.Forensic Science Inter-national,Journal of Forensic Sciences,International Journal of Legal Medicine ranked high in publica-tion and citation frequency.Through the analysis of keywords,it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technol-ogy for sex and age estimation,cause of death analysis,postmortem interval estimation,individual identification and so on.Conclusion It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research.Exploring the combination of advanced ar-tificial intelligence technologies with forensic research will be a hotspot and direction for future re-search.

Objective To observe the effect of esketamine on postoperative recovery in children after endoscopic adenoidectomy.Methods Sixty pediatric patients who underwent adenoidectomy with endoscope from Jan 2022 to Jan 2023 in Eye&ENT Hospital,Fudan University were enrolled.The pediatric patients were randomly divided into hydro-morphine group(n=30)and esketamine group(n=30).Anesthesia induction:lidocaine 1.5 mg/kg,propofol 2.5 mg/kg and remifentanil 4 μg/kg were injected intravenously,and then the endotracheal tube was used for airway management.Anesthesia maintenance:remifentanil infusion was at 0.2-0.5 μg·kg-1·min-1 and the end tidal concentration of sevoflurane was at 0.7-1.0 minimum alveolar concentration(MAC).At the end of surgery,either hydromorphone 0.01 mg/kg or esketamine 0.5 mg/kg were administered for postoperative pain control.Time to resume spontaneous breathing was recorded.Other parameters included respiratory rate per minute,duration of stay in the post-anesthesia care unit,hemodynamic profiles.The adverse events including agitation and desaturation were also of note.Results Children in esketamine group resumed spontaneous breathing faster(P=0.048),had faster respiratory rate when recovery of spontaneous breathing(P=0.001)and lower concentration of end tidal CO2(P=0.005).The findings suggested that esketamine did not impair respiratory function.Compared to hydro-morphine group,children in esketamine group had shorter stay in the post-anesthesia care unit with statistical difference(P=0.020).Esketamine had no effect on heart rate and blood pressure,so there were less adverse events.Conclusion Compared with 0.01 mg/kg hydro-morphine,0.5 mg/kg esketamine does not impair respiratory function and it facilitate fast recovery in children undergoing endoscopic adenoidectomy after general anesthesia.