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/Aims: Anti-reflux mucosal ablation (ARMA) is a promising endoscopic intervention for proton pump inhibitor (PPI)-dependent gastroesophageal reflux disease (GERD). However, the effect of ARMA on esophageal motility remains unclear. Methods: Twenty patients with PPI-dependent GERD receiving ARMA were prospectively enrolled. Comprehensive self-report symptom questionnaires, endoscopy, 24-hour impedance-pH monitoring, and high-resolution impedance manometry were performed and analyzed before and 3 months after ARMA. Results: All ARMA procedures were performed successfully. Symptom scores, including GerdQ (11.16 ± 2.67 to 9.11 ± 2.64, P = 0.026) and reflux symptom index (11.63 ± 5.62 to 6.11 ± 3.86, P = 0.001), improved significantly, while 13 patients (65%) reported discontinuation of PPI. Total acid exposure time (5.84 ± 4.63% to 2.83 ± 3.41%, P = 0.024) and number of reflux episodes (73.05 ± 19.34 to 37.55 ± 22.71, P < 0.001) decreased significantly after ARMA. Improved esophagogastric junction (EGJ) barrier function, including increased lower esophageal sphincter resting pressure (13.89 ± 10.78 mmHg to 21.68 ± 11.5 mmHg, P = 0.034), 4-second integrated relaxation pressure (5.75 ± 6.42 mmHg to 9.99 ± 5.89 mmHg, P = 0.020), and EGJ-contractile integral(16.42 ± 16.93 mmHg · cm to 31.95 ± 21.25 mmHg · cm, P = 0.016), were observed. Esophageal body contractility also increased significantly (distal contractile integral, 966.85 ± 845.84 mmHg · s · cm to 1198.8 ± 811.74 mmHg · s · cm, P = 0.023). Patients with symptom improvement had better pre-AMRA esophageal body contractility. Conclusions ARMA effectively improves symptoms and reflux burden, EGJ barrier function, and esophageal body contractility in patients with PPIdependent GERD during short-term evaluation. Longer follow-up to clarify the sustainability of ARMA is needed.

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.

Knee osteoarthritis(KOA)is a chronic degenerative joint disease,which poses a major challenge particularly among the elderly population due to its high incidence and high disability.Imaging examination has been used commonly to diagnose KOA.However,it faces imitations in predicting disease progression due to the lack of prior information and constraints in manpower and time.With the rapid evolution of big data and computational technologies,artificial intelligence(AI)is progressively integrating into various healthcare domains.Therefore,the integration of artificial intelligence(AI)into healthcare holds promise for revolutionizing KOA diagnosis and treatment.AI-assisted diagnostic models have demonstrated the potential to automate diagnosis,classify disease severity,and predict disease progression with improved efficiency and accuracy.In addition,these models provide personalized diagnosis and treatment options,as well as accurate disease progression risk assessment.Despite these promising outcomes,challenges such as high costs associated with data annotation and limitations in model generalization capabilities persist.This paper reviews recent advancements in AI applications and summarizes the potential value of utilizing AI applications for KOA.To further enhance the utilization of AI in KOA management to overcome current limitations,future efforts should focus on standardizing clinical sample databases,optimizing AI algorithms,and enhancing external verification sets.

Background/Aims: Anti-reflux mucosal ablation (ARMA) is a promising endoscopic intervention for proton pump inhibitor (PPI)-dependent gastroesophageal reflux disease (GERD). However, the effect of ARMA on esophageal motility remains unclear. Methods: Twenty patients with PPI-dependent GERD receiving ARMA were prospectively enrolled. Comprehensive self-report symptom questionnaires, endoscopy, 24-hour impedance-pH monitoring, and high-resolution impedance manometry were performed and analyzed before and 3 months after ARMA. Results: All ARMA procedures were performed successfully. Symptom scores, including GerdQ (11.16 ± 2.67 to 9.11 ± 2.64, P = 0.026) and reflux symptom index (11.63 ± 5.62 to 6.11 ± 3.86, P = 0.001), improved significantly, while 13 patients (65%) reported discontinuation of PPI. Total acid exposure time (5.84 ± 4.63% to 2.83 ± 3.41%, P = 0.024) and number of reflux episodes (73.05 ± 19.34 to 37.55 ± 22.71, P < 0.001) decreased significantly after ARMA. Improved esophagogastric junction (EGJ) barrier function, including increased lower esophageal sphincter resting pressure (13.89 ± 10.78 mmHg to 21.68 ± 11.5 mmHg, P = 0.034), 4-second integrated relaxation pressure (5.75 ± 6.42 mmHg to 9.99 ± 5.89 mmHg, P = 0.020), and EGJ-contractile integral(16.42 ± 16.93 mmHg · cm to 31.95 ± 21.25 mmHg · cm, P = 0.016), were observed. Esophageal body contractility also increased significantly (distal contractile integral, 966.85 ± 845.84 mmHg · s · cm to 1198.8 ± 811.74 mmHg · s · cm, P = 0.023). Patients with symptom improvement had better pre-AMRA esophageal body contractility. Conclusions ARMA effectively improves symptoms and reflux burden, EGJ barrier function, and esophageal body contractility in patients with PPIdependent GERD during short-term evaluation. Longer follow-up to clarify the sustainability of ARMA is needed.

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/Aims: Anti-reflux mucosal ablation (ARMA) is a promising endoscopic intervention for proton pump inhibitor (PPI)-dependent gastroesophageal reflux disease (GERD). However, the effect of ARMA on esophageal motility remains unclear. Methods: Twenty patients with PPI-dependent GERD receiving ARMA were prospectively enrolled. Comprehensive self-report symptom questionnaires, endoscopy, 24-hour impedance-pH monitoring, and high-resolution impedance manometry were performed and analyzed before and 3 months after ARMA. Results: All ARMA procedures were performed successfully. Symptom scores, including GerdQ (11.16 ± 2.67 to 9.11 ± 2.64, P = 0.026) and reflux symptom index (11.63 ± 5.62 to 6.11 ± 3.86, P = 0.001), improved significantly, while 13 patients (65%) reported discontinuation of PPI. Total acid exposure time (5.84 ± 4.63% to 2.83 ± 3.41%, P = 0.024) and number of reflux episodes (73.05 ± 19.34 to 37.55 ± 22.71, P < 0.001) decreased significantly after ARMA. Improved esophagogastric junction (EGJ) barrier function, including increased lower esophageal sphincter resting pressure (13.89 ± 10.78 mmHg to 21.68 ± 11.5 mmHg, P = 0.034), 4-second integrated relaxation pressure (5.75 ± 6.42 mmHg to 9.99 ± 5.89 mmHg, P = 0.020), and EGJ-contractile integral(16.42 ± 16.93 mmHg · cm to 31.95 ± 21.25 mmHg · cm, P = 0.016), were observed. Esophageal body contractility also increased significantly (distal contractile integral, 966.85 ± 845.84 mmHg · s · cm to 1198.8 ± 811.74 mmHg · s · cm, P = 0.023). Patients with symptom improvement had better pre-AMRA esophageal body contractility. Conclusions ARMA effectively improves symptoms and reflux burden, EGJ barrier function, and esophageal body contractility in patients with PPIdependent GERD during short-term evaluation. Longer follow-up to clarify the sustainability of ARMA is needed.

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 identify effective strategies to motivate medical staff in private hospitals to engage in health science popularization by analyzing the current situation and influencing factors.Methods A general descriptive analysis was conducted to evaluate the basic information and status of health science popularization among the subjects.The factors influencing the willingness of medical staff in private hospitals to engage in health science popularization were examined using single-factor a-nalysis and a multi-factor logistic regression model.Results A total of 88.61％of medical staff expressed willingness to partici-pate in health science popularization,while 56.39％engaged in such activities.Factors such as interest in health science popu-larization,availability of resources,training,and dissemination channels significantly influenced participation(P＜0.05).Con-clusion Similar to public hospitals,most medical staff in private hospitals are willing to disseminate health science,but actual participation rate remains low.To enhance engagement,hospitals should improve performance incentive mechanisms and develop a diversified training system and comprehensive media platforms for science popularization.