Despite of the widespread use of hydrogen and methane breath test, the variability in testing protocols, gas measurement techniques, and interpretation criteria continues to challenge the reproducibility and comparability across centers, especially in the Asia-Pacific region. The Asian Neurogastroenterology and Motility Association hence presents the first Asian monograph guiding application and interpretation of breath test. The monograph was formulated according to the framework of indications, preparatory process, performance, and interpretation of results, as well as future direction for research.

Licochalcone E is a chalcone isolated from Glycyrrhiza uralensis and G. inflata Batal. This study explored the effect of licochalcone E on improving hepatic steatosis in obese mice and evaluated the role of licochalcone E in regulating lipid accumulation in hepatocytes. In vitro, oleic acid–induced hepatocytes were treated with licochalcone E to investigate its effect on lipid metabolic pathways. In animal experiments, male C57BL/6 mice were fed with a high-fat diet (HFD) and treated with licochalcone E by intraperitoneal injection for 12 weeks to assess its effects on biochemical indexes and hepatic steatosis. Furthermore, mice were fed a methionine/choline-deficient (MCD) diet and administered licochalcone E, followed by evaluation of liver fibrosis. Licochalcone E effectively reduced body weight, epididymal and inguinal fat weight, and adipocyte size in HFD-induced obese mice. Licochalcone E treatment of obese mice also reduced hepatic lipid accumulation and improved hepatocyte steatosis. Licochalcone E regulated the expression of lipogenesis- and lipolysis-related genes in the livers of obese mice and increased AMPK phosphorylation and Sirt1 expression in the liver. Licochalcone E also attenuated hepatic inflammation and oxidative stress in obese mice. Furthermore, treatment of MCD-induced mice with licochalcone E reduced the number of lipid vacuoles and the extent of fibrosis and inhibited liver inflammation. In FL83B hepatocytes, licochalcone E could regulate lipogenesis and lipolysis, and increase the phosphorylation of AMPK and ACC. These findings provide new insights into the role of licochalcone E in regulating lipid metabolism and preventing hepatic steatosis.

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.

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.