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.

Abstract The health literacy level among the elderly in China remains at a low level. The 14th Five-Year Plan for Healthy Aging clearly points out that health literacy promotion projects should be implemented to improve the health literacy level among the elderly. The health literacy promotion strategies for the elderly require individual, social, policy and environmental supports. This article reviewed four types of health literacy promotion strategies for the elderly, including social strategies, lecture-based health education strategies, new media-based health communication strategies and environmental strategies. It also proposed that health education institutions, communities and other parties should work together, take advantage of digital technology and internet, and take various measures simultaneously to improve the health literacy of the elderly.

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.

Diarrhea is a common and frequent disease in clinical practice.Many factors cause diarrhea,and numerous research method with animal models of diarrhea have been explored.Despite this,drugs for the treatment of diarrhea in clinical practice are limited,and some existing drugs are only suitable for diarrhea caused by a single factor.Therefore,the construction and selection of appropriate animal models of diarrhea are not only important for in-depth studies of the pathogenesis,but are also effective means for the clinical screening and evaluation of drugs for comprehensively preventing and treating diarrhea.This article reviews the literature on the establishment and evaluation of animal models of functional,bacterial,viral,and symptomatic diarrhea,as well as progress of therapeutic drug research,to provide a reference for animal experimental research into the prevention and treatment of diarrhea.

AIM To study the chemical constituents from the ethanol precipitated sediment of Radix Isatidis and their anti-inflammatory activities.METHODS The effects of ethanol precipitated sediment on IL-6 and TNF-α levels were detected by LPS-induced RAW264.7 cell inflammation model and enzyme-linked immunosorbent assay.The chemical constituents were analyzed by UPLC-QTOF-MS/MS and high performance molecular exclusion method.RESULTS When the concentration of Radix Isatidis ethanol sediment were 200 and 400 μg/mL,it could significantly inhibit the release of IL-6 and TNF-α.The ethanol sediment of Radix Isatidis was mainly composed of polysaccharides and proteins,including trace amounts of lipopeptides,indoles and amino acids,among which polysaccharides were mainly glucans.CONCLUSION The constituents from the ethanol sediment of Radix Isatidis are sugars,alkaloids(indoles),amino acids,and organic acids(fatty acids),and they may exert anti-inflammatory effects by synergistic manner.

Objective:To explore the effects of the first dorsal metatarsal artery terminal branch flaps in repairing skin and soft tissue defects of fingers.Methods:The study was a retrospective observational study. From October 2021 to December 2022, 44 patients with skin and soft tissue defects in 55 fingers who met the inclusion criteria were admitted to Suzhou Ruihua Orthopedic Hospital. There were 39 males (48 fingers) and 5 females (7 fingers), aged 18 to 54 years. The single wound area after debridement ranged from 1.5 cm×1.0 cm to 3.0 cm×2.0 cm. The color Doppler ultrasonography was performed before operation to locate the first dorsal metatarsal artery and its terminal branches, and a first dorsal metatarsal artery terminal branch flap was designed according to the wound condition, with the area of harvested single flap ranged from 1.7 cm×1.2 cm to 3.2 cm×2.2 cm. The wounds in the flap donor areas were transplanted with full-thickness skin grafts from ipsilateral inner calf. The type of flap was recorded, and the diameter of the terminal branch of the first dorsal metatarsal artery was measured during operation. The survival of the flap was observed one week after operation. The wound healing in the flap donor and recipient areas was observed two weeks after operation. At the last follow-up, the functional recovery of the affected fingers was evaluated by the trial standards for evaluation of partial function of upper extremity by the Hand Surgery Society of Chinese Medical Association, the sensory function of the flap was evaluated using the sensory function evaluation standard of British Medical Research Council, the scar in the donor and recipient areas of the flap was evaluated using the Vancouver scar scale (VSS), and the Allen test was conducted in the toe of flap donor area to evaluate the blood flow.Results:The monoblock type flaps in 31 patients and flow-through type flaps in 2 patients were used to repair wounds in single finger, 2 monoblock type flaps in 8 patients were used to repair wounds in 2 fingers at the same time, and the single-pedicle and two-flap type flaps in 3 patients were used to repair wounds in 2 fingers at the same time. The diameter of the fibular terminal branch of the first dorsal metatarsal artery ranged from 0.40 to 1.10 mm, and the diameter of the tibial terminal branch of the first dorsal metatarsal artery ranged from 0.70 to 0.75 mm. All the flaps survived at one week after operation, and all the wounds demonstrated optimal healing in the flap donor and recipient areas at two weeks after operation. All patients were followed up for 6 to 18 months. At the last follow-up, the functional recovery of 48 fingers was evaluated as excellent, and the functional recovery of 7 fingers was evaluated as good; the sensory function of 8 flaps was rated as S2, and the sensory function of 47 flaps was rated as S3, and the two-point discrimination distance of the flaps was 8-14 mm; the VSS scores in the flap recipient areas ranged from 3 to 6, and the VSS scores in the flap donor areas ranged from 4 to 7; the Allen test result of the toes in the donor areas were all negative with normal blood flow.Conclusions:The first dorsal metatarsal artery terminal branch flaps have several advantages, including relatively hidden donor area, shallow anatomical level, simple intraoperative operation, and flexible flap design. The flap is incised without damaging the main artery of the toe, which can repair skin and soft tissue defects of the fingers and ensure the utmost protection of the toes in donor areas. The fingers exhibit improved appearance, texture, sensation, and function after operation.