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.

Twenty one flavonoid glycosides were isolated and purified from n-butanol portion of the water extract of A. annua by various chromatographic techniques such as HP-20 macroporous adsorption resin, silica gel, ODS, Sephadex LH-20 gel column chromatography and preparative high performance liquid chromatography. Their structures were identified by analysis of physicochemical properties and spectral data, and determined as axillarin-7-O-β-D-xylopyranosyl-(1→6)-β-D-glucopyranoside (1), orientin (2), apigenin-6-C-β-D-glucopyranosyl-8-C-β-L-arabinopyranoside (3), apigenin-6-C-β-D-galactopyranosyl-8-C-β-L-arabinopyranoside (4), apigenin-6-C-β-L-arabinopyranosyl-8-C-β-D-glucopyranoside (5), apigenin-6-C-α-L-arabinofuranosyl-8-C-β-D-glucopyranoside (6), quercetin-3-O-β-D-glucopyranosyl-(1→2)-β-D-glucopyranoside (7), apigenin-6-C-α-L-arabinopyranosyl-8-C-β-D-glucopyranoside (8), vicenin-2 (9), patuletin-7-O-β-D-glucopyranoside (10), luteolin-6-C-glucopyranoside (11), vitexin (12), kaempferol-3-O-β-galactopyranosyl-(1→2)-β-glucopyranoside (13), quercetin-7-O-β-D-glucopyranoside (14), patuletin-3-O-β-D-glucopyranoside (15), 7-O-methyl-quercetagetin-6-O-β-D-glucopyranoside (16), quercetin-3-O-β-D-glucopyranoside (17), nepitrin (18), rutin (19), kaempferol-3-O-β-sophoroside (20), and patuletin-3-O-rutinoside (21). Compound 1 is a new compound, compounds 2, 4, 6, 7, 10, 11, 13, 15, 16, 18, 20 and 21 are isolated from A. annua for the first time. In the anti-inflammatory assay, compound 1 inhibited the release of IL-6 from LPS-induced RAW264.7 cells to significantly degrees with the high (100 μmol·L^-1), medium (50 μmol·L^-1), low (25 μmol·L^-1) concentration.

Objective To investigate the value of ¹⁸F-FDG PET/CT metabolic parameters and metabolic heterogeneity for predicting the expression of human epidermal growth factor receptor 2 (HER2) in patients with gastric cancer. Methods A total of 45 patients with gastric cancer confirmed by surgical pathology between September 2016 and May 2021 were enrolled in this study.All the patients underwent ¹⁸F-FDG PET/CT examination before surgery.The maximum standardized uptake value (SUVmax),metabolic tumor volume (MTV),and total lesion glycolysis (TLG) of primary gastric cancer were measured,and the linear regression slope of MTV corresponding to different SUVmax thresholds (40% SUVmax and 80% SUVmax) was calculated.The absolute value of the slope was deemed to represent the metabolic heterogeneity of primary gastric cancer,termed the heterogeneity index (HI).Univariate and multivariate Logistic regression analyses were conducted to evaluate the correlations of ¹⁸F-FDG PET/CT metabolic parameters and HI with HER2 expression. Results The 45 patients included 10 with positive HER2 expression and 35 with negative result.The MTV (P=0.043) and HI (P=0.048) were lower in the patients with positive HER2 expression than in the patients with negative HER2 expression.The MTV and HI had the optimal thresholds of 12.10 cm³ and 3.71,respectively,which respectively showed the accuracy of 62.2% and 57.8% for predicting HER2 expression.The univariate Logistic regression showed that the tumor differentiation degree,MTV,and HI were correlated with HER2 expression,while the multivariate Logistic regression showed that only the tumor differentiation degree (OR=20.130,95%CI=1.843-219.860,P=0.014) was an independent predictor for HER2 expression.A further stratified analysis of the tumor differentiation degree showed that HER2 expression only varied among different MTV threshold groups in patients with moderately/well differentiated gastric cancer (P=0.031). Conclusions MTV and HI were associated with HER2 expression in gastric cancer,whereas neither played an independent predictive role.Therefore,these factors should be combined with clinicopathological characteristics of patients to jointly guide treatment decisions.
Fluorodeoxyglucose F18 ; Humans ; Positron Emission Tomography Computed Tomography/methods* ; Receptor, ErbB-2 ; Retrospective Studies ; Stomach Neoplasms/diagnostic imaging*

Background: and Purpose The rate of donepezil discontinuation and the underlying reasons for discontinuation in Asian patients with Alzheimer’s disease (AD) are currently unknown. We aimed to determine the treatment discontinuation rates in AD patients who had newly been prescribed donepezil in routine clinical practice in Asia. Methods: This 1-year observational study involved 38 institutions in seven Asian countries, and it evaluated 398 participants aged 50–90 years with a diagnosis of probable AD and on newly prescribed donepezil monotherapy. The primary endpoint was the rate of donepezil discontinuation over 1 year. Secondary endpoints included the reason for discontinuation,treatment duration, changes in cognitive function over the 1-year study period, and compliance as assessed using a clinician rating scale (CRS) and visual analog scale (VAS). Results: Donepezil was discontinued in 83 (20.9%) patients, most commonly due to an adverse event (43.4%). The mean treatment duration was 103.67 days in patients who discontinued. Among patients whose cognitive function was assessed at baseline and 1 year, there were no significant changes in scores on the Mini-Mental State Examination, Montreal Cognitive Assessment, and Trail-Making Test–Black and White scores, whereas the Clinical Dementia Rating score increased significantly (p<0.001). Treatment compliance at 1 year was 96.8% (306/316) on the CRS and 92.6±14.1% (mean±standard deviation) on the VAS. Conclusions In patients on newly prescribed donepezil, the primary reason for discontinuation was an adverse event. Cognitive assessments revealed no significant worsening at 1 year, indicating that continuous donepezil treatment contributes to the maintenance of cognitive function.

Background: and Purpose The rate of donepezil discontinuation and the underlying reasons for discontinuation in Asian patients with Alzheimer’s disease (AD) are currently unknown. We aimed to determine the treatment discontinuation rates in AD patients who had newly been prescribed donepezil in routine clinical practice in Asia. Methods: This 1-year observational study involved 38 institutions in seven Asian countries, and it evaluated 398 participants aged 50–90 years with a diagnosis of probable AD and on newly prescribed donepezil monotherapy. The primary endpoint was the rate of donepezil discontinuation over 1 year. Secondary endpoints included the reason for discontinuation,treatment duration, changes in cognitive function over the 1-year study period, and compliance as assessed using a clinician rating scale (CRS) and visual analog scale (VAS). Results: Donepezil was discontinued in 83 (20.9%) patients, most commonly due to an adverse event (43.4%). The mean treatment duration was 103.67 days in patients who discontinued. Among patients whose cognitive function was assessed at baseline and 1 year, there were no significant changes in scores on the Mini-Mental State Examination, Montreal Cognitive Assessment, and Trail-Making Test–Black and White scores, whereas the Clinical Dementia Rating score increased significantly (p<0.001). Treatment compliance at 1 year was 96.8% (306/316) on the CRS and 92.6±14.1% (mean±standard deviation) on the VAS. Conclusions In patients on newly prescribed donepezil, the primary reason for discontinuation was an adverse event. Cognitive assessments revealed no significant worsening at 1 year, indicating that continuous donepezil treatment contributes to the maintenance of cognitive function.

In this study, we isolated and purified the extracts of the whole plant of Crotalaria sessiliflora L. by column chromatographic. Twelve compounds were isolated and identified as followings: sessiliflorin B (1), quercetin (2), kaempferol (3), soyasapogenol B (4), fernenol (5), neoechinulin A (6), ethyl 4-hydroxybenzoate (7), ethyl caffeate (8), 5,7-dihydroxychromone (9), crotadihydrofuran A (10), butesuperin B (11) and aurantiamide acetate (12). Compound 1 is a new compound, compound 3-12 were isolated from the plant for the first time.