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: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

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/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

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: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

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.

ObjectiveDetection and quantification of RNA synthesis in cells is a widely used technique for monitoring cell viability, health, and metabolic rate.After exposure to environmental stimuli, both the internal reference gene and target gene would be degraded. As a result, it is imperative to consider the accurate capture of nascent RNA and the detection of transcriptional levels of RNA following environmental stimulation. This study aims to create a Click Chemistry method that utilizes its property to capture nascent RNA from total RNA that was stimulated by the environment. MethodsThe new RNA was labeled with 5-ethyluridine (5-EU) instead of uracil, and the azido-biotin medium ligand was connected to the magnetic sphere using a combination of “Click Chemistry” and magnetic bead screening. Then the new RNA was captured and the transcription rate of 16S rRNA was detected by fluorescence molecular beacon (M.B.) and quantitative reverse transcription PCR (qRT-PCR). ResultsThe bacterial nascent RNA captured by “Click Chemistry” screening can be used as a reverse transcription template to form cDNA. Combined with the fluorescent molecular beacon M.B.1, the synthesis rate of rRNA at 37℃ is 1.2 times higher than that at 15℃. The 16S rRNA gene and cspI gene can be detected by fluorescent quantitative PCR,it was found that the measured relative gene expression changes were significantly enhanced at 25℃ and 16℃ when analyzed with nascent RNA rather than total RNA, enabling accurate detection of RNA transcription rates. ConclusionCompared to other article reported experimental methods that utilize screening magnetic columns, the technical scheme employed in this study is more suitable for bacteria, and the operation steps are simple and easy to implement, making it an effective RNA capture method for researchers.

Aim To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.