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.

The mild-natured and bitter-flavored traditional Chinese medicines (MB-TCMs) are an important class of TCMs that have been widely used in clinical practice and recognized as safe long-term treatments for chronic diseases. However, as an important class of TCMs, the panorama of pharmacological effects and the mechanisms of MB-TCMs have not been systemically reviewed. Compelling studies have shown that gut microbiota can mediate the therapeutic activity of TCMs and help to elucidate the core principles of TCM medicinal theory. In this systematic review, we found that MB-TCMs commonly participated in the modulation of metabolic syndrome, intestinal inflammation, nervous system disease and cardiovascular system disease in association with promoting the growth of beneficial bacteria Bacteroides, Akkermansia, Lactobacillus, Bifidobacterium, Roseburia as well as inhibiting the proliferation of harmful bacteria Helicobacter, Enterococcus, Desulfovibrio and Escherichia-Shigella. These alterations, correspondingly, enhance the generation of protective metabolites, mainly including short-chain fatty acids (SCFAs), bile acid (BAs), 5-hydroxytryptamine (5-HT), indole and gamma-aminobutyric acid (GABA), and inhibit the generation of harmful metabolites, such as proinflammatory factors trimethylamine oxide (TAMO) and lipopolysaccharide (LPS), to further exert multiplicative effects for the maintenance of human health through several different signaling pathways. Altogether, this present review has attempted to comprehensively summarize the relationship between MB-TCMs and gut microbiota by establishing the TCMs-gut microbiota-metabolite-signaling pathway-diseases axis, which may provide new insight into the study of TCM medicinal theories and their clinical applications.

Objective To investigate the current situation and influencing factors of latent tuberculosis infection(LTBI)among close contacts of positive etiology pulmonary tuberculosis(PTB)patients,provide basis for formula-ting intervention measures for LTBI.Methods A multi-stage stratified cluster random sampling method was used to select close contacts of positive etiology PTB patients from 39 districts and counties in Chongqing City as the study objects.Demographic information was collected by questionnaire survey and the infection of Mycobacterium tuberculosis was detected by interferon gamma release assay(IGRA).The influencing factors of LTBI were analyzed by x2 test and binary logistic regression model.Results A total of 2 591 close contacts were included,the male to female ratio was 0.69∶1,with the mean age of(35.72±16.64)years.1 058 cases of LTBI were detected,Myco-bacterium tuberculosis latent infection rate was 40.83％.Univariate analysis showed that the infection rate was dif-ferent among peoples of different age,body mass index(BMI),occupation,education level,marital status,wheth-er they had chronic disease or major surgery history,whether they lived together with the indicator case,and whether the cumulative contact time with the indicator case ≥250 hours,difference were all statistically significant(all P＜0.05);infection rate presented increased trend with the increase of age and BMI(both P＜0.001),and decreased trend with the increase of education(P＜0.05).Logistic regression analysis showed that age 45-54 years old(OR=1.951,95％CI:1.031-3.693),age 55-64 years old(OR=2.473,95％CI:1.279-4.781),other occupations(OR=0.530,95％CI:0.292-0.964),teachers(OR=0.439,95％CI:0.242-0.794),students(OR=0.445,95％CI:0.233-0.851),junior high school education or below(OR=1.412,95％CI:1.025-1.944),BMI＜18.5 kg/m2(OR=0.762,95％CI:0.586-0.991),co-living with indicator cases(OR=1.621,95％CI1.316-1.997)and cumu-lative contact time with indicator cases ≥250 hours(OR=1.292,95％CI:1.083-1.540)were the influential fac-tors for LTBI(all P＜0.05).Conclusion The close contacts with positive etiology PTB have a high latent infection rate of Mycobacterium tuberculosis,and it is necessary to pay attention to close contacts of high age,farmers,and frequent contact with patients,and take timely targeted interventions to reduce the risk of occurrence of disease.

Objective:To investigate the correlation between triglyceride-glucose (TyG) index on admission and unfavorable outcomes of patients with moderate-to-severe traumatic brain injury (msTBI) at 6 months postinjury.Methods:A retrospective cohort study was conducted to analyze the clinical data of 277 patients with msTBI admitted to Affiliated Jiangyin Hospital of Nantong University from January 2019 to December 2022, including 208 males and 69 females, aged 18-88 years [(57.0±15.1)years]. Glasgow Coma Scale (GCS) scores on admission were 3-8 points in 168 patients and 9-12 points in 109. According to the Glasgow Outcome Scale-Extended (GOSE) assessment at 6 months after injury, there were 121 patients with unfavorable outcomes (GOSE≤4 points) and 156 with favorable outcomes (GOSE≥5 points). The following indicators of the patients were recorded, including gender, age, history of diabetes, cause of injury, admission GCS, GCS motor score (GCSM), pupillary light reflex, worst Marshall CT classification within the first 24 hours after admission, admission TyG index, Mean Amplitude of Glycemic Excursions (MAGE) within 24 hours after admission, GCSM decline≥2 points within 72 hours after admission, craniotomy or not after admission, and prognosis, etc. TyG index served as the exposure variable focused in this study, which was calculated with fasting triglycerides and fasting blood glucose within 24 hours after admission. The 6-month prognosis of the patients was designated as the outcome variable of the study. After the patients were divided into different groups according to the three quantiles of the TyG index and unfavorable or favorable outcomes, the univariate analysis was conducted on watch variables, and variables with statistically significant differences were included in directed acyclic graphs (DAGs) for further identification of confounding variables. Factors which were found with no statistical significance in the univariate analysis but might affect insulin resistance after injury according to the authors′ previous researches were also included in the DAGs analysis. Three Logistic regression models were designed (Model 1 without correction, Model 2 with core variables of International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) corrected, and Model 3 with confounding variables screened by DAGs corrected) to analyze whether the TyG index was an independent risk factor for the prognosis of msTBI patients. The optimal Logistic regression model was selected and then restricted cubic spline (RCS) was employed to investigate the relationship between the TyG index and the unfavorable outcomes.Results:The univariate analysis suggested that there were significant differences in gender, history of diabetes, MAGE, GCSM decline, and prognosis among the three quantiles of the TyG index ( P<0.05 or 0.01). Significant differences in age, history of diabetes, GCSM, pupillary light reflex, Marshall CT classification, TyG index, MAGE and GCSM decline were observed between unfavorable and favorable outcome groups ( P<0.05 or 0.01). The results of Logistic regression analysis that identified the confounding variables that influenced the correlation between the TyG index and unfavorable prognosis with DAGs suggested that a high TyG index level was significantly correlated with unfavorable outcomes in msTBI patients. Moreover, Model 3 that was corrected with confounding variables screened by DAGs had an optimal goodness-of-fit and adaptability. Model 3-based further RCS analysis indicated that the risk of unfavorable outcomes following msTBI may increase approximately linearly with the increase in TyG index within a certain range (TyG index<9.79). Conclusions:A high TyG index level on admission is the identified as an independent risk factor for unfavorable outcomes of patients with msTBI at 6 months postinjury. As the TyG index level increases, the risk of unfavorable outcomes also rises and may show a linear increasing trend within a certain range (TyG index<9.79).

Objective This study aims to develop and validate a dynamic web-based nomogram for predicting kinetophobia in patients following percutaneous coronary intervention(PCI).Methods A prospective design was employed to selectively enroll 330 PCI patients admitted to a hospital in Hangzhou from December 2022 to July 2023.Single-factor analysis and Lasso regression were utilized to identify independent risk factors for kinesophobia post-PCI.Logistic regression was performed using R software,and a nomogram was constructed.The model was assessed through the area under the receiver operating characteristic curve(AUC)and Hosmer-Lemeshow tests.Results There were 206 cases of kinesiophobia in 330 patients after PCI,and the incidence was 62.4％.Logistic regression analysis identified combined heart failure,emergency surgery,NYHA cardiac function grade,ADL level,sedentary behavior,Chinese version of PROMIS Physical Function Summary Table score,and Chinese version of Perceptive Social Support Scale score as independent influencing factors for kinesophobia after PCI(P＜0.05).The AUC value of the model was 0.821,with a sensitivity of 70.4％and specificity of 82.0％.The Hosmer-Lemeshow fit test yielded a non-significant result(x2=9.350,P=0.314).Calibration and decision curves demonstrated the model's favorable calibration and clinical practicability.The C-index of the nomogram prediction model was 0.778,0.774,and 0.800,respectively,by 5-fold cross-validation,10-fold cross-validation,and the Bootstrap method.Conclusion The dynamic nomogram model developed in this study effectively predicts kinesophobia in patients after PCI.It provides valuable references and support for clinical staff in early identification of high-risk patients,enabling the formulation of individualized health education strategies and exercise rehabilitation plans.

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.