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.

Objective To investigate the Meta-analysis of microRNA(miRNA)in distinguishing active tu-berculosis and latent tuberculosis.Methods CNKI,Wanfang Data,VIP,PubMed,Cochrane Library,Web of Science and Embase databases were searched to select the literature on miRNA in discriminating active tuber-culosis and latent tuberculosis from the establishment of the database to April 2023,and screened strictly ac-cording to the inclusion and exclusion criteria.The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2,and data extraction and summary analysis were carried out with Stata16.0 software.Heterogeneity among studies was evaluated by calculating I2,and the sources of heteroge-neity were further explored by Meta-regression and subgroup analysis.Publication bias was assessed using Deeks funnel plot.Results The Meta-analysis encompassed 9 articles,comprising 13 studies.The combined sensitivity of miRNA in differentiating active tuberculosis and latent tuberculosis was found to be 0.79(95％CI:0.69-0.86,I2=86.24％),with a specificity of 0.73(95％CI:0.64-0.81,I2=81.80％).The positive likelihood ratio was 2.96(95％CI:2.22-3.95,12=63.84％),while the negative likelihood ratio was 0.29(95％CI:0.20-0.41,I2=84.04％).Furthermore,the diagnostic odds ratio was 10.33(95％CI:6.43-16.61,I2=99.90％),and the area under the receiver operating characteristic curve was 0.83(95％CI:0.79-0.86).The results of Meta-regression and subgroup analysis showed that sample size may be the source of sensitivity heterogeneity,and dysregulation of miRNA may be the source of specificity heterogeneity.Deeks funnel plot showed no publication bias among included studies.Conclusion miRNA shows good diagnostic a-bility in distinguishing active tuberculosis and latent tuberculosis,which has important significance for impro-ving the development of diagnostic strategies for tuberculosis management.

OBJECTIVE To investigate the mechanism of Yifei xuanfei jiangzhuo formula （YFXF） against vascular dementia （VD）. METHODS The differentially expressed genes of YFXF （YDEGs） were obtained by network pharmacology. High-risk genes were screened from YDEGs by using the nomogram model. The optimal machine learning models in generalized linear， support vector machine， extreme gradient boosting and random forest models were screened based on high-risk genes. VD model rats were established by bilateral common carotid artery occlusion， and were randomly divided into model group and YFXF group （12.18 g/kg， by the total amount of crude drugs）， and sham operation group was established additionally， with 6 rats in each group. The effects of YFXF on behavior （using escape latency and times of crossing platform as indexes）， histopathologic changes of cerebral cortex， and the expression of proteins related to the secreted phosphoprotein 1 （SPP1）/phosphoinositide 3-kinase （PI3K）/protein kinase B （aka Akt） signaling pathway and the mRNA expression of SPP1 in cerebral cortex of VD rats were evaluated. RESULTS A total of 6 YDEGs were obtained， among which SPP1， CCL2， HMOX1 and HSPB1 may be high-risk genes of VD. The generalized linear model based on high-risk genes had the highest prediction accuracy （area under the curve of 0.954）. Compared with the model group， YFXF could significantly shorten the escape latency of VD rats， significantly increase the times of crossing platform （P＜0.05）； improve the pathological damage of cerebral cortex， such as neuronal shrinkage and neuronal necrosis； significantly reduce the expressions of SPP1 protein and mRNA （P＜0.05）， while significantly increase the phosphorylation levels of PI3K and Akt （P＜0.05）. CONCLUSIONS VD high-risk genes SPP1， CCL2， HMOX1 and HSPB1 may be the important targets of YFXF. YFXF may play an anti-VD role by down-regulating the protein and mRNA expressions of SPP1 and activating PI3K/Akt signaling pathway.

Objective To investigate the relationship between coagulation indicators and early prognosis in patients with acute respiratory distress syndrome (ARDS).Methods The data of ARDS patients receiving the treatment in the intensive care unit (ICU) from 2008-2019 were selected from the Critical Care Medicine Open Database (MIMIC-Ⅳ V2.0 version) jointly published by MIT,Beth Israel Deaconess Medical Center,and Philips Medical,the data were categorized according to the severity of the patients' disease and the causes of lung damage.The coagulation indexes and 28 d mortality (m28d) rates were compared among different ARDS patients.The receiver operating characteristic (ROC) curve was drawn.The area under the curve was calculated to evaluate the predictive values of the related indicators.The univariate and multivariate logistic re-gression was adopted to analyze the risk factors affecting m28d in the patients with ARDS.Results Maximum prothrombin time (PTmax) in the patients with pulmonary origin ARDS was significantly lower than that in the patients without pulmonary origin ARDS,and the difference was statistically significant (P<0.05).PLTmin,PLTmax and Sequential Organ Failure Assessment (SOFA) score had statistical difference among dif-ferent severity degrees of ARDS patients (P<0.05).Minimum international normalized ratio (INRmin),maxi-mum international normalized ratio (INRmax),minimum prothrombin time (PTmin),PTmax,maximum activated partial thromboplastin time (APTTmax) and SOFA score had statistical differences between the survival group and death group (P<0.05).AUC of INRmin,INRmax,PTmin,PTmax and APTTmax were 0.607,0.624,0.610,0.620 and 0.648 respectively.The multivariate logistic regression analysis showed that APTTmax (OR=1.011,95%CI:1.001-1.022,P=0.029) was an independent risk factor for affecting m28d in the ARDS patients.Conclu-sion Plasma PLT levels in different severities of ARDS patients have the difference and APTTmax on the first day in ICU is an independent risk factor for affecting early prognosis in ARDS patients.

Complete androgen insensitivity syndrome(CAIS) is characterized by lack of androgen response in target organs due to androgen receptor dysfunction, resulting in feminized external genitalia. Individuals with CAIS are typically advised to live as females. This article reports a patient diagnosed with CAIS and gender dysphoria in adulthood. Following the removal of a left pelvic mass, pathology indicated cryptorchidism with a concurrent Leydig cell tumor. Genetic testing revealed a deletion mutation in exon 3 of androgen receptor gene. During follow-up, the patient underwent gender reassignment, transitioning socially from female to male. This case provides new insights into gender allocation for CAIS patients.

We investigated the impact and predictive value of bladder function in patients with benign prostatic hyperplasia (BPH) on the efficacy of transurethral prostatectomy. Symptomatic, imaging, and urodynamic data of patients who underwent transurethral prostatectomy at West China Hospital of Sichuan University (Chengdu, China) from July 2019 to December 2021 were collected. Follow-up data included the quality of life (QoL), International Prostate Symptom Score (IPSS), and IPSS storage and voiding (IPSS-s and IPSS-v). Moreover, urinary creatinine (Cr), nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and prostaglandin estradiol (PGE2) were measured in 30 patients with BPH and 30 healthy participants. Perioperative indicators were determined by subgroup analyses and receiver operating characteristic (ROC) curve analysis. Among the 313 patients with BPH included, patients with severe micturition problems had more improvements but higher micturition grades postoperatively than those with moderate symptoms. Similarly, good bladder sensation, compliance, and detrusor contractility (DC) were predictors of low postoperative IPSS and QoL. The urinary concentrations of BDNF/Cr, NGF/Cr, and PGE2/Cr in patients were significantly higher than those in healthy participants (all P < 0.001). After evaluation, only DC was significantly related to both urinary indicators and postoperative recovery of patients. Patients with good DC, as predicted by urinary indicators, had lower IPSS and IPSS-v than those with reduced DC at the 1st month postoperatively (both P < 0.05). In summary, patients with impaired bladder function had poor recovery. The combined levels of urinary BDNF/Cr, NGF/Cr, and PGE2/Cr in patients with BPH may be valid predictors of preoperative bladder function and postoperative recovery.