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 associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. METHODS: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk. RESULTS: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk. CONCLUSION PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental Exposure

OBJECTIVE: Pseudomonas aeruginosa( P. aeruginosa) is a prevalent pathogenic bacterium involved in meningitis; however, the virulence factors contributing to this disease remain poorly understood. METHODS: The virulence of the P. aeruginosa A584, isolated from meningitis samples, was evaluated by constructing in vitro blood-brain barrier and in vivo systemic infection models. qPCR, whole-genome sequencing, and drug efflux assays of A584 were performed to analyze the virulence factors. RESULTS: Genomic sequencing showed that A584 formed a phylogenetic cluster with the reference strains NY7610, DDRC3, Pa58, and Pa124. Its genome includes abundant virulence factors, such as hemolysin, the Type IV secretion system, and pyoverdine. A584 is a multidrug-resistant strain, and its wide-spectrum resistance is associated with enhanced drug efflux. Moreover, this strain caused significantly more severe damage to the blood-brain barrier than the standard strain, PAO1. qPCR assays further revealed the downregulation of the blood-brain barrier-associated proteins Claudin-5 and Occludin by A584. During systemic infection, A584 exhibited a higher capacity of brain colonization than PAO1 (37.1 × 10 ⁶ CFU/g brain versus 2.5 × 10 ⁶ CFU/g brain), leading to higher levels of the pro-inflammatory factors IL-1β and TNF-α. CONCLUSION This study sheds light on the virulence factors of P. aeruginosa involved in meningitis.
Pseudomonas aeruginosa/genetics* ; Virulence Factors/metabolism* ; Animals ; Virulence ; Mice ; Pseudomonas Infections/microbiology* ; Blood-Brain Barrier/microbiology* ; Humans ; Female

Objective To evaluate the characteristics of different antigen-specific T cell immune responses to severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)after inoculation with 2 doses of SARS-CoV-2 inactivated vaccine for 12 months.Methods Fifteen healthy adults were enrolled in this study and blood samples collected at 12 months after receiving two doses of SARS-CoV-2 inactivated vaccine.The level and phenotypic characteristics of SARS-CoV-2 antigen-specific T lymphocytes were detected by activation-induced markers(AIM)based on polychromatic flow cytometry.Results After 12 months of inoculation with 2 doses of SARS-CoV-2 inactivated vaccine,more than 90%of adults had detectable Spike and Non-spike antigen-specific CD4+ T cells immune responses(Spike:14/15,P=0.0001;Non-spike:15/15,P<0.0001).80%of adults had detectable Spike and Non-spike antigen-specific CD8+ T cells immune responses(Spike:12/15,P=0.0463;Non-spike:12/15,P=0.0806).Antigen-specific CD4+ T cells induced by SARS-CoV-2 inactivated vaccination after 12 months were composed of predominantly central memory(CM)and effector memory 1(EM1)cells.On the other hand,in terms of helper subsets,antigen-specific CD4+ T cells mainly showed T helper 1/17(Th1/17)and T helper 2(Th2)phenotypes.Conclusions SARS-CoV-2 inactivated vaccination generates durable and extensive antigen-specific CD4+ T cell memory responses,which may be the key factor for the low proportion of severe coronavirus disease 2019(COVID-19)infection in China.

ObjectiveTo investigate the role and possible mechanism of action of rhubarb decoction （RD） retention enema in improving inflammatory damage of brain tissue in a rat model of mild hepatic encephalopathy （MHE）. MethodsA total of 60 male Sprague-Dawley rats were divided into blank group （CON group with 6 rats） and chronic liver cirrhosis modeling group with 54 rats using the complete randomization method. After 12 weeks， 40 rats with successful modeling which were confirmed to meet the requirements for MHE model by the Morris water maze test were randomly divided into model group （MOD group）， lactulose group （LT group）， low-dose RD group （RD1 group）， middle-dose RD group （RD2 group）， and high-dose RD group （RD3 group）， with 8 rats in each group. The rats in the CON group and the MOD group were given retention enema with 2 mL of normal saline once a day； the rats in the LT group were given retention enema with 2 mL of lactulose at a dose of 22.5% once a day； the rats in the RD1， RD2， and RD3 groups were given retention enema with 2 mL RD at a dose of 2.5， 5.0， and 7.5 g/kg， respectively， once a day. After 10 days of treatment， the Morris water maze test was performed to analyze the spatial learning and memory abilities of rats. The rats were analyzed from the following aspects： behavioral status； the serum levels of alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） and the level of blood ammonia； pathological changes of liver tissue and brain tissue； the mRNA and protein expression levels of phosphatidylinositol 3-kinase （PI3K）， protein kinase B （AKT）， and mammalian target of rapamycin （mTOR） in brain tissue. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsCompared with the MOD group， the RD1， RD2， and RD3 groups had a significantly shorter escape latency （all P<0.01）， significant reductions in the levels of ALT， AST， IL-1β， IL-6， TNF-α， and blood ammonia （all P<0.05）， significant alleviation of the degeneration， necrosis， and inflammation of hepatocytes and brain cells， and significant reductions in the mRNA and protein expression levels of PI3K， AKT， and mTOR in brain tissue （all P<0.05）， and the RD3 group had a better treatment outcome than the RD1 and RD2 groups. ConclusionRetention enema with RD can improve cognitive function and inflammatory damage of brain tissue in MHE rats， possibly by regulating the PI3K/AKT/mTOR signaling pathway.

Aim To investigate the effects of multi-gly-cosides of Tripterygium wilfordii(GTW)on mitochon-drial dynamics-related proteins and the mechanism of nephroprotective effects in IgA nephrophathy(IgAN)rats.Methods SPF grade male SD rats were random-ly divided into the Control group,modelling group,prednisone group(6.25 mg·kg·d-1)and GTW group(6.25 mg·kg·d-1).The IgAN rat model was established by the method of"bovine serum albumin(BSA)+carbon tetrachloride(CCl4)+lipopolysac-charide(LPS)".The total amount of urinary protein(24 h-UTP)and erythrocyte count in urine were meas-ured in 24 h urine.Blood biochemistry of serum albu-min(ALB),alanine aminotransferase(ALT),urea ni-trogen(BUN),and creatinine(Scr)were measured in abdominal aorta of the rats;immunofluorescence and HE staining were used to observe the histopathology of the kidneys;RT-PCR and Western blotting were used to detect the mRNA and protein expression levels of key proteins regulating mitochondrial division and fu-sion:dynamin-related protein 1(Drp1),mitochondrial fusion protein 1(Mfn1),and mitochondrial fusion pro-tein 2(Mfn2),and PTEN-induced putative kinase 1(Pink1),in the kidney tissue of rats.Results GTW significantly reduced urinary erythrocyte count and 24 h-UTP,decreased serum ALT,BUN and Scr levels,in-creased serum ALB levels,improved renal histopatho-logical status in IgAN rats,increased the protein and mRNA expression levels of Mfn1,Mfn2,and Pink1,and decreased the protein and mRNA expression levels of Drp1 in renal tissues.Conclusions GTW may regu-late mitochondrial structure and maintain the dynamic balance of mitochondrial dynamics by promoting the ex-pression of Mfn1,Mfn2,Pink1 and decreasing Drp1.This may result in a reduction in urinary erythrocyte counts and proteinuria,and an improvement in renal function.