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 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.

Objective To investigate the clinical characteristics of female migraine patients with patent foramen ovale(PFO)and design a risk prediction model for PFO in female migraine patients(migraineur patients PFO risk prediction model,MPRPM).Methods Female migraine patients who visited Zhongshan Hospital,Fudan University from Jun 1,2019 to Dec 31,2022 were included.Preoperative information and follow-up results after discontinuation of medication were collected.Patients were divided into PFO-positive and PFO-negative groups based on transesophageal echocardiography results.A multivariate Logistic regression model and a random forest model were constructed,and the random forest model was validated multidimensionally.Key features were selected based on the mean decrease accuracy(MDA)to construct MPRPM.Results A total of 305 female patients were included in the study,with 204 patients in the PFO-positive group and 101 patients in the PFO-negative group.Multivariate Logistic regression analysis showed that age at migraine onset,attack frequency,severe impact on life during attacks,exercise-related headaches,menstruation-induced headaches,aura migraines,and a history of cryptogenic stroke were predictive factors for PFO positivity.The random forest model effectively predicted the incidence of PFO in female migraine patients,with an AUC of 0.895(95%CI:0.847-0.943).MPRPM demonstrated a sensitivity of 71.6%and specificity of 91.1%(AUC:0.862,95%CI:0.818-0.906,P<0.001).The optimal cut-off value was 2.5 points.Patients correctly classified by the model showed a higher rate of symptom improvement compared to incorrectly classified patients(94.3%vs.82.0%,P=0.023).Conclusion We identified predictive factors for PFO in migraine patients.MPRPM can provide guidance in the diagnostic process and therapeutic decision-making for female migraine patients,assist in patient triage,and reduce the healthcare burden.

Clinicians need to focus on various points in the diagnosis and treatment of insomnia.This article prescribed the treatment protocol based on the unique features,such as insomnia in the elderly,women experiencing specific physiologi-cal periods,children insomnia,insomnia in sleep-breathing disorder patients,insomnia in patients with chronic liver and kidney dysfunction.It pro-vides some reference for clinicians while they make decision on diagnosis,differentiation and treat-ment methods.

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.