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.

Mutations in myosin heavy chain 7(MYH7)and myosin binding protein C3(MYBPC3)genes encoding thick filaments are the main cause of hypertrophic cardiomyopathy(HCM),while a small part of HCM is caused by mutations of troponin C1,slow skeletal and cardiac type(TNNC1),troponin T2,cardiac type(TNNT2),troponin I3,cardiac type(TNNI3),actin alpha cardiac muscle 1(ACTC1),and tropomyosin 1(TPM1)genes encoding thin filaments.In this review,we mainly introduce the detailed mechanism and research status of HCM caused by mutations of the gene encoding cardiomyocyte sarcomere in the past few years,in order to provide reference for further study of the pathogenesis and treatment of HCM.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Objective To investigate the effects of medullary differentiation-2(MD-2)in obesity-induced myocardial injury by regulating adenosine 5'-monophosphate-activated protein kinase(AMPK)activity.Methods The obese mice model was established by feeding high-fat feed.The wild-type and MD-2 gene knockout mice were randomly divided into wild-type(WT)-control group(fed normally),WT-model group(fed with high-fat diet),knockout(KO)-control group(fed normally)and KO-model group(fed with high-fat diet)with 8 mice in each group.At week 16,creatine kinase-MB(CK-MB)and lactate dehydrogenase(LDH)were measured by reagent kits;the left ventricular ejection fraction(LVEF)and left ventricular fraction shortening(LVFS)were measured with ultrasonic apparatus;the pathological changes in myocardial tissue were observed by hematoxylin and eosin(HE)staining and Masson staining;the protein expression of phosphorylation AMPK(p-AMPK)was measured by Western blot.Results HE staining and Masson staining results revealed significant necrosis and fibrosis of the myocardial tissue in WT-model group,while the degrees of necrosis and fibrosis in KO-model group were significantly reduced.The LVEF values of the WT-control group,WT-model group,KO-control group and KO-model group were(83.98±7.58)％,(52.03±4.91)％,(76.42±3.89)％and(73.71±4.22)％,respectively;the LVFS values of each group were(53.61±8.51)％,(24.56±3.76)％,(48.14±5.00)％and(44.07±3.74)％,respectively;the CK-MB values of each group were(49.83±15.49),(83.75±11.90),(54.03±18.66)and(66.85±12.98)U·L-1,respectively;the LDH values of each group were(12.24±3.20),(27.90±6.10),(13.55±3.55)and(15.53±2.58)U·L-1,respectively;the relative expression levels of p-AMPK in each group were 221.31±88.76,46.29±10.07,148.66±32.02 and 161.49±78.11.Compared with WT-control group,the above indicators in WT-model group,compared with WT-model group,the above indicators in KO-model group,the differences were statistically significant(all P＜0.05).Conclusion MD-2 mediates high-fat feed induced myocardial injury in obese mice by inhibiting AMPK activity.

The Wnt signaling pathway includes both classical and non classical pathways,Wnt5a-Frizzled-2 pathway participates in the Wnt/Ca2+signaling pathway in the non-classical pathway,which is activated by the Wnt-related protein Wnt5a and its ligand Frizzled-2.It can regulate some key sites in cells to affect cell signal transduction,and is closely related to cell growth process.Activation of Wnt5a-Fizzled-2 pathway occurs in some tissues with abundant blood supply,such as heart and brain tissues,during ischemia-reperfusion.Activation of the Wnt5a-Frizzled-2 pathway causes these intracellular calcium overload,ultimately promoting apoptosis.This article reviews the abnormal activation of Wnt5a-Frizzled-2 signaling pathway in ischemia-reperfusion injury diseases and the induced calcium overload leading to apoptosis,in order to provide reference for the study of physiological mechanisms of ischemia-reperfusion injury.

ObjectiveTo investigate the contamination status of Staphylococcus aureus in food and the presence of enterotoxin genes in Jiading District， Shanghai， and to provide a basis for the prevention and treatment of foodborne Staphylococcus aureus disease. MethodsFrom 2021 to 2023， 15 types of food were sampled for S. aureus testing， and the presence of five enterotoxin genes， including sea⁃see， was tested in the strains. ResultsOut of 705 food samples， 88 （12.48%） were positive for S. aureus. S. aureus was detected in 12 of the 15 food types， with the three food types with the highest positive rates being cold noodles （45.00%）， raw poultry （26.25%）， and vegetable salads （20.00%）. The enterotoxin gene carriage rate was 32.95% in food strains. The carriage rates for sea， seb， and sec were 7.95%， 12.50%， and 14.77%， respectively. Neither sed nor see was detected. The detection rate of strains carrying two types of enterotoxin genes was 2.27%. The enterotoxin carriage rates in strains from vegetables， beverages， and raw meat were 57.14%， 40.00%， and 30.00%， respectively. ConclusionThe S. aureus detection rate in food in Jiading District is much higher than the national average. The enterotoxin gene carriage rates are high， with food strains carrying sea， seb， and sec， with sec being the most prevalent. There is a need to enhance monitoring of S. aureus and enterotoxins， especially in high-risk foods such as noodles， vegetables， and non-packaged beverages.