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 analyze the role of the key items list in the quality control of running medical records by com-paring the improvements of the quality of medical records,and to explore more effective ways of quality management of medical records.Methods Based on the goal setting theory and influencing factors of goal incentive utility,a list of key items for medi-cal record quality management was formulated,which was implemented in a children's hospital in Tianjin since April 2023.A to-tal of 4 823 operating medical records were collected from June 2022 to September 2023,and the defect rates of quality control items were compared by using statistical methods.Results After the implementation of the key items list,the average score of operating medical records was improved,and the defect rate decreased from 13.63％to 7.94％.Moreover,the defect rates of admission records,first-trip records,senior physician rounds records,consultation records,and surgical records decreased from 4.25％,3.07％,8.26％,and 10.56％to 1.61％,1.56％,4.41％,and 5.06％.Conclusion The implementation of key i-tems list management can effectively improve the quality of running medical records,reduce the defects of medical records,and improve the effects of medical record management.

OBJECTIVE To obtain stronger cytotoxic activity of panaxadiol derivatives. METHODS The 3-amino panaxadiol was prepared by the bioelectronic isosteric principle, and then 18 derivatives of cinnamic acid, NO donor and other types of panaxadiol derivatives were synthesized, among them, 12 compounds had not been reported in the literature, and their structures had been confirmed by 1H-NMR, 13C-NMR and mass spectrometry. These compounds were evaluated for their cytotoxic activity by MTS assay against human leukemia cell line HL-60, liver cancer cell line SMMC-7721, lung cancer cell line A-549, breast cancer cell line MCF-7, and colon cancer cell line SW480. RESULTS These results showed that compounds 6c, 7 as well as 7j exhibited potent inhibitory activities against all five tumor cells, especially the IC50 values of compound 7 against HL-60 and SMMC-7721cells were 3.41 and 4.51 μmol·L−1, respectively. It was significantly superior to panaxadiol in cytotoxicity. CONCLUSION These results show that 7 and 7j can be used as promising lead compounds for further research.

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.