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 clinical effect of superficial temporal artery-middle cerebral artery anastomosis(STA-MCA)in the treatment of patients with occlusive cerebrovascular disease.Methods A total of 74 patients with occlusive cerebrovascular disease admitted to our hospital were included and divided into the observation group and control group according to the random number table method,with 37 cases in each group.Patients in the control group received conservative treatment,and patients in the observation group received STA-MCA.After 3 months of follow-up,the cerebral blood flow indexes(including cerebral blood flow of anterior cerebral artery,and peak time)before treatment and 3rd day,1st month and 3rd month after treatment were observed,the modified Rankin scores before treatment and 3rd day and 1 month after treatment were recorded,and the revascularization and occurrence of complications after treatment were recorded.Results At 1 month and 3 months after treatment,the cerebral blood flow of anterior cerebral artery in the two groups increased and the peak time was shortened,and the cerebral blood flow of anterior cerebral artery in the observation group was higher than that in the control group,and the peak time was shorter than that in the control group,with statistically significant differences(P<0.05).The modified Rankin scores of the two groups 1 month after treatment were lower compared with those before treatment,and the modified Rankin score of the observation group was lower than that of the control group,with statistically significant differences(P<0.05).At 1 month after treatment,the proportions of patients with grades 0 and 1 of vascular reconstruction in the observation group were lower than those in the control group,and the proportions of patients with grades 2 and 3 were higher than those in the control group,with statistical significant differences(P<0.05).At 3 months after treatment,the proportions of patients with grades 0 and 1 of vascular reconstruction in the observation group were lower than those in the control group,and the proportion of patients with grade 3 of vascular reconstruction was higher than that in the control group,with statistically significant differences(P<0.05).There was no statistically significant difference in the total incidence of complications after treatment between the two groups(P>0.05).Conclusion STA-MCA has a good clinical effect in the treatment of patients with occlusive cerebrovascular disease,which is conducive to improving the cerebral blood flow indexes and promoting the recovery of neurological function and vascular reconstruction,with safety and reliability.

Objective:To investigate the clinical value immature granulocyte percentage(IG％)and other inflammatory indicators in the severity of acute appendicitis.Methods:A total of 201 pediatric patients undergoing appendicitis surgery admitted to Zhoukou Central Hospital from June 2022 to August 2023 were included.Patients with pathologically confirmed actue appendici-tis were divided two subgroups:actue simple appendicitis(ASA)group and actue complicated ap-pendicitis(ACA)group,The variables that included IG％,white blood cell(WBC)count,absolute neutrophil count(ANC),absolute lymphocyte count(ALC),neutrophil to lymphocyte ratio(NLR),pro-calcitonin(PCT),C-reactive protein(CRP),platelet to lymphocyte(PLR)and other indexes were ana-lyzed between ASA and ACA group.The logistic regression model for diagnosis of ACA was es-tablished,and the diagostic value of this model and other inflammtory indicators for ACA was evaluated by receiver operating characteristic(ROC)curve analysis.Results:The levels of IG％,WBC,ANC,ALC,NLR,PCT and PLR were higher and the level of ALC was lower in ACA group than those in ASA group(all P＜0.05).Logistic regression analysis showed that IG％,NLR and CRP were three diagnostic determinants of ACA(all P＜0.05).The AUC of the established logistic model and IG％,NLR,CRP were 0.868,0.821,0.691 and 0.790(all P＜0.001).The logistic model was vali-dated by independent cohorts,and the AUC was 0.872,the sensitivity was 90.0％and the speci-ficity was 75.6％.Conlusions:The IG％value can early indicator for pediatric ACA,and the es-tablished logistic regression model based on biomarkers including IG％,NLR and CRP has clinical value in diagnosing ACA in children.

Objective: To elucidate the effects of chlorogenic acid (CGA), a bioactive polyphenol compound prevalent in traditional Chinese medicine and various foods, including Lonicera japonica Thunb. (Jin Yin Hua), Eucommia ulmoides Oliv. (Du Zhong Ye), tea, and coffee, on cardiomyocyte ferroptosis and heart failure. Methods: We assessed the effect of CGA on cardiac function using a mouse model of heart failure induced by transverse aortic constriction (TAC). These indicators included the left ventricular ejection fraction (LVEF), fractional shortening (LVFS), end-systolic volume (LVESV), end-diastolic volume (LVEDV), end-systolic diameter (LVESD), and end-diastolic diameter (LVEDD). An isoprenaline hydrochloride (ISO)-induced H9c2 cardiomyocyte cell model was also established, and the cells were treated with various concentrations of CGA. To assess the effect of CGA on ferroptosis in cardiomyocytes, we measured cell viability and evaluated the levels of intracellular reactive oxygen species (ROS), ferrous ions (Fe2+), and lipid peroxidation using fluorescent staining. To clarify the ferroptosis signaling pathway regulated by CGA, western blotting was used to examine the expression of ferroptosis biomarkers, specifically solute carrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4), in H9c2 cardiomyocytes and mouse myocardial tissues. Results: CGA significantly enhanced cardiac performance indices such as LVEF, LVFS, LVESV, LVEDV, LVESD, and LVEDD. H9c2 cardiomyocytes exposed to ISO showed decreased cell viability and increased ROS levels, Fe2+ content, and lipid peroxidation levels. However, CGA treatment significantly ameliorated these changes. Additionally, in both H9c2 cardiomyocytes and myocardial tissue obtained from mice with TAC, CGA increased the expression of ferroptosis-related proteins, including SLC7A11 and GPX4. Conclusion CGA has the potential to enhance cardiac function and diminish lipid peroxidation and ROS levels in cardiomyocytes via the SLC7A11/GPX4 signaling pathway. This process alleviates ferroptosis in cardiomyocytes. These results provide new insights into the clinical use of CGA and the management of heart failure.

Objective To explore the influencing factors of mild cognitive impairment(MCI)in patients with atrial fibrillation and diabetes mellitus,and to establish the prediction model,so as to provide guidance for the treatment of MCI in patients with atrial fibrillation and diabetes mellitus.Methods 199 patients with atrial fibrillation and diabetes diagnosed in the second ward of Cardiovascular Department of the Fifth Affiliated Hospital of Zhengzhou University from January 2023 to January 2024 were analyzed.The related factors of MCI in patients with atrial fibrillation and diabetes mellitus were analyzed by univariate analysis and multivariate logistic regres-sion.According to the results of multivariate logistic regression analysis,the prediction model of MCI in patients with atrial fibrillation and diabetes mellitus was established.Results Univariate analysis showed that age(P=0.002 3),homocysteine(P＜0.000 1),fasting blood glucose(P=0.022 5),glycated hemoglobin(P=0.006 6),and blood uric acid(P=0.032 2)were the influencing factors of MCI.Multivariate logistic regression analysis:age(OR=1.08,P=0.000 4),homocysteine(OR=1.37,P＜0.000 1),fasting blood glucose(OR=1.22,P=0.023 5),glycated hemoglobin(OR=1.61,P=0.004 2),and blood uric acid(OR=1.29,P=0.009 1)were the independent influencing factors of MCI.The optimal threshold is when the Youden index(YI=sensitivity+speci-ficity)is maximum.At the optimal threshold,the sensitivity was 0.74,the specificity was 0.80,and the area under the curve(AUC)was 0.809,indicating that the model can effectively predict the occurrence of MCI.Conclusion Age,fasting blood glucose,blood homocysteine,blood uric acid and glycosylated hemoglobin are independent risk factors for MCI in patients with atrial fibrillation and diabetes.The clinical prediction model based on multivariate logistic regression has a certain predictive value for the occurrence of MCI in patients with atrial fibrillation and diabetes mellitus.

As the disease with high morbidity and mortality in the world, heart failure affects the development of human society. Due to its complicated pathology and limited treatment options, it is urgent to discover new disease targets and develop new treatment strategies. As innate immune cells accompanied by the evolution of heart failure, macrophages play an important role in cardiac homeostasis and stress. In recent years, the role of macrophages in the heart has attracted more and more attention as a potential target for heart failure intervention, and the research on cardiac macrophages has made important progress. Traditional Chinese medicine(TCM) has significant effects on regulating inflammatory response, treating heart failure, and maintaining homeostasis. In this article, researches on the functions of cardiac macrophages and application of TCM were reviewed from the source and classification of cardiac macrophages and the relationship of macrophages and cardiac inflammation, myocardial fibrosis, cardiac angiogenesis, and cardiac electrical conduction, which provided a basis for further basic research and clinical applications.
Humans ; Medicine, Chinese Traditional ; Heart Failure/drug therapy* ; Macrophages ; Drugs, Chinese Herbal/therapeutic use*