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 understand the dilemma of active management in young and middle-aged postoperative patients with lumbar disc herniation, in order to provide reference for the formulation of active intervention program of self-health management meeting the needs of patients.Methods:Using the phenomenological research method, a total of 16 middle-aged and young patients with lumbar disc herniation were selected for semi-structured in-depth interviews, and the themes were analyzed and extracted by Colaizzi 7-step analysis method.Results:Three themes and nine sub-themes were extracted namely, inadequate disease response capacity and response resources (lack of disease symptom management capacity, limited access to disease management information, difficult use of disease management information, difficulty in maintaining positive self-management for a long time (forced to stop behavior change due to stress, lack of motivation to achieve good behavior change, doubts about the effectiveness of self-management) and multi-dimensional negative emotions (contrast between maintaining independence and dependence on others, fear of being misunderstood, fear of relapse) .Conclusions:Medical staff should provide comprehensive health information services for young and middle-aged postoperative patients with lumbar disc herniation, improve the out-of-hospital follow-up mechanism with the help of information technology, further improve family and social support strategies, reduce the difficulties faced in the process of active management and achieve long-term active disease management.

Objective:To investigate the correlation of peripheral blood T lymphocyte subsets with overall survival(OS)and clinical baseline characteristics in mantle cell lymphoma(MCL).Methods:The clinical data of 55 MCL patients who were newly diagnosed in the Department of Hematology,Second Hospital of Shanxi Medical University from January 2012 to July 2022 were analyzed retrospectively.The percentages of T lymphocyte subsets and CD4+/CD8+ratio in peripheral blood were detected by flow cytometry,and their correlation with clinical characteristics of patients were analyzed.Kaplan-Meier method was used for survival analysis and survival curves were drawn.Log-rank test was used for univariate analysis,while Cox proportional hazards model was used for multivariate analysis.Results:The median follow-up was 40(1-68)months,and the median overall survival(OS)was 47 months.Among the 55 patients,30(54.5％)patients had a decrease in peripheral blood CD4+T lymphocyte,while 17(30.9％)patients had a increase in peripheral blood CD8+T lymphocyte,and 20(36.4％)patients had a decrease in CD4+/CD8+ratio.There were no significant correlations between CD4+/CD8+ratio and sex,age,Ki-67,B symptoms,leukocytes,hemoglobin,lymphocytes,platelets,albumin,lactate dehydrogenase(LDH),β2-microglobulin,splenomegaly,bone marrow invasion,primary site and MIPI score.Survival analysis showed that patients with CD4+T cell＞23.3％,CD8+Tcell ≤33.4％and CD4+/CD8+ratio＞0.6 had longer OS(P=0.020,P＜0.001,P＜0.001).Univariate analysis showed that Ki-67＞30％,LDH＞250 U/L,splenomegaly,bone marrow involvement,CD4+T cells 23.3％,CD8+T cells＞33.4％,CD4+/CD8+ratio ≤0.6 were adverse prognostic factors affecting OS of MCL patients.Multivariate analysis showed that CD4+/CD8+ratio ≤0.6(HR=4.382,P=0.005)was an independent adverse prognostic factor for OS of MCL patients.Conclusions:Low CD4+/CD8+ratio is associated with poor prognosis in MCL,and the CD4+/CD8+ratio can be used as an important indicator to evaluate the prognosis risk in MCL patients.

Objective:To explore the quality of working life and its related factors in patients with breast canc-er returned to work after surgery.Methods:A total of 316 patients with breast cancer who had returned to work af-ter surgery were selected,and they were investigated with the General information questionnaire,Quality of Working Life Questionnaire for Cancer Survivors(QWLQ-CS),Breast Cancer Survivors Resilience Scale(BCRS)and Strat-egies Used by People to Promote Health(SUPPH).Multivariate linear regression analysis was used to analyze the related factors of quality of working life.Results:Multiple linear regression analysis showed that the QWLQ-CS score was positively correlated with education level,per capita monthly income of family,work pattern,return to work time,personal protection and social protection score of BCRS,and positive attitude scores of SUPPH(β=1.05,1.23,2.26,0.69,0.95,1.00,0.13),while negatively correlated with the complications(β=-3.83).Con-clusion:The quality of working life for patients with breast cancer after surgery needs to be improved after they re-turn to work,psychological resilience and self-efficacy are positively correlated with quality of working life.

Objective To investigate the status of thriving at work among junior nurses,and to analyze the influencing factors,so as to provide theoretical bases for promoting the job growth of junior nurses and improving the level of thriving at work.Methods From January to March 2023,431 junior nurses from 3 tertiary hospitals in Xinjiang Uygur Autonomous Region were selected as the research subjects.Questionnaire survey were conducted through the General Information Questionnaire,the Thriving at Work Scale,and the Job Crafting Scale.The univariate analysis and multiple linear regression were used to analyze the influencing factors of thriving at work among junior nurses.Results A total of 431 nurses with low seniority completed the survey.The total score of thriving at work is(35.46±6.74)score.Multiple linear regression analysis showed that age,education level and job remodeling score were the factors affecting the job prosperity of junior nurses(P＜0.05).Conclusion The thriving at work of junior nurses was at a moderate level.Nursing managers should strengthen the benign guidance of junior nurses,provide sufficient resource support,improve the level of job remodeling to promote the thriving at work of junior nurses and maintain the stability of organizational development.

Objective This study aims to elucidate the protective role of Acacetin against apoptosis in HP-infected GES-1 cells and to delineate its potential underlying mechanisms.Materials and Methods GES-1 cells were subjected to in vitro treatment with HP and Acacetin.Cell viability was assessed utilizing the CCK-8 assay,alterations in cell migration and healing capacities through the wound healing assay,rates of apoptosis via flow cytometry,and expression levels of apoptosis-associated proteins through western blot analysis.Results HP infection led to a diminution in GES-1 cell viability,a suppression of cell migration,an augmentation in the rate of apoptosis,and an increase in the expression levels of Bax and cle-caspase3.Conversely,treatment with Acacetin was found to enhance cell viability,mitigate apoptosis induced by HP infection,and modulate the expression of apoptosis proteins by downregulating Bax and cle-caspase3.Discussion and Conclusion Acacetin significantly improves GES-1 cell vitality and inhibits apoptosis in HP-infected GES-1 cells,thereby offering a protective effect on gastric mucosal epithelial cells.