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 mechanism of gossypol acetic acid （GAA） in the treatment of uterine fibroids. METHODS Human leiomyoma cells SK-UT-1 were selected as objects to investigate the effects of different concentrations （5， 10， 20， 40， 80， 160 μmol/L） of GAA on the activities of cell proliferation. 4D-DIA proteomic detection and bioinformatics analysis were carried out to screen differential proteins. Gene ontology （GO） analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） signaling pathway analysis were performed. The expressions of top 3 proteins [N-myc downstream regulated gene 1 （NDRG1）， epidermal growth factor receptor feedback inhibitor 1 （ERRFI1）， CXC chemokine ligand 3 （CXCL3）] with differential fold changes in SK-UT-1 cells were determined. RESULTS 10-160 μmol/L GAA could significantly reduce the survival rate of SK- UT-1 cells （P＜0.05）. Proteomics results showed that a total of 921 differentially expressed proteins were obtained， including 254 up-regulated proteins and 667 down-regulated proteins. The differentially expressed proteins were mainly distributed in mitochondria， nucleus， extracellular matrix， etc. Bioinformatics results showed that differentially expressed proteins were mainly involved in signaling pathways such as PI3K/AKT （phosphoinositide 3-kinase/protein kinase B）， MAPK （mitogen-activated protein kinase）， TNF （tumor necrosis factor）， etc.， which mainly involved cell apoptosis， aging， and movement. GAA significantly decreased protein expressions of NDRG1 and CXCL3 （P＜0.05）， but increased protein expression of ERRFI1 （P＜0.05）. CONCLUSIONS The improvement effect of GAA on uterine fibroids may involve signaling pathways such as PI3K/AKT， MAPK， TNF， etc. It can improve the occurrence and development of uterine fibroids by downregulating the expressions of NDRG1 and CXCL3 proteins， upregulating the expression of ERRFI1 protein， and affecting the proliferation and apoptosis of uterine fibroid cells.

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.

ObjectiveAccording to the etiology， pathogenesis， and clinical characteristics of dry eye （DE）， this paper aims to analyze existing DE animal models to provide recommendations for building more clinically relevant DE models that integrate traditional Chinese and Western medicine. MethodsBy the retrieval and analysis of relevant literature on DE animal experiments， combined with expert consensus， an evaluation scale was created to assess relevance from the perspectives of pathogenesis， diagnostic criteria， and traditional Chinese medicine （TCM） differentiation. On the basis of data provided by the literature， the clinical relevance was evaluated for the animal models constructed in the literature. ResultsAmong the existing methods for establishing a DE animal model， benzalkonium chloride eye-drop induction showed the highest clinical relevance， demonstrating 98% alignment with Western medicine. However， current models generally showed higher relevance to Western medicine than to TCM， and there was a lack of models integrating disease with syndrome. ConclusionAs DE involves diverse causes and pathogenesis， single-factor models cannot fully simulate the complex pathology of DE. Future research should focus on building multi-mechanism DE models， exploring new etiological directions， standardizing model evaluation systems， and promoting integration of traditional Chinese and Western medicine. This will help precisely simulate the pathophysiological process of human DE and provide more valuable guidance for clinical diagnosis and treatment， ultimately enhancing patient outcomes and satisfaction.

Objective:To establish the "1+3" management module of high-value consumables in Operation Room and verify its application, so as to provide new ideas for cost management of consumables in Operation Room.Methods:The Operating Room team of Shenzhen Hospital of Southern Medical University designed a "1+3" management module in 2022, where "1" referred to the management process of high-value consumables in Operation Room, and "3" referred to the precise management of consumables in Operation Room warehouse, the management of closed-loop use of Operation Room consumables and adverse event management of consumables. Surgeries using high-value consumables in the Thoracic Surgery Department, Gastrointestinal Surgery Department, and Urology Department of the hospital were selected as the research objects. The surgeries using conventional consumables from January to June 2022 were set as the control group, and the surgeries implementing the "1+3" management module from July to December 2022 were set as the observation group. The number of consumables received by the itinerant nurses before the operation and the number of high-value consumables returned after the operation were compared between the two groups. And the number of missed and error charges for high-value consumables in the two groups were counted and compared.Results:The number of consumables received before operation in the control group was higher than that in the observation group, and the difference was statistically significant ( P<0.05). The number of high-value consumables returned in the observation group was less than that in the control group, and the difference was statistically significant ( P<0.01). The proportion of missed charges for consumables in the observation group was lower than that in the control group, and the difference was statistically significant ( P<0.01), but there was no statistically significant difference in the proportion of incorrect charges between the two groups ( P>0.05) . Conclusions:The "1+3" management module for high-value consumables in Operation Room makes the process of receiving, returning, and charging high-value consumables clear, with traceable data, achieving refined management of high-value consumables in Operation Room, reducing the number of high-value consumables returned to the warehouse and reducing the proportion of missed consumables, which is conducive to effective cost control in Operation Room.

Objective:To analyze the immune reconstitution after BTKi treatment in patients with chronic lymphocytic leukemia(CLL).Methods:The clinical and laboratorial data of 59 CLL patients admitted from January 2017 to March 2022 in Fujian Medical University Union Hospital were collected and analyzed retrospectively.Results:The median age of 59 CLL patients was 60.5(36-78).After one year of BTKi treatment,the CLL clones(CD5+/CD19+)of 51 cases(86.4％)were significantly reduced,in which the number of cloned-B cells decreased significantly from(46±6.1)× 109/L to(2.3±0.4)× 109/L(P=0.0013).But there was no significant change in the number of non-cloned B cells(CD19+minus CD5+/CD19+).After BTKi treatment,IgA increased significantly from(0.75±0.09)g/L to(1.31±0.1)g/L(P＜0.001),while IgG and IgM decreased from(8.1±0.2)g/L and(0.52±0.6)g/L to(7.1±0.1)g/L and(0.47±0.1)g/L,respectively(P＜0.001,P=0.002).BTKi treatment resulted in a significant change in T cell subpopulation of CLL patients,which manifested as both a decrease in total number of T cells from(2.1±0.1)× 109/L to(1.6±0.4)× 109/L and NK/T cells from(0.11±0.1)× 109/L to(0.07±0.01)× 109/L(P=0.042,P=0.038),both an increase in number of CD4+cells from(0.15±6.1)× 109/L to(0.19±0.4)× 109/L and CD8+cells from(0.27±0.01)× 109/L to(0.41±0.08)× 109/L(both P＜0.001).BTKi treatment also up-regulated the expression of interleukin(IL)-2 while down-regulated IL-4 and interferon(IFN)-γ.However,the expression of IL-6,IL-10,and tumor necrosis factor(TNF)-α did not change significantly.BTKi treatment could also restored the diversity of TCR and BCR in CLL patients,especially obviously in those patients with complete remission(CR)than those with partial remission(PR).Before and after BTKi treatment,Shannon index of TCR in patients with CR was 0.02±0.008 and 0.14±0.001(P＜0.001),while in patients with PR was 0.01±0.03 and 0.05±0.02(P＞0.05),respectively.Shannon index of BCR in patients with CR was 0.19±0.003 and 0.33±0.15(P＜0.001),while in patients with PR was 0.15±0.009 and 0.23±0.18(P＜0.05),respectively.Conclusions:BTKi treatment can shrink the clone size in CLL patients,promote the expression of IgA,increase the number of functional T cells,and regulate the secretion of cytokines such as IL-2,IL-4,and IFN-γ.BTKi also promote the recovery of diversity of TCR and BCR.BTKi treatment contributes to the reconstitution of immune function in CLL patients.

Objectives:To understand the current situation of the sense of uselessness of the elderly in China and the impact of Internet use on the sense of uselessness of the elderly,exploring the moderating role of age and social participation in it,so as to provide empirical evidence for improving the mental health of the elderly and promoting active aging.Method:T-test and analysis of variance were used for univariate analysis,multiple linear regression was used for multivariate analysis,and point selection and Johnson Neyman methods were used to analyze the moderating effects of age and social participation.Results:At present,the level of the sense of uselessness of the elderly in China is relatively high.Internet use affects the sense of uselessness of the elderly(b=-0.10,P<0.001);With the increase of age and the level of social participation,the alleviating effect of Internet use on the sense of uselessness of the elderly has gradually weakened(b=0.01,P<0.05;b=0.02,P<0.01).Internet use can alleviate the sense of uselessness of the elderly and is regulated by age and social participation.It is suggested that the Internet access intervention should be carried out as soon as possible for the elderly at a lower age,and the elderly with a lower level of social participation should be focused,so as to play the positive role of the Internet in reducing the sense of uselessness of the elderly and improving their mental health.