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.

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.

Pyroptosis is the programmed death of cells accompanied by an inflammatory response and is widely involved in the development of a variety of diseases, such as infectious diseases, cardiovascular diseases, and neurodegeneration. It has been shown that cellular scorching is involved in the pathogenesis of pulmonary arterial hypertension ( PAH) in cardiovascular diseases. Patients with PAH have perivascular inflammatory infiltrates in lungs, pulmonary vasculopathy exists in an extremely inflam-matory microenvironment, and pro-inflammatory factors in cellular scorching drive pulmonary vascular remodelling in PAH patients. This article reviews the role of cellular scorch in the pathogenesis of PAH and the related research on drugs for the treatment of PAH, with the aim of providing new ideas for clinical treatment of PAH.

BACKGROUND:Diabetic foot patients with wound infections constitute a large patient population,and there is currently no satisfactory treatment approach. OBJECTIVE:To investigate the clinical efficacy of a modified tibial cortex transverse transport combined with antibiotic-loaded bone cement for treating refractory diabetic foot ulcers. METHODS:A total of 46 diabetic foot ulcers patients,27 males and 19 females,with an average age of 64.37 years,were selected from Beijing Chaoyang Hospital,Capital Medical University and Beijing Chaoyang Integrative Medicine Rescue and First Aid Hospital from January 2020 to January 2023.All of them underwent the modified tibial cortex transverse transport combined with antibiotic-loaded bone cement treatment.Ankle-brachial index,WIFi(Wound/Ischemia/Foot infection)classification,pain visual analog scale score,and ulcer area were recorded before and 3 months after surgery. RESULTS AND CONCLUSION:(1)The mean ulcer healing time for the 46 patients was(58.07±24.82)days.At 3 months postoperatively,there were significant improvements in ankle-brachial index,pain visual analog scale score,ulcer area,and WIFi classification in 46 patients,as compared to the preoperative values,with statistically significant differences(P<0.05).Two patients experienced pin-tract infections,without infection or ulcer recurrence during the follow-up period.(2)These findings indicate that the modified tibial cortex transverse transport combined with antibiotic-loaded bone cement effectively alleviates patients'pain,improves lower limb circulation,controls infections,and promotes ulcer healing.

Pancreatic cancer is one of the most prevalent malignant tumors of the digestive system， and its incidence and mortality rates are increasing year by year. Most patients with pancreatic cancer are unable to receive surgery due to the advanced stage. Although chemotherapy regimens based on gemcitabine and fluorouracil have prolonged the survival time of patients to some extent， some patients cannot tolerate chemotherapy and hence lose the opportunity for treatment. With the advent of the era of precision medicine， molecular-targeted therapy has exhibited an excellent therapeutic efficacy and has thus become one of the most important treatment techniques for tumors； however， due to the high heterogeneity of pancreatic cancer and its complicated tumor microenvironment， molecular-targeted therapy for pancreatic cancer has not achieved notable results. Therefore， it is imperative to seek new therapeutic targets and medications to overcome this issue. This article reviews the latest advances in the research on molecular-targeted therapy for unresectable pancreatic cancer based on common molecular targets and tumor immunity-related therapeutic targets， in order to provide new treatment strategies for patients with pancreatic cancer.

Objective: To understand the distribution of tobacco retailer within 100 meters outside middle schools in Wuhan City and its impact on smoking behavior of middle school students, so as to provide basis and feasible suggestions for the development of tobacco control policy for adolescents. Methods: From February to May 2023, a multi stage stratified cluster random sampling method was used to select 20 middle schools from 4 districts in Wuhan City. To investigate the distribution of tobacco retailer within 100 metres outside the school and the sale of tobacco to minors. A total of 4 882 students were surveyed using the core questions of the 2021 Chinese Adolescent Tobacco Prevalence Questionnaire. Fisher exact probability test, Chi square test and Chi square trend test were used for statistical analysis. Results: Nearly 70.00% of middle schools had tobacco retailer within 100 metres, with an average of (1.10±0.97) per middle school. The awareness rate (100.00%) and labeling rate (87.50%) of licensed tobacco retailer were higher than those of non licensed tobacco retailer (33.33%, 16.67%) ( P <0.05). The rates of tried smoking, current smoking and buying cigarettes within 30 days were 7.13%, 1.99% and 2.54%, respectively. The rates of students who tried smoking ( 8.58 %), current smoking (2.29%) and buying cigarettes within 30 days (2.85%) in schools with tobacco retailer within 100 metres were higher than those in schools without tobacco retailer (3.79%, 1.28%, 1.83%)( χ 2=35.80, 5.37, 4.37 , P <0.05). And as the grade increased, the rates of tried smoking, current smoking and buying cigarettes among middle school students all showed an upward trend ( χ 2 trend =66.20, 36.10, 16.17, P <0.05). Conclusions Middle school students in Wuhan City have high tobacco availability. The findings suggest that school ban should be extended from 50 meters to 100 meters, and the regulatory authorities must strictly prohibit selling tobacco products to minors at tobacco retailer.