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.

Effective annotation of in vivo drug metabolites using liquid chromatography-mass spectrometry (LC-MS) remains a formidable challenge. Herein, a metabolic reaction-based molecular networking (MRMN) strategy is introduced, which enables the "one-pot" discovery of prototype drugs and their metabolites. MRMN constructs networks by matching metabolic reactions and evaluating MS² spectral similarity, incorporating innovations and improvements in feature degradation of MS² spectra, exclusion of endogenous interference, and recognition of redundant nodes. A minimum 75% correlation between structural similarity and MS² similarity of neighboring metabolites was ensured, mitigating false negatives due to spectral feature degradation. At least 79% of nodes, 49% of edges, and 97% of subnetworks were reduced by an exclusion strategy of endogenous ions compared to the Global Natural Products Social Molecular Networking (GNPS) platform. Furthermore, an approach of redundant ions identification was refined, achieving a 10%-40% recognition rate across different samples. The effectiveness of MRMN was validated through a single compound, plant extract, and mixtures of multiple plant extracts. Notably, MRMN is freely accessible online at https://yaolab.network, broadening its applications.

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.

Objective:To provide reference for hospital drug selection and clinical rational drug selection,through evaluating the nutritional value of six commonly used balanced compound amino acid injection (BCAA) in clinical practice,including 18AA (250 mL:12.5 g),18AA-I (250 mL:17.5 g),18AA-Ⅱ(250 mL:21.25 g),18AA-IV (250 mL:8.7 g),18AA-V (250 mL:8.06 g),and 18AA-V-SF (250 mL:8.06 g). Methods:Based on the whole egg protein model,the nutritional value of six varieties of BCAA from two aspects were evaluated,including the first limiting amino acid chemical score (CS),value of essential amino acid (EAA) and the comprehensive quality of total EAA (both essential amino acid index and closeness to standard protein). Results:The first limiting amino acid CS value from high to low was 18AA-Ⅱ>18AA>18AA-V=18AA-V-SF>18AA-I=18AA-Ⅳ. Total EAA comprehensive quality:the essential amino acid index from high to low was 18AA-Ⅱ>18AA>18AA-I>18AA-Ⅳ>18AA-V=18AA-V-SF. The closeness to whole egg protein from high to low was 18AA-Ⅱ=18AA=18AA-I>18AA-Ⅳ>18AA-V=18AA-V-SF. Ultimately,the nutritional value of the 6 varieties of BCAA decreased from high to low:18AA-Ⅱ>18AA>18AA-I>18AA-Ⅳ>18AA-V=18AA-V-SF. Conclusions:Among the six varieties of BCAA,18AA-Ⅱ has the highest nutritional value and the highest amino acid content in the same liquid volume,making it the preferred drug for patients with normal liver and kidney function.

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.

Objective To investigate the prevalence of tension-type headache(TTH)in children and adolescents aged 0-19 years globally in 1990-2021,and to provide a basis for the prevention and treatment of TTH.Methods Based on the Global Burden of Disease Study data,the age-standardized prevalence distribution of TTH and its changing trend were analyzed among the children and adolescents aged 0-19 years,with different sexes,age groups,sociodemographic index(SDI)regions and countries/territories.Results The age-standardized prevalence rate(ASPR)of TTH in children and adolescents aged 0-19 globally in 2021 was 17 339.89/100 000,which was increased by 1.73%since 1990.The ASPR in females was slightly higher than that in males(1990:17 707.65/100 000 vs 16 403.78/100 000;2021:17 946.29/100 000 vs 16 763.09/100 000).The ASPR in adolescence was significantly higher than that in school-aged and preschool periods(1990:27 672.04/100 000 vs 10 134.16/100 000;2021:28 239.04/100 000 vs 10 059.39/100 000).Regions with high SDI exhibited a higher ASPR than the other regions,with significant differences in prevalence rates across different countries.From 1990 to 2021,there was a slight increase in global ASPR,with an average annual percentage change(AAPC)of 0.06%.Females experienced a smaller increase than males based on AAPC(0.04%vs 0.07%).There was reduction in ASPR in preschool and school-aged groups,with an AAPC of-0.02%,while there was a significant increase in ASPR in adolescence,with an AAPC of 0.07%.ASPR decreased in regions with low-middle and low levels of SDI,with an AAPC of-0.02%and-0.04%,respectively,while it increased in regions with middle SDI,with an AAPC of 0.24%.Conclusions There is a consistent increase in the ASPR of TTH in children and adolescents aged 0-19 years globally,with significant differences across sexes,age groups,SDI regions and countries/territories.