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 expressions of annexin A2 and glycogen synthesis kinase-3β(GSK-3β)in cutaneous squamous cell carcinoma(CSCC)tissues,and to analyze their correlation with CSCC as well as their clinical pathological diagnostic value.Methods The pathological tissues of 68 patients with CSCC and 40 patients with keratoacanthoma(KA)who underwent surgical treatment in the Department of Dermatology of the Second Hospital of Nanning from October 2020 to May 2024 were collected,and the surrounding normal skin tissues of 32 patients with benign skin diseases were used as controls.The expressions of annexin A2,GSK-3β and β-catenin were detected by immunohistochemistry and Western blotting.Spearman was used to evaluate the correlation between the expressions of annexin A2 and GSK-3β and the pathological characteristics in CSCC.The receiver operating characteristic(ROC)curve was drawn to analyze the clinical diagnostic value of annexin A2 and GSK-3β in CSCC.Results Compared with the normal skin tissues,the expressions of annexin A2 and β-catenin in CSCC increased,and GSK-3β decreased(P＜0.05);Compared with the KA tissues,the expression of annexin A2 in CSCC tissues increased(P＜0.05).The expression of annexin A2 was negatively correlated with that of GSK-3β in CSCC(r=-0.3901,P＜0.01).GSK-3β expression was related to tissue differentiation,with lower expression in poorly differentiated patients'cancer tissues(P＜0.05).The sensitivity of annexin A2 and GSK-3β for diagnosis of CSCC was 85.3%and 41.2%,respectively,with specificities of 46.9%and 84.4%respectively.The sensitivity of annexin A2 for distinguishing between CSCC and KA was 85.3%,with a specificity of 40.0%.Conclusion Annexin A2 and GSK-3β may be used as potential biomarkers for the early diagnosis or differential diagnosis of CSCC,and play important roles in the development of CSCC.Their mechanism may be related to the activation of Wnt/β-catenin signaling pathway.

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.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

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 value of intraoperative ultrasound radiomics for predicting isocitrate dehydrogenase 1(IDH1)mutation of high-grade glioma.Methods Ninety-five patients with high-grade glioma(WHO grade Ⅲ and Ⅳ)who underwent craniotomy glioma resection and ultrasound assisted tumor localization during operation and then confirmed by pathology were retrospectively enrolled.The patients were divided into training set(n=66,including 24 IDH1 mutation type and 42 IDH1 wild type)and validation set(n=29,including 11 IDH1 mutation type and 18 IDH1 wild type)at the ratio of 7∶3.Based on intraoperative ultrasound,radiomics features were extracted,the best ones were screened,and a radiomics model was established for predicting IDH1 mutation of high-grade glioma using random forest algorithm.Receiver operating characteristic(ROC)curve was plotted,the area under the curve(AUC)was calculated to evaluate the predictive efficacy of the model,and decision curve analysis(DCA)was used to evaluate the clinical value of the model.Results A total of 851 radiomics features were extracted based on intraoperative ultrasound,and finally 5 best ones were screened out to construct a radiomics model.The AUC of the radiomics model for predicting IDH1 mutation of high-grade glioma in training and validation sets was 0.902 and 0.707,respectively,with no significant difference(P=0.097).DCA maps showed that the clinical net benefit of the radiomics model was high.Conclusion Intraoperative ultrasound radiomics could effectively predict IDH1 mutation of high-grade glioma.

Objective To investigate the value of intraoperative ultrasound radiomics for predicting isocitrate dehydrogenase 1(IDH1)mutation of high-grade glioma.Methods Ninety-five patients with high-grade glioma(WHO grade Ⅲ and Ⅳ)who underwent craniotomy glioma resection and ultrasound assisted tumor localization during operation and then confirmed by pathology were retrospectively enrolled.The patients were divided into training set(n=66,including 24 IDH1 mutation type and 42 IDH1 wild type)and validation set(n=29,including 11 IDH1 mutation type and 18 IDH1 wild type)at the ratio of 7∶3.Based on intraoperative ultrasound,radiomics features were extracted,the best ones were screened,and a radiomics model was established for predicting IDH1 mutation of high-grade glioma using random forest algorithm.Receiver operating characteristic(ROC)curve was plotted,the area under the curve(AUC)was calculated to evaluate the predictive efficacy of the model,and decision curve analysis(DCA)was used to evaluate the clinical value of the model.Results A total of 851 radiomics features were extracted based on intraoperative ultrasound,and finally 5 best ones were screened out to construct a radiomics model.The AUC of the radiomics model for predicting IDH1 mutation of high-grade glioma in training and validation sets was 0.902 and 0.707,respectively,with no significant difference(P=0.097).DCA maps showed that the clinical net benefit of the radiomics model was high.Conclusion Intraoperative ultrasound radiomics could effectively predict IDH1 mutation of high-grade glioma.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.