Objective:To investigate the clinical characteristics and hormonal changes in patients with adrenocorticotropic hormone(ACTH)-suppressed and non-suppressed subclinical Cushing′s syndrome(SCS), to evaluate the influencing factors of ACTH suppression, and to assess the diagnostic efficiency of serum dehydroepiandrosterone sulfate(DHEAS) levels in distinguishing these two groups of SCS patients.Methods:Clinical data of patients diagnosed with SCS in the Department of Endocrinology, Drum Tower Hospital Affiliated to Nanjing University Medical College, between June 2014 and October 2023 were retrospectively collected. A total of 194 cases were included. According to morning(8: 00 AM) plasma ACTH levels, patients were divided into an ACTH-suppressed group(ACTH<2.2 pmol/L) and a non-suppressed group(ACTH≥2.2 pmol/L). Additionally, 194 gender-, age-, and BMI-matched patients with non-functional adrenal tumors(NFA) were enrolled as controls. Clinical characteristics and hormone levels were compared between groups. Logistic regression analysis was performed to identify factors influencing ACTH suppression in SCS patients. Furthermore, receiver operating characteristic(ROC) curve analysis was conducted to evaluate the diagnostic performance of serum DHEAS levels in distinguishing ACTH-suppressed and non-suppressed SCS patients. Results:There were no significant differences in the prevalence of overweight/obesity, hypertension, abnormal glucose metabolism, or bone metabolism disorders between the ACTH-suppressed and non-suppressed groups. The serum cortisol level after the 1 mg-dexamethasone suppression test(DST) was significantly lower in the ACTH non-suppressed group than that in the suppressed group, while the serum DHEAS level was significantly higher in the non-suppressed group(both P<0.01). The area under the curve(AUCs) of serum DHEAS for diagnosing ACTH non-suppressed SCS patients and ACTH-suppressed SCS patients was 0.779(95% CI 0.721-0.837) and 0.874(95% CI 0.831-0.918), respectively. Using a serum DHEAS cutoff of 60.0 μg/dL, the sensitivity and specificity for diagnosing ACTH non-suppressed SCS patients were 66.7% and 76.1%, respectively, while for ACTH-suppressed SCS patients, the sensitivity and specificity were 84.9% and 75.5%, respectively. Conclusion:There were no significant differences in metabolic characteristics between ACTH-suppressed and non-suppressed SCS patients. Serum cortisol level after 1 mg-DST is an independent influencing factor for ACTH suppression status. Low serum DHEAS level serves as a sensitive diagnostic marker for SCS and also demonstrates diagnostic value in ACTH non-suppressed SCS patients.

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.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To investigate the relationship between nerve growth factor (NGF) concentration in follicular fluid and abnormal menstrual cycle in infertile patients with polycystic ovary syndrome (PCOS).Methods:A retrospective cohort study was conducted on 100 infertile patients with PCOS who underwent in vitro fertilization and embryo transfer (IVF-ET) at Department of Obstetrics and Gynecology, Peking University Third Hospital from March 2017 to June 2019. For comparison, the 100 patients with PCOS were divided into low NGF group ( n=50) and high NGF group ( n=50) based on the median NGF concentration (1 644.03 ng/L) in follicular fluid. Baseline characteristics, menstrual status and clinical outcomes of assisted reproductive technology were compared. We performed multiple linear regression analysis to examine the effect of NGF in follicular fluid on menstrual cycle length for multivariate analysis. Results:1) PCOS patients in the low NGF group had significantly higher body mass index [(27.24±5.17) kg/m 2] and white blood cell count [7.31(5.99, 8.43)×10 9/L ] than those in the high NGF group [(25.03±4.46) kg/m 2, P=0.024; 5.95(5.08,7.01)×10 9/L, P=0.001], while high-density lipoprotein cholesterol [1.15 (0.98, 1.36) mmol/L] and basic follicle-stimulating hormone level [6.51 (5.10,7.95) U/L] in the low NGF group were significantly lower than those in the high NGF group [1.36 (1.09,1.52) mmol/L, P=0.039;6.51 (5.10,7.95)U/L, P=0.040]. 2) PCOS patients in the low NGF group had significantly higher menstrual cycle length [60.00 (35.00, 180.00) d] than the high NGF group [32.50 (27.00,67.50) d, P=0.001]. 3) Multiple linear regression analysis revealed that after adjustment for body mass index, age, infertility duration, infertility type, and glucose and lipid metabolic parameters, the NGF concentration in the follicular fluid independently and negatively correlated with menstrual cycle length ( P<0.05). 4) The NGF concentration in follicular fluid was not correlated with assisted reproductive outcomes. Conclusion:NGF concentration in follicular fluid is closely related to the degree of menstrual cycle abnormalities in patients with PCOS.

Objective:To investigate the relationship between nerve growth factor (NGF) concentration in follicular fluid and abnormal menstrual cycle in infertile patients with polycystic ovary syndrome (PCOS).Methods:A retrospective cohort study was conducted on 100 infertile patients with PCOS who underwent in vitro fertilization and embryo transfer (IVF-ET) at Department of Obstetrics and Gynecology, Peking University Third Hospital from March 2017 to June 2019. For comparison, the 100 patients with PCOS were divided into low NGF group ( n=50) and high NGF group ( n=50) based on the median NGF concentration (1 644.03 ng/L) in follicular fluid. Baseline characteristics, menstrual status and clinical outcomes of assisted reproductive technology were compared. We performed multiple linear regression analysis to examine the effect of NGF in follicular fluid on menstrual cycle length for multivariate analysis. Results:1) PCOS patients in the low NGF group had significantly higher body mass index [(27.24±5.17) kg/m 2] and white blood cell count [7.31(5.99, 8.43)×10 9/L ] than those in the high NGF group [(25.03±4.46) kg/m 2, P=0.024; 5.95(5.08,7.01)×10 9/L, P=0.001], while high-density lipoprotein cholesterol [1.15 (0.98, 1.36) mmol/L] and basic follicle-stimulating hormone level [6.51 (5.10,7.95) U/L] in the low NGF group were significantly lower than those in the high NGF group [1.36 (1.09,1.52) mmol/L, P=0.039;6.51 (5.10,7.95)U/L, P=0.040]. 2) PCOS patients in the low NGF group had significantly higher menstrual cycle length [60.00 (35.00, 180.00) d] than the high NGF group [32.50 (27.00,67.50) d, P=0.001]. 3) Multiple linear regression analysis revealed that after adjustment for body mass index, age, infertility duration, infertility type, and glucose and lipid metabolic parameters, the NGF concentration in the follicular fluid independently and negatively correlated with menstrual cycle length ( P<0.05). 4) The NGF concentration in follicular fluid was not correlated with assisted reproductive outcomes. Conclusion:NGF concentration in follicular fluid is closely related to the degree of menstrual cycle abnormalities in patients with PCOS.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To investigate the clinical characteristics and hormonal changes in patients with adrenocorticotropic hormone(ACTH)-suppressed and non-suppressed subclinical Cushing′s syndrome(SCS), to evaluate the influencing factors of ACTH suppression, and to assess the diagnostic efficiency of serum dehydroepiandrosterone sulfate(DHEAS) levels in distinguishing these two groups of SCS patients.Methods:Clinical data of patients diagnosed with SCS in the Department of Endocrinology, Drum Tower Hospital Affiliated to Nanjing University Medical College, between June 2014 and October 2023 were retrospectively collected. A total of 194 cases were included. According to morning(8: 00 AM) plasma ACTH levels, patients were divided into an ACTH-suppressed group(ACTH<2.2 pmol/L) and a non-suppressed group(ACTH≥2.2 pmol/L). Additionally, 194 gender-, age-, and BMI-matched patients with non-functional adrenal tumors(NFA) were enrolled as controls. Clinical characteristics and hormone levels were compared between groups. Logistic regression analysis was performed to identify factors influencing ACTH suppression in SCS patients. Furthermore, receiver operating characteristic(ROC) curve analysis was conducted to evaluate the diagnostic performance of serum DHEAS levels in distinguishing ACTH-suppressed and non-suppressed SCS patients. Results:There were no significant differences in the prevalence of overweight/obesity, hypertension, abnormal glucose metabolism, or bone metabolism disorders between the ACTH-suppressed and non-suppressed groups. The serum cortisol level after the 1 mg-dexamethasone suppression test(DST) was significantly lower in the ACTH non-suppressed group than that in the suppressed group, while the serum DHEAS level was significantly higher in the non-suppressed group(both P<0.01). The area under the curve(AUCs) of serum DHEAS for diagnosing ACTH non-suppressed SCS patients and ACTH-suppressed SCS patients was 0.779(95% CI 0.721-0.837) and 0.874(95% CI 0.831-0.918), respectively. Using a serum DHEAS cutoff of 60.0 μg/dL, the sensitivity and specificity for diagnosing ACTH non-suppressed SCS patients were 66.7% and 76.1%, respectively, while for ACTH-suppressed SCS patients, the sensitivity and specificity were 84.9% and 75.5%, respectively. Conclusion:There were no significant differences in metabolic characteristics between ACTH-suppressed and non-suppressed SCS patients. Serum cortisol level after 1 mg-DST is an independent influencing factor for ACTH suppression status. Low serum DHEAS level serves as a sensitive diagnostic marker for SCS and also demonstrates diagnostic value in ACTH non-suppressed SCS patients.

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.

Objective:To evaluate the efficacy and safety of denosumab in the treatment of prostate cancer with bone metastases.Methods:Relevant studies were retrieved from PubMed,EMBASE,Cochrane,Web of Science,Sinomed,CNKI and Wanfang data-bases.The Cochrane risk-of-bias assessment tool was used to evaluate the quality of included studies,and relevant data were extracted.meta-analysis was performed using RevMan 5.4 and RStudio software,and forest plots were generated.Results:Six randomized con-trolled trials(RCTs)were included.Compared with the control group,denosumab significantly reduced the risk of skeletal-related e-vents(HR=0.78,95％CI:0.62-0.93).In terms of safety,denosumab did not increase the risk of total adverse events,severe adverse events and the adverse events higher than CTC grade 3.Conclusion:Denosumab can delay the time to first skeletal-related event with good safety.However,due to the limitations of this study,further high-quality,large-sample,multicenter RCTs are needed to confirm these findings.

The development, progression, and curative efficacy of head and neck squamous cell carcinoma (HNSCC) are influenced by complex interactions between epithelial and immune cells. Nevertheless, the specific changes in the nature of these interactions and their underlying molecular mechanisms in HNSCC are not yet fully understood. Cuproptosis, a form of programmed cell death that is dependent on copper, has been implicated in cancer pathogenesis. However, the understanding of cuproptosis in the context of HNSCC remains limited. In this study, we have discovered that cuproptosis-related long non-coding RNAs (CRLs) known as JPX play a role in promoting the expression of the oncogene urokinase-type plasminogen activator (PLAU) by competitively binding to miR-193b-3p in HNSCC. The increased activity of the JPX/miR-193b-3p/PLAU axis in malignant epithelial cells leads to enhanced cell proliferation, migration, and invasion in HNSCC. Moreover, the overexpression of PLAU in tumor epithelial cells facilitates its interaction with the receptor PLAUR, predominantly expressed on macrophages, thereby influencing the abnormal epithelial-immune interactome in HNSCC. Notably, the JPX inhibitor Axitinib and the PLAU inhibitor Palbociclib may not only exert their effects on the JPX/miR-193b-3p/PLAU axis that impacts the malignant tumor behaviors and the epithelial-immune cell interactions but also exhibit synergistic effects in terms of suppressing tumor cell growth and arresting cell cycle by targeting epidermal growth factor receptor (EGFR) and cyclin-dependent kinase (CDK4/6) for the treatment of HNSCC.
Humans ; MicroRNAs/metabolism* ; RNA, Long Noncoding/metabolism* ; Head and Neck Neoplasms/metabolism* ; Cell Proliferation ; Squamous Cell Carcinoma of Head and Neck/genetics* ; Urokinase-Type Plasminogen Activator/genetics* ; Cell Movement ; Cell Line, Tumor ; Gene Expression Regulation, Neoplastic ; Carcinoma, Squamous Cell/genetics* ; Neoplasm Invasiveness