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 construct a predictive model for high-grade pathological components of early invasive lung adenocarcinoma(ILAC)based on radiomics.Methods Collecting information on total 495 patients who underwent radical operation and were pathologically diagnosed as stage Ⅰ in the cardiothoracic surgery of Zhoushan Hospital from January 2015 to December 2019,including gender,age,pathological findings,tumor markers and preoperative chest CT images.The micropapillary and solid components in postoperative pathology were defined as"high-grade pathological components",while those without high-grade pathological components were classified into the low-grade group and those with high-grade pathological components were classified into the high-grade group.And patients were randomly divided into the training set(343 cases)and the validation set(152 cases)with a ratio of 7∶3 using the simple randomization grouping method.The region of interest of nodules on CT images were delineated layer by layer by scientific research platform and 1950 radiomics features were extracted.And then those features were filtrated by F-test,Pearson correlation coefficient,and L1 based feature selection.A model was built by using Logistic regression machine learning classifier,named mod 2,and radscore was also obtained.Differences between general information and CT features were analyzed.Binary Logistic regression analysis was used to construct a model for statistically significant variables,named mod 1.At the same time,Radscore was added to build the mod and named comb mod.The area under the curve(AUC),sensitivity and specificity of the three models were calculated.A nomogram was also drawn.Results A total of 495 patients were divided into the training set(n=343)and the validation set(n=152).Gender,carcinoma embryonic antigen(CEA),nodule,and maximum diameter were screened out in clinical features and involved in constructing the mod 1.Twelve features were selected from the radiomics features to build mod 2.Comb mod performed best,training set AUC:0.887,validation set AUC:0.875,and had good clinical practicability.Conclusion The model composed of general feature,CT feature and radiomics features could accurately predict high-grade pathological components in early ILAC,and provide references for clinicians to choose surgical method for patients.

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 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.