ObjectiveTo investigate the effect and mechanism of transplantation of human umbilical cord mesenchymal stem cell （hUC-MSC） with overexpression of the Numb gene in the treatment of cholestatic liver fibrosis （CLF）. MethodsThe technique of lentiviral transfection was used to induce the overexpression of the Numb gene in hUC-MSC （hUC-MSC^Numb-OE）， and hUC-MSC transfected with empty vector （hUC-MSC^OE-EV） was used as negative control. Bile duct ligation （BDL） was performed to establish a rat model of CLF， and then the rats were randomly divided into BDL group， hUC-MSC group， hUC-MSC^OE-EV group， and hUC-MSC^Numb-OE group， while a sham-operation group was also established. The rats in the intervention groups were given a single splenic injection of the corresponding cells after BDL， and samples were collected at the end of week 4. Related indicators were measured， including serum biochemistry， liver histopathology， the content of hydroxyproline （Hyp） in the liver， hepatic stellate cell activation， ductular reaction， liver regeneration， and the expression levels of key molecules in the Numb-p53 signaling axis. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsCompared with the BDL group， the hUC-MSC group and the hUC-MSC^OE-EV group had significant reductions in the levels of serum biochemical parameters （aspartate aminotransferase， gamma-glutamyl transpeptidase， total bile acid， total bilirubin， and direct bilirubin）， liver fibrosis markers （the content of Hyp and the expression levels of alpha-smooth muscle actin， tumor necrosis factor-α， and transforming growth factor-beta 1）， and ductular reaction markers （the expression levels of CK7 and CK19） （all P <0.05）， and compared with the hUC-MSC^OE-EV group， the hUC-MSC^Numb-OE group had significantly greater improvements in the above indicators （all P <0.05）. In addition， compared with the hUC-MSC^OE-EV group， the hUC-MSC^Numb-OE group had significant improvements in the expression levels of liver regeneration-related markers （albumin and hepatocyte nuclear factor 4α） and the molecules associated with the Numb-p53 signaling axis （Numb， pNumb， Mdm2， and p53） （all P <0.05）. ConclusionOverexpression of the Numb gene can enhance the therapeutic effect of hUC-MSC on CLF， possibly by activating the Numb-PTB^L-p53-HNF4α axis， promoting the hepatic differentiation of hUC-MSCs and subsequently enhancing liver regeneration.

Objective: To establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective:To explore the efficacy of non-invasive prenatal testing (NIPT) of fetal free DNA in maternal peripheral blood in fetuses with increased nuchal translucency (NT).Methods:A retrospective analysis was conducted on 1 184 singleton pregnant women that underwent chromosomal microarray analysis (CMA) at Nanjing Drum Tower Hospital, Nanjing University Medical School from June 2014 to December 2022 due to fetal increased NT (≥3.0 mm). These subjects were categorized based on whether the increased NT was accompanied by other high-risk factors into isolated increased NT without advanced maternal age (further subdivided into 3.0 mm≤NT<3.5 mm, 3.5 mm≤NT<4.0 mm, and NT≥4.0 mm subgroups), isolated increased NT with advanced maternal age, increased NT with nasal bone abnormalities, increased NT with other soft markers, and increased NT with structural abnormalities groups. Assuming the sensitivity and specificity of NIPT and expanded NIPT at this center were both 100%, genomic abnormalities outside the detection range of NIPT or expanded NIPT were termed as residual risk of NIPT or expanded NIPT. Chi-square test and Bonferroni correction were used to compare the residual risks of NIPT and expanded NIPT among the three subgroups of isolated increased NT without advanced maternal age group. Results:(1) In the group of isolated increased NT without advanced maternal age: For the 3.0 mm≤NT<3.5 mm subgroup (329 cases), 19 abnormalities were detected by CMA [12 cases of chromosome aneuploidy, seven cases of pathogenic copy number variation (pCNV)], with residual risks of NIPT and expanded NIPT both at 2.1% (7/329). For the 3.5 mm≤NT<4.0 mm subgroup (173 cases), 29 abnormalities were detected by CMA (17 cases of chromosome aneuploidy, nine cases of pCNV, three cases of chromosome unbalanced translocation), with residual risks of NIPT at 8.1% (14/173) and expanded NIPT at 7.5% (13/173). For the NT≥4.0 mm subgroup (270 cases), CMA detected abnormalities in 70 cases (50 cases of chromosome aneuploidy, 16 cases of pCNV, three cases of unbalanced translocations, and one case of sex chromosome abnormality combined with pCNV). The residual risk of NIPT was 12.2% (33/270), and the residual risk of expanded NIPT was 7.0% (19/270). The residual risks of NIPT and expanded NIPT in the 3.0 mm≤NT<3.5 mm subgroup were lower than those in the 3.5 mm≤NT<4.0 mm and NT≥4.0 mm subgroups (Bonferroni correction, all P<0.017). (2) In the group of 92 cases with isolated increased NT and advanced maternal age, CMA detected abnormalities in 36 cases (29 cases of chromosome aneuploidy, five cases of pCNV, one case of trisomy 21 combined with sex chromosome abnormality, and one case of trisomy 18 combined with sex chromosome abnormality). The residual risk of NIPT was 7.6% (7/92), and that of expanded NIPT was 5.4% (5/92). (3) In the group of 49 cases with increased NT combined with nasal bone abnormalities, CMA detected abnormalities in 24 cases (23 cases of chromosome aneuploidy and one case of pCNV). The residual risks of NIPT and expanded NIPT were both 2.0% (1/49). (4) In the group of 26 cases with increased NT combined with other soft markers, CMA detected abnormalities in nine cases (six cases of chromosome aneuploidy, one case of pCNV, and two cases of chromosome unbalanced translocations). The residual risks of NIPT and expanded NIPT were both 11.5% (3/26). (5) In the group of 245 cases with increased NT combined with structural abnormalities, CMA detected abnormalities in 121 cases (107 cases of chromosome aneuploidy, seven cases of pCNV, four cases of chromosome unbalanced translocations, one case of trisomy 21 combined with trisomy 20, and two cases of trisomy 18 combined with sex chromosome abnormalities). The residual risk of NIPT was 16.7% (41/245), and that of expanded NIPT was 4.1% (10/245). Conclusions:For isolated NT≥3.5 mm or NT≥3.0 mm combined with other high-risk factors, chorionic villus sampling in early pregnancy can be recommended, advancing the timing of prenatal diagnosis from the second trimester to the first trimester. For fetuses with isolated 3.0 mm≤NT<3.5 mm, the 2.1% residual risk of chromosomal abnormalities should be fully informed during counseling, even if the risk of NIPT is low.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To observe the value of preoperative magnetization transfer imaging(MTI)for predicting postoperative pancreatic fistula(POPF)after distal pancreatectomy(DP).Methods A total of 65 patients with pancreatic tumor who underwent DP and preoperative MR scanning were retrospectively enrolled and divided into clinically relevant POPF(CR-POPF)group(n=14,with grade B or C fistula),biochemical fistula group(n=31,postoperative drain fluid amylase level exceeding 3 times the upper limit of normal)and non-fistula group(n=20,postoperative drain fluid amylase level not exceeding 3 times the upper limit of normal)based on postoperative records.Clinical data and magnetization transfer ratio(MTR)of pancreatic tissue at the surgical margin were compared among 3 groups.The predictive value of MTR for CR-POPF was evaluated according to the area under the curve(AUC)of receiver operating characteristic(ROC)curve.Results Patients' age,intraoperative blood loss and the proportion of pancreatic ductal adenocarcinoma in both CR-POPF group and biochemical fistula group were lower than those in non-fistula group(all adjusted P＜0.05),while no significant difference was found between the former two groups(all adjusted P＞0.05).MTR of pancreatic tissue at the surgical margin in CR-POPF group was lower than that in both biochemical fistula group and non-fistula group(both P＜0.05),whereas no statistical difference was detected between the latter two groups(P＞0.05).The AUC of MTR for predicting CR-POPF after DP was 0.727.Conclusion Preoperative MTI could be used to predict POPF after DP.

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.

This study investigates the effect and underlying mechanism of Guizhi Tongluo Tablets(GZTL) in treating atherosclerosis(AS) in a mouse model. Apolipoprotein E-knockout(ApoE~(-/-)) mice were randomly assigned to the following groups: model, high-, medium-, and low-dose GZTL, and atorvastatin(ATV), and age-matched C57BL/6J mice were selected as the control group. ApoE~(-/-) mice in other groups except the control group were fed with a high-fat diet for the modeling of AS and administrated with corresponding drugs via gavage for 8 weeks. General conditions, signs of blood stasis, and body mass of mice were monitored. Aortic plaques and their stability were assessed by hematoxylin-eosin, Masson, and oil red O staining. Serum levels of total cholesterol(TC), triglycerides(TG), and low-density lipoprotein cholesterol(LDL-C) were measured by biochemical assays, and those of interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and interleukin-6(IL-6) were determined via enzyme-linked immunosorbent assay. Apoptosis was assessed by terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL). Single-cell RNA sequencing(scRNA-seq) was employed to analyze the differential expression of CD72hi macrophages(CD72hi-Mφ) in the aortas of AS patients and mice. The immunofluorescence assay was employed to visualize CD72hi-Mφ expression in mouse aortic plaques, and real-time fluorescence quantitative PCR was utilized to determine the mRNA levels of IL-1β, TNF-α, and IL-6 in the aorta. The results demonstrated that compared with the control group, the model group exhibited significant increases in body mass, aortic plaque area proportion, necrotic core area proportion, and lipid deposition, a notable decrease in collagen fiber content, and an increase in apoptosis. Additionally, the model group showcased elevated serum levels of TC, TG, LDL-C, IL-1β, TNF-α, and IL-6, alongside marked upregulations in the mRNA levels of IL-1β, TNF-α, and IL-6 in the aorta. In comparison with the model group, the GZTL groups and the ATV group showed a reduction in body mass, and the medium-and high-dose GZTL groups and the ATV group demonstrated reductions in aortic plaque area proportion, necrotic core area proportion, and lipid deposition, an increase in collagen fiber content, and a decrease in apoptosis. Furthermore, the treatment goups showcased lowered serum levels of TC, TG, LDL-C, IL-1β, TNF-α, and IL-6. The data of scRNA-seq revealed significantly elevated CD72hi-Mφ signaling in carotid plaques of AS patients compared with that in the normal arterial tissue. Animal experiments confirmed that CD72hi-Mφ expression, along with several pro-inflammatory cytokines, was significantly upregulated in the aortas of AS mice, which were downregulated by GZTL treatment. In conclusion, GZTL may alleviate AS by inhibiting CD72hi-Mφ activity.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Atherosclerosis/immunology* ; Mice ; Mice, Inbred C57BL ; Macrophages/immunology* ; Male ; Humans ; Apolipoproteins E/genetics* ; Tablets ; Tumor Necrosis Factor-alpha/genetics* ; Apoptosis/drug effects* ; Interleukin-1beta/genetics* ; Interleukin-6/genetics* ; Disease Models, Animal ; Mice, Knockout