ObjectiveTo explore the mechanism of Hei Xiaoyaosan in improving the cognitive function in Alzheimer's disease （AD） from cell apoptosis mediated by the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/nuclear factor kappa B （NF-κB） signaling pathway. MethodsFour-month-old SD male rats were randomly assigned into a blank group， a sham group， a model group， a donepezil hydrochloride （0.45 mg·kg^-1） group， and high-， medium-， and low-dose （15.30， 7.65， and 3.82 g·kg^-1， respectively） Hei Xiaoyaosan groups， with 10 rats in each group. The sham group received bilateral hippocampal injection of 1 μL normal saline， while the other groups received bilateral hippocampal injection of 1 μL beta-amyloid 1-42 （Aβ_1-42） solution for the modeling of AD. Rats were administrated with corresponding agents once a day for 42 consecutive days. The Morris water maze test was carried out to assess the learning and memory abilities of rats. Hematoxylin-eosin staining was employed to observe pathological changes in the hippocampus of rats. Enzyme-linked immunosorbent assay was employed to measure the levels of cysteinyl aspartate-specific proteinase-3 （Caspase-3）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax）. Western blot was employed to determine the protein levels of PI3K， Akt， and NF-κB. A cell model of AD was established by co-culturing Aβ_1-42 and PC12 cells in vitro. Cell viability and apoptosis were detected by the cell-counting kit 8 （CCK-8） assay and flow cytometry （FC）， respectively. ResultsAnimal experiments showed that compared with the blank group， the model group had a prolonged escape latency （P<0.01）， a reduced number of crossing platforms （P<0.01）， disarrangement and a reduced number of hippocampal neurons， up-regulated expression of Bax and Caspase-3， down-regulated expression of Bcl-2 （P<0.01）， decreased p-PI3K/PI3K and p-Akt/Akt levels， and an increased p-NF-κB/NF-κB level （P<0.01）. Compared with the model group， donepezil hydrochloride and high- and medium-dose Hei Xiaoyaosan shortened the escape latency and increased the number of crossing platforms （P<0.05， P<0.01）， improved the arrangement and increased the number of hippocampal neurons， down-regulated the expression levels of Bax and Caspase-3， up-reguated the expression level of Bcl-2 （P<0.05， P<0.01）， increased the p-PI3K/PI3K and p-Akt/Akt levels （P<0.05， P<0.01）， and reduced the p-NF-κB/NF-κB level （P<0.05， P<0.01）. Cell experiments showed that compared with the blank group， the model group exhibited an increased apoptosis rate （P<0.01）. Compared with the model group， the serum containing Hei Xiaoyaosan at various doses improved the cell viability （P<0.01）， and the serum containing Hei Xiaoyaosan at the high dose decreased the cell apoptosis （P<0.01）. ConclusionHei Xiaoyaosan may improve the learning and memory abilities of AD model rats by regulating cell apoptosis， while increasing the vitality and reducing the apoptosis rate of AD model cells via the PI3K/Akt/NF-κB signaling pathway.

【Objective】 To investigate the prevalence and influencing factors of overweight and obesity among preschool children in Suzhou. 【Methods】 A stratified cluster random sampling method was used to select 24 452 children aged 3 - 6 years in different districts of Suzhou from December 2021 to June 2022. Then the prevalence rate of overweight and obesity was determined by physical measurements. A case-control study was conducted with a questionnaire survey of 3 786 children(1 893 in the obesity group and 1 893 in the control group) to analyze the factors influencing preschool obesity. 【Results】 1) The overall detection rates of overweight among preschool children in Suzhou was 14.8%(boys 14.6%, girls 15.0%). The overall detection rates of obesity was 7.9%(boys 8.7%, girls 7.1%), with a statistically significant difference between boys and girls(χ²=19.828, P<0.01). 2) There was statistically significant difference in the detection rates of obesity among different age groups(χ²=98.415, P<0.01), with the lowest rate in the 3 - 4 years old group(5.8%) and the highest rate in the 6 - 7 years old group(11.8%). 3) The overall detection rates of mild, moderate and severe obesity was 4.8%, 2.6% and 0.5%, respectively. The proportion of moderate and severe obesity significantly increased with age(χ²=57.275, P<0.01). 4) Risk factors for preschool obesity included birth weight >4 000g, cesarean section, parental overweight/obesity, strong appetite of children, eating speed <10min/meal, high frequency of fried food consumption(>1time/week), eating while watching television, sedentary behavior >2h/d, insufficient exercise endurance, screen time >1h/d, and late bedtime(after 21∶30)(P<0.05). Protective factors for preschool obesity included larger breakfast consumption, fruits and vegetables as regular snacks, and physical activity after meals(P<0.05). 5) Factors influencing the degree of preschool obesity included paternal overweight(OR=1.33, 95%CI:1.06 - 1.65), paternal obesity(OR=1.91, 95%CI:1.46 - 2.49), maternal overweight(OR=1.25, 95%CI:1.01 - 1.54), maternal obesity(OR=1.94, 95%CI:1.40 - 2.69), low education level of father(junior high school or below)(OR=1.57, 95%CI:1.25 - 1.96), strong appetite of children(OR=1.72, 95%CI:1.41 - 2.11), eating speed <10min/meal(OR=1.29, 95%CI:1.05 - 1.57), sedentary behavior >2h/d(OR=1.51, 95%CI:1.24 - 1.85), insufficient exercise endurance(OR=1.56, 95%CI:1.12 - 2.19), and screen time>1h/d(OR=1.42, 95%CI:1.16 - 1.75). 【Conclusions】 The detection rates of overweight and obesity among preschool children in Suzhou are relatively high, and the detection rate and severity of obesity increase with age. In addition to genetic factors, preschool obesity are also associated with pregnancy and birth history, as well as unhealthy lifestyle after birth.

The “hospital-community” hospice care model involves multiple stakeholders，including demander，executor，leader，and fundraiser of medical and health services.The degree of benefit correlation，policy influence，and implementation willingness of various stakeholders were analyzed to provide reference for terminal cancer patients to obtain continuous，convenient，and high-quality hospice care.Health department and medical insurance department are the main driving forces for cross-institutional hospice care，but there are differences in their driving paths.The financial department is an important guarantor of policy implementation，and needs to ensure that its core interests are not lost.Community medical institutions are an important driving factor for policy implementation，but they require policy support and hospital drive. Medical staff in hospitals and communities，have weak willingness to implement policies，which can easily become obstacles to policy implementation in the absence of incentive and compensation mechanisms.Patients and their caregivers are important beneficiaries，but lack of publicity，education，and interactive communication can also lead to rejection and contradiction.Therefore，it is necessary to leverage the collaboration and coordination between policy enforcement departments，innovate the development model of hospitals，lead community medical institutions，and promote interactive communication and decision-making sharing of “doctor-doctor” and “doctor-patient”.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Objective:To construct and validate a predictive model for the occurrence of malignant cerebral edema(MCE)in the elderly with acute large hemispheric infarction(LHI)of the anterior cerebral artery.Methods:Clinical, laboratory and imaging data of 301 elderly patients with acute LHI of the anterior cerebral artery admitted to the Department of Neurology of the Third Affiliated Hospital of Soochow University between January 2018 and April 2023 were retrospectively analyzed.Patients were divided into a modeling group(211 cases)and a validation group(90 cases)by the simple random sampling method with a ratio of 7∶3.According to the occurrence of MCE, univariate and multivariate Logistic regression analyses were performed with data from the modeling group to screen for independent predictors of the development of MCE.Nomograms were created and internally validated using R software.Additionally, external validation was performed with data from the validation group, and the performance of the model was assessed by receiver operating characteristic(ROC)curves, calibration plots, and clinical decision curve analysis(DCA), respectively.Results:The MCE incidence and baseline data between the modeling and validation groups were not statistically significantly different and were actually comparable.Multivariate Logistic analysis in the modeling group showed that a history of atrial fibrillation( OR=3.459, 95% CI: 1.202-9.955, P=0.021), Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score( OR=1.202, 95% CI: 1.052-1.373, P=0.007), National Institutes of Health Stroke Scale(NIHSS)score( OR=1.163, 95% CI: 1.039-1.3013, P=0.008), Alberta Stroke Program Early CT Score(ASPECTS)( OR=0.782, 95% CI: 0.639-0.958, P=0.018), and collateral score(CS)( OR=0.414, 95% CI: 0.221-0.777, P=0.006)were independent predictors of the occurrence of MCE in the elderly patients with LHI.Based on the nomogram model constructed using the independent predictors, the ROC value for the risk of developing MCE was 0.912(95% CI: 0.867-0.957)in the modeling group and 0.957(95% CI: 0.902-0.997)in the validation group.The predicted probabilities from the nomograms in the modeling and validation groups were close to the actual probabilities, indicating good calibration.The DCA curves in the validation group showed that the predictive model had good clinical utility. Conclusions:The nomogram model established in this study exhibits good discrimination and calibration for the prediction of MCE, and has some predictive value.

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death, and most patients with HCC are diagnosed at an advanced stage. Before 2017, tyrosine kinase inhibitors were the main drugs for the treatment of advanced hepatocellular carcinoma. With the emergence of immune checkpoint inhibitors (ICIs), immunotherapy has gradually brought new hope to such patients. At present, the combination of ICIs and other systemic or local treatments has become a potential strategy for the treatment of advanced hepatocellular carcinoma, and some of these combinations have been included in large-scale clinical trials. The main challenges of immunotherapy for advanced hepatocellular carcinoma include the exploration of predictive biomarkers, management of immune-related adverse events, and exploration of effective combination regimens. This article provides the latest research progress on the single or combined use of ICIs and other immunotherapy for hepatocellular carcinoma and discusses the limitations of current research and clinical application and the future development direction.

BACKGROUND: Gestational diabetes mellitus (GDM) brings health issues for both mothers and offspring, and GDM prevention is as important as GDM management. It was shown that a history of GDM was significantly associated with a higher maternal risk for GDM recurrence. The incidence of GDM recurrence was unclear because of the incidence of second-child was low before 2016 in China. We aim to investigate the prevalence of GDM recurrence and its associated high-risk factors which may be useful for the prediction of GDM recurrence in China. METHODS: A retrospective study was conducted which enrolled participants who underwent regular prenatal examination and delivered twice in the same hospital of 18 research centers. All participants were enrolled from January 2018 to October 2018, where they delivered the second baby during this period. A total of 6204 women were enrolled in this study, and 1002 women with a history of GDM were analyzed further. All participants enrolled in the study had an oral glucose tolerance test (OGTT) result at 24 to 28 weeks and were diagnosed as GDM in the first pregnancy according to the OGTT value (when any one of the following values is met or exceeded to the 75-g OGTT: 0 h [fasting], ≥5.10 mmol/L; 1 h, ≥10.00 mmol/L; and 2 h, ≥8.50 mmol/L). The prevalence of GDM recurrence and development of type 2 diabetes mellitus were calculated, and its related risk factors were analyzed. RESULTS: In 6204 participants, there are 1002 women (1002/6204,16.15%) with a history of GDM and 5202 women (5202/6204, 83.85%) without a history of GDM. There are significant differences in age (32.43 ± 4.03 years vs. 33.00 ± 3.34 years vs. 32.19 ± 3.37 years, P  < 0.001), pregnancy interval (4.06 ± 1.44 years vs. 3.52 ± 1.43 years vs. 3.38 ± 1.35 years, P  = 0.004), prepregnancy body mass index (BMI) (27.40 ± 4.62 kg/m2vs. 23.50 ± 3.52 kg/m2vs. 22.55 ± 3.47 kg/m2, P < 0.001), history of delivered macrosomia (22.7% vs. 11.0% vs. 6.2%, P < 0.001) among the development of diabetes mellitus (DM), recurrence of GDM, and normal women. Moreover, it seems so important in the degree of abnormal glucose metabolism in the first pregnancy to the recurrence of GDM and the development of DM. There are significant differences in OGTT levels of the first pregnancy such as area under the curve of OGTT value (18.31 ± 1.90 mmol/L vs. 16.27 ± 1.93 mmol/L vs. 15.55 ± 1.92 mmol/L, P < 0.001), OGTT fasting value (5.43 ± 0.48 mmol/L vs. 5.16 ± 0.49 mmol/L vs. 5.02 ± 0.47 mmol/L, P < 0.001), OGTT 1-hour value (10.93 ± 1.34 mmol/L vs. 9.69 ± 1.53 mmol/L vs. 9.15 ± 1.58 mmol/L, P < 0.001), OGTT 2-hour value (9.30 ± 1.66 mmol/L vs. 8.01 ± 1.32 mmol/L vs. 7.79 ± 1.38 mmol/L, P < 0.001), incidence of impaired fasting glucose (IFG) (fasting plasma glucose ≥5.6 mmol/L) (31.3% vs. 14.6% vs. 8.8%, P < 0.001), and incidence of two or more abnormal OGTT values (68.8% vs. 39.7% vs. 23.9%, P < 0.001) among the three groups. Using multivariate analysis, the factors, such as age (1.07 [1.02-1.12], P = 0.006), prepregnancy BMI (1.07 [1.02, 1.12], P  = 0.003), and area under the curve of OGTT in the first pregnancy (1.14 [1.02, 1.26], P  = 0.02), have an effect on maternal GDM recurrence; the factors, such as age (1.28 [1.01-1.61], P  = 0.04), pre-pregnancy BMI (1.26 [1.04, 1.53], P = 0.02), and area under the curve of OGTT in the first pregnancy (1.65 [1.04, 2.62], P = 0.03), have an effect on maternal DM developed further. CONCLUSIONS The history of GDM was significantly associated with a higher maternal risk for GDM recurrence during follow-up after the first pregnancy. The associated risk factors for GDM recurrence or development of DM include age, high pre-pregnancy BMI, history of delivered macrosomia, the OGTT level in the first pregnancy, such as the high area under the curve of OGTT, IFG, and two or more abnormal OGTT values. To prevent GDM recurrence, women with a history of GDM should do the preconception counseling before preparing next pregnancy.
Adult ; Blood Glucose/metabolism* ; China/epidemiology* ; Diabetes Mellitus, Type 2/epidemiology* ; Diabetes, Gestational ; Female ; Fetal Macrosomia ; Glucose Intolerance ; Humans ; Male ; Pregnancy ; Retrospective Studies

Purpose: Investigation of stroke lesions mostly focuses on the grey matter (GM). White matter (WM) degeneration during acute stroke has remained understudied. In the present study, monkeys were employed to investigate the alterations in GM and WM in the brain following ischemic occlusion using diffusion tensor imaging (DTI). Materials and Methods: Permanent middle cerebral artery occlusion was induced in rhesus monkeys (n = 6) using an interventional approach. Serial DTI was conducted on a clinical 3 T in the hyperacute phase (2–6 hours), 48, and 96 hours post-occlusion. Regions of interest in GM and WM of lesion areas were selected for data analysis. Results: Mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) in WM decreased substantially during hyperacute stroke, similar to those seen in GM. No obvious fractional anisotropy changes were seen in WM during the hyperacute phase until 48 hours poststroke when significant fiber loss was observed. Pseudo-normalization of MD, AD, and RD was seen at 96 hours. Pathological changes in WM and GM were observed in ischemic areas at 8, 48, and 96 hours poststroke. Relative changes in MD, AD, and RD of WM were correlated negatively with infarction volumes at 6 hours poststroke. Conclusion The present study revealed the microstructural changes in GM and WM of monkey brains during acute stroke using DTI. The preliminary results suggest that AD and RD may be sensitive surrogate markers to assess specific microstructural changes in WM during the hyperacute stroke.