The plants of the genus Caragana Fabr. are desert plants of the Leguminosae family, which are widely used in traditional ethnomedicine, with the effects of nourishing Yin and nourishing blood, dispelling wind and removing dampness, clearing heat and detoxifying toxins. The anti-inflammatory effect of Caragana Fabr. has attracted much attention, the research on the related mechanism has made some progress, but it lacks systematic collation. By summarising the anti-inflammatory effects of the active ingredients of Caragana Fabr., the authors found that they have good anti-inflammatory activities on different inflammatory cell models in vitro, and have good improvement effects on rheumatoid arthritis, colitis, complex nephritis, and acute lung injury and other disease models. The anti-inflammatory effects of the active components of this genus mainly include the reduction of the levels of a variety of pro-inflammatory factors, and the signalling pathways involved mainly include TLR4/NF-κB, TLR4/MAPK, TLR4/NF-κB/IRF3, JAK/STAT-1, ERK/STAT-1 and so on. Therefore, the paper mainly reviews the research progress on the anti-inflammatory effects and mechanisms of the Caragana Fabr. plants and their active components according to disease types, with a view to providing reference for the in-depth study of their anti-inflammatory activities and the development of new products with related functions.

OBJECTIVE: To investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. METHODS: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk. RESULTS: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk. CONCLUSION PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental Exposure

OBJECTIVE: The results of limited studies on the relationship between environmental pollution and dementia have been contradictory. We analyzed the combined effects of PM _2.5 and smoking on the prevalence of dementia and cognitive impairment in an elderly community-dwelling Chinese population. METHODS: We assessed 24,117 individuals along with the annual average PM _2.5 concentrations from 2012 to 2016. Dementia was confirmed in the baseline survey at a qualified clinical facility, and newly suspected dementia was assessed in 2017, after excluding cases of suspected dementia in 2015. National census data were used to weight the sample data to reflect the entire population in China, with multiple logistic regression performed to analyze the combined effects of PM _2.5 and smoking frequency on dementia and cognitive impairment. RESULTS: Individuals exposed to the highest PM _2.5 concentration and smoked daily were at higher risk of dementia than those in the lowest PM _2.5 concentration group ( OR, 1.603; 95% CI [1.626-1.635], P < 0.0001) and in the nonsmoking group ( OR, 1.248; 95% CI [1.244-1.252]; P < 0.0001). Moderate PM _2.5 exposure and occasional smoking together increased the short-term risk of cognitive impairment. High-level PM _2.5 exposure and smoking were associated with an increased risk of dementia, so more efforts are needed to reduce this risk through environmental protection and antismoking campaigns. CONCLUSION High-level PM _2.5 exposure and smoking were associated with an increased risk of dementia. Lowering the ambient PM _2.5, and smoking cessation are recommended to promote health.
Humans ; Dementia/etiology* ; Male ; Aged ; Female ; Cognitive Dysfunction/etiology* ; China/epidemiology* ; Particulate Matter/analysis* ; Smoking/epidemiology* ; Air Pollutants/analysis* ; Aged, 80 and over ; Environmental Exposure/adverse effects* ; Prevalence ; Middle Aged

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective To investigate the internalized weight bias and weight control beliefs regarding the current status,influencing factors,and impacts on eating behaviors of women losing weight. Methods A convenient sampling method was used to select the females who underwent physical examination in the Peking Union Medical College Hospital from August to December 2023.The general information questionnaire,Weight Bias Internalization Scale,Weight Control Belief Questionnaire,and Chinese version of Three-Factor Eating Questionnaire were utilized for investigation.Latent class analysis was conducted to explore the potential categories based on the characteristics of internalized weight bias and weight control beliefs.Univariate analysis and multiple-factor linear regression analysis were performed to explore influencing factors of potential categories and their effects on eating behaviors. Results A total of 518 subjects were included in this study.The internalized weight bias and weight control beliefs could be classified into three potential categories: low weight bias tolerance type(53.7%),medium weight bias persistence type(29.5%),and high weight bias overcritical type(16.8%).Logistic regression analysis showed that body mass index,unmarriage,and divorce were the influencing factors of the high weight bias overcritical type.The scores of non-control eating and emotional eating in medium weight bias persistence type and high weight bias overcritical type were higher than those in low weight bias tolerance type(all P<0.001). Conclusions The attitudes of Chinese women towards body weight can be categorized into low weight bias tolerance type,medium weight bias persistence type,and high weight bias overcritical type.The high weight bias overcritical type is characterized by high weight bias and high personal responsibility attribution,and it is closely associated with negative eating behaviors.A high body mass index,unmarriage,and divorce are high-risk factors for developing the high weight bias overcritical type.
Humans ; Female ; Feeding Behavior/psychology* ; Surveys and Questionnaires ; Adult ; Body Mass Index ; Weight Loss ; Body Weight ; Body Image ; Middle Aged ; Weight Prejudice

Monoamine neurotransmitters mainly include serotonin,dopamine,epinephrine,and norepinephrine.They play an indispensable regulatory role in key physiological activities such as emotion,sleep,and memory within the central nervous system.Precise detection of these neurotransmitters holds great significance in the field of neuroscience research.Detection methods for monoamine neurotransmitters encompass high-performance liquid chromatography,mass spectrometry,capillary electrophoresis,fluorescence spectroscopy,and electrochemical methods,etc.Compared with other methods,electrochemical methods offer advantages such as high sensitivity,good selectivity,low cost,strong portability,convenient operation,and capability for in vivo real-time detection.This article reviewed recent research progress in electrochemical detection of monoamine neurotransmitters,focusing on a retrospective and summary from three aspects:sensor electrode materials,detection of various monoamine neurotransmitters,and in vivo real-time analysis.Furthermore,the future development of electrochemical sensors for monoamine neurotransmitters was prospected.

Food,drug and environment related cases are becoming more and more frequent,and the demand for on-site rapid detection is also increasing.Nanozymes are nanomaterials with enzyme-like catalytic activity,which have the advantages of high catalytic efficiency,good stability,economy,adjustability,multifunctionality and large-scale preparation.The colorimetric sensing technology based on nanozymes combined with smart phones has wide range of applications in the field of food,drugs and environment detection,and is expected to become an important means for relevant departments to combat crime.This paper summarized the progresses of nanozymes in the field of environmental,food and drug crime(EFDC)detection,focusing on the detection mechanism of different types of nanozymes and the current status of research on the detection of EFDC,and prospected the future development of nanozymes.The possible future prospects of machine learning(ML)in the field of nanozymes colorimetric sensing technology and the challenges in detection of EFDC were also discussed.

Objectives:To evaluate the predictive value of Tabular Prior-data Fitted Network(TabPFN) for short-term outcome in patients with spontaneous intracerebral hemorrhage (sICH), and compared with the Extreme Gradient Boosting (XGboost) model and traditional logistic regression (LR) model. Methods:Patients with sICH admitted to the Department of Neurology, Hefei Second People's Hospital from January 2018 to March 2024 were included retrospectively. The demographic and baseline data were collected. At 3 months after onset, the modified Rankin Scale score was used to determine the outcome, 0-2 was defined as good outcome and >2 was defined as poor outcome. All enrolled patients were randomly divided into a training set and a testing set at a ratio of 7:3. Feature selection was performed using recursive feature elimination (RFE) method, and then the selected feature variables were included into TabPFN, XGboost, and LR models for training and testing. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the models. Shapley additive explanations (SHAP) method was used for model interpretation.Results:A total of 547 patients with sICH were enrolled, including 367 males (67.1%), with a median age of 65 (interquartile range, 54-76) years. Two hundred twenty-six patients (41.3%) had poor outcome. Age, baseline blood pressure (systolic blood pressure, diastolic blood pressure), baseline laboratory tests (white blood cell count, red blood cell count, platelet count, neutrophil count, hemoglobin, fasting blood glucose, creatinine, uric acid, urea nitrogen, alanine aminotransferase, aspartate aminotransferase), hematoma rupture into the ventricle, island sign, baseline hematoma volume, and baseline National Institutes of Health Stroke Scale (NIHSS) score were selected as characteristic variables using RFE method. ROC curve analysis showed that the ROC AUC for TabPFN, Xgboost, and LR models predicting poor short-term outcome in the testing set were 0.918 (95% confidence interval [ CI] 0.870-0.966], 0.883 (95% CI 0.826-0.940), and 0.905 (95% CI 0.854-0.957), respectively. SHAP analysis showed that the top four important variables in the TabPFN model were baseline NIHSS score, baseline hematoma volume, baseline aspartate aminotransferase, and age. Conclusions:The TabPFN model is superior to the LR model and the XGBoost model in predicting poor outcome in patients with sICH. In the TabPFN model, baseline NIHSS score, baseline hematoma volume, aspartate aminotransferase, and age are the most important predictors of poor outcome in patients with sICH.Objectives To evaluate the predictive value of Tabular Prior-data Fitted Network(TabPFN) for short-term outcome in patients with spontaneous intracerebral hemorrhage (sICH), and compared with the Extreme Gradient Boosting (XGboost) model and traditional logistic regression (LR) model. Methods Patients with sICH admitted to the Department of Neurology, Hefei Second People's Hospital from January 2018 to March 2024 were included retrospectively. The demographic and baseline data were collected. At 3 months after onset, the modified Rankin Scale score was used to determine the outcome, 0-2 was defined as good outcome and >2 was defined as poor outcome. All enrolled patients were randomly divided into a training set and a testing set at a ratio of 7:3. Feature selection was performed using recursive feature elimination (RFE) method, and then the selected feature variables were included into TabPFN, XGboost, and LR models for training and testing. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the models. Shapley additive explanations (SHAP) method was used for model interpretation. Results A total of 547 patients with sICH were enrolled, including 367 males (67.1%), with a median age of 65 (interquartile range, 54-76) years. Two hundred twenty-six patients (41.3%) had poor outcome. Age, baseline blood pressure (systolic blood pressure, diastolic blood pressure), baseline laboratory tests (white blood cell count, red blood cell count, platelet count, neutrophil count, hemoglobin, fasting blood glucose, creatinine, uric acid, urea nitrogen, alanine aminotransferase, aspartate aminotransferase), hematoma rupture into the ventricle, island sign, baseline hematoma volume, and baseline National Institutes of Health Stroke Scale (NIHSS) score were selected as characteristic variables using RFE method. ROC curve analysis showed that the ROC AUC for TabPFN, Xgboost, and LR models predicting poor short-term outcome in the testing set were 0.918 (95% confidence interval [ CI] 0.870-0.966], 0.883 (95% CI 0.826-0.940), and 0.905 (95% CI 0.854-0.957), respectively. SHAP analysis showed that the top four important variables in the TabPFN model were baseline NIHSS score, baseline hematoma volume, baseline aspartate aminotransferase, and age. Conclusions The TabPFN model is superior to the LR model and the XGBoost model in predicting poor outcome in patients with sICH. In the TabPFN model, baseline NIHSS score, baseline hematoma volume, aspartate aminotransferase, and age are the most important predictors of poor outcome in patients with sICH.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.