Background Although current evidence suggests a link between outdoor air pollution and depressive symptoms, the effect of solid fuel use (a significant indoor air pollutant) on depressive symptoms in China's rural middle-aged and elderly population remains poorly understood. Objective To explore the association between solid fuel use for cooking and depressive symptoms among middle-aged and elderly people in rural areas of China, and to provide a basis for the prevention and control of depressive symptoms among residents in rural areas. Methods Data were obtained from the 2020 China Family Panel Studies (CFPS), depressive symptoms were assessed using 8-item Center for Epidemiologic Studies Depression Scale (CES-D), and cooking fuel type was self-reported. Subsequently, two-level binary unconditional logistic regression models were fitted to assess the impact of solid fuel use for cooking on depressive symptoms. Results A total of 7991 participants aged 45 years and older [mean age (58.52±9.18) years] were included in the study, with a mean CES-D score of 6.07±4.48 and a mean positive depressive symptoms rate of 34% (40.54% in the solid fuel group and 30.81% in the clean fuel group, χ²=74.40, P<0.001). After controlling for potential confounding covariates, rural middle-aged and elderly residents in the solid fuel group had a higher risk of reporting depressive symptoms than those in the clean fuel group (OR=1.36, 95%CI: 1.20-1.54), an association that was unaffected by participant characteristics and was positive across age and gender groups. Conclusion There is a positive association between solid fuel use for cooking and depressive symptoms in middle-aged and elderly people in rural areas of China. It is recommended that rural areas promote the upgrading of stoves through the installation of ventilation equipment or the switch from solid cooking fuel to clean fuel. This approach is directed toward reducing exposure to household air pollution, alleviating the prevalence of depression among middle-aged and elderly residents in vast rural areas, and enhancing their mental health.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Ginsenoside Rg_2(GRg2) is a triterpenoid compound found in Panax notoginseng. This study explored its effects and mechanisms on diabetic retinopathy and angiogenesis. The study employed endothelial cell models induced by glucose or vascular endothelial growth factor(VEGF), the chorioallantoic membrane(CAM) model, the oxygen-induced retinopathy(OIR) mouse model, and the db/db mouse model to evaluate the therapeutic effects of GRg2 on diabetic retinopathy and angiogenesis. Transwell assays and endothelial tube formation experiments were conducted to assess cell migration and tube formation, while vascular area measurements were applied to detect angiogenesis. The impact of GRg2 on the retinal structure and function of db/db mice was evaluated through retinal thickness and electroretinogram(ERG) analyses. The study investigated the mechanisms of GRg2 by analyzing the activation of Yes-associated protein(YAP) and Toll-like receptors(TLRs) pathways. The results indicated that GRg2 significantly reduced cell migration numbers and tube formation lengths in vitro. In the CAM model, GRg2 exhibited a dose-dependent decrease in the vascular area ratio. In the OIR model, GRg2 notably decreased the avascular and neovascular areas, ameliorating retinal structural disarray. In the db/db mouse model, GRg2 increased the total retinal thickness and enhanced the amplitudes of the a-wave, b-wave, and oscillatory potentials(OPs) in the ERG, improving retinal structural disarray. Transcriptomic analysis revealed that the TLR signaling pathway was significantly down-regulated following YAP knockdown, with PCR results consistent with the transcriptome sequencing findings. Concurrently, GRg2 downregulated the expression of Toll-like receptor 4(TLR4), TNF receptor-associated factor 6(TRAF6), and nuclear factor-kappaB(NF-κB) proteins in high-glucose-induced endothelial cells. Collectively, GRg2 inhibits cell migration and tube formation and significantly reduces angiogenesis in CAM and OIR models, improving retinal structure and function in db/db mice, with its pharmacological mechanism likely involving the down-regulation of YAP expression.
Animals ; Ginsenosides/pharmacology* ; Diabetic Retinopathy/physiopathology* ; Mice ; YAP-Signaling Proteins ; Humans ; Male ; Signal Transduction/drug effects* ; Cell Movement/drug effects* ; Adaptor Proteins, Signal Transducing/genetics* ; Mice, Inbred C57BL ; Neovascularization, Pathologic/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Panax notoginseng/chemistry* ; Endothelial Cells/metabolism* ; Transcription Factors/genetics* ; Angiogenesis

Sarin(GB)is a typical representative of nerve agents with high toxicity,and very low amount can cause death.GB can cause water and atmospheric environment poisoning,so the detection of GB in water and air is of great significance.In this work,a colorimetric sensor array(CSA)based on GB inhibition of cholinesterase activity was constructed to detect GB with high selectivity.A 4×4 colorimetric array was constructed using acetylcholinesterase(AChE),butyryl cholinesterase(BuChE)and the corresponding substrate acetylthiocholine iodide(S-ACh),butyryl thiocholine iodide(S-BCh),acetylcholine chloride(ACh),butyryl choline chloride(BCh)and 2,6-dichloroindophenol ethyl ester(DCIE).The linear curve of the sensor was Y=131.3×lgC+271.6(R2=0.997),where Y was the array response Euclidean distance,C was the concentration of GB(mg/L),the linear range was 0.03?0.32 mg/L,and the detection limit was 27.6 μg/L.The method could effectively distinguish chemical warfare agents(CWA)such as VX,Soman(GD),mustard gas(HD),Louie reagent(L),and had high anti-interference ability,sensitivity and good repeatability.It was successfully applied to the detection of GB in simulated water and simulated air samples,and the sample recovery rate was 97.2% ?100.9%.This method would be potentially applied to the field rapid detection of nerve agents.

The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

OBJECTIVE: This study aimed to investigate the effect of Astragalus (AST) on osteoporosis (OP) and the downstream mechanisms. METHODS: Human bone marrow-derived mesenchymal stem cells (hBMSCs) were induced to differentiate into osteogenic cells. After transfection with relevant plasmids, cell proliferation, cell cycle progression, and apoptosis were assessed. Alizarin red staining was used to detect calcium nodules in the cells, alkaline phosphatase (ALP) staining was used to detect ALP activity in the cells, and quantitative reverse transcription-polymerase chain reaction and western blotting were used to determine RUNX2 and Osterix expression levels. An OP rat model was established using ovariectomy and micro-computed tomography scanning. Hematoxylin and eosin staining and Masson's trichrome staining were used to evaluate the pathological conditions of bone tissues, while immunohistochemistry was conducted to detect RUNX2 in bone tissues. RESULTS: AST promoted the osteogenic differentiation of BMSCs, reduced miR-181d-5p expression levels, and increased SOX11 expression levels. Restoring miR-181d-5p expression or reducing SOX11 expression levels reversed the effects of AST on the osteogenic differentiation of hBMSCs. miR-181d-5p was found to target SOX11 in hBMSCs. AST improved OP in rats, and miR-181d-5p overexpression or SOX11 inhibition reversed the therapeutic effects of AST on OP in rats. CONCLUSION AST promoted the osteogenic differentiation of hBMSCs and alleviated OP by targeting SOX11 via miR-181d-5p.
Osteogenesis/drug effects* ; Animals ; MicroRNAs/genetics* ; Mesenchymal Stem Cells/drug effects* ; Osteoporosis/drug therapy* ; Humans ; Cell Differentiation/drug effects* ; Astragalus Plant/chemistry* ; Rats ; Rats, Sprague-Dawley ; Female ; SOXC Transcription Factors/genetics* ; Plant Extracts/pharmacology* ; Cells, Cultured ; Drugs, Chinese Herbal/pharmacology*

Objective:To construct and evaluate a risk warning model for enteral nutrition associated diarrhea(ENAD)in acute stroke patients based on logistic regression and Nomogram.Methods:This study was a retrospective study,a total of 172 patients with acute stroke who were admitted to Yancheng First People's Hospital from January 2022 to May 2024 were selected,and clinical data of patients were collected.Multivariate logistic regression analysis was used to identify the influencing factors of ENAD occurrence,based on the results of the multivariate logistic regression analysis,a Nomogram warning model was constructed,the receiver operating characteristic(ROC)curve was applied to analyze the predictive value of the Nomogram warning model for the risk of ENAD occurrence in patients with acute stroke.Results:Univariate analysis showed that,the occurrence of ENAD in patients with acute stroke was related to fasting time,mechanical ventilation,oral potassium preparations,albumin,number of types of antibiotics used,acute physiology and chronic health status Ⅱ(APACHE Ⅱ)score,enteral nutrition infusion rate,length of stay in the intensive care unit(ICU),use of proton pump inhibitors,duration of antibiotic use,use of gastrointestinal prokinetic drugs,and daily enteral nutrition infusion volume(P＜0.05).The results of multiple logistic regression analysis showed that,high APACHE Ⅱ score,prolonged use of antibiotics,low albumin,use of gastrointestinal prokinetic drugs,oral potassium preparations,prolonged fasting time,and rapid enteral nutrition infusion were risk factors for ENAD in patients with acute stroke(P＜0.05).Construct and validate a Nomogram warning model based on the results of multiple logistic regression analysis.After evaluation,the model fits well with an area under the curve(AUC)of 0.823,indicating high predictive value for ENAD occurrence in patients with acute stroke.Conclusion:High APACHE Ⅱ score,prolonged use of antibiotics,low albumin,use of gastrointestinal prokinetic drugs,oral potassium preparations,prolonged fasting time,and rapid enteral nutrition infusion are risk factors for ENAD in patients with acute stroke.The Nomogram warning model constructed based on the above indicators has high predictive value for the occurrence of ENAD in patients with acute stroke.

Objective:To construct and evaluate a risk warning model for enteral nutrition associated diarrhea(ENAD)in acute stroke patients based on logistic regression and Nomogram.Methods:This study was a retrospective study,a total of 172 patients with acute stroke who were admitted to Yancheng First People's Hospital from January 2022 to May 2024 were selected,and clinical data of patients were collected.Multivariate logistic regression analysis was used to identify the influencing factors of ENAD occurrence,based on the results of the multivariate logistic regression analysis,a Nomogram warning model was constructed,the receiver operating characteristic(ROC)curve was applied to analyze the predictive value of the Nomogram warning model for the risk of ENAD occurrence in patients with acute stroke.Results:Univariate analysis showed that,the occurrence of ENAD in patients with acute stroke was related to fasting time,mechanical ventilation,oral potassium preparations,albumin,number of types of antibiotics used,acute physiology and chronic health status Ⅱ(APACHE Ⅱ)score,enteral nutrition infusion rate,length of stay in the intensive care unit(ICU),use of proton pump inhibitors,duration of antibiotic use,use of gastrointestinal prokinetic drugs,and daily enteral nutrition infusion volume(P＜0.05).The results of multiple logistic regression analysis showed that,high APACHE Ⅱ score,prolonged use of antibiotics,low albumin,use of gastrointestinal prokinetic drugs,oral potassium preparations,prolonged fasting time,and rapid enteral nutrition infusion were risk factors for ENAD in patients with acute stroke(P＜0.05).Construct and validate a Nomogram warning model based on the results of multiple logistic regression analysis.After evaluation,the model fits well with an area under the curve(AUC)of 0.823,indicating high predictive value for ENAD occurrence in patients with acute stroke.Conclusion:High APACHE Ⅱ score,prolonged use of antibiotics,low albumin,use of gastrointestinal prokinetic drugs,oral potassium preparations,prolonged fasting time,and rapid enteral nutrition infusion are risk factors for ENAD in patients with acute stroke.The Nomogram warning model constructed based on the above indicators has high predictive value for the occurrence of ENAD in patients with acute stroke.

To establish a method for determining 26 inorganic elements in earthworm polypeptide and determine the elemental content in different batches of earthworm polypeptide, microwave digestion method was used to pre-treat the samples, and ICP-MS method was used to determine the content of 26 elements in different batches of earthworm polypeptide. The linear relationships of 26 elements were good in the range of 0-1 000 μg·L^-1, with R² greater than 0.999, precision RSD 0.21%-2.71%, repeatability RSD 0.19%-4.69%, stability RSD 0.11%-4.24%, and recovery rates of 82.41%-116.16%. The data was plotted using Origin 2022 software to characterize the distribution of elements content. SPSS 27.0 was used for principal component analysis, and SIMCA 14.1 software was used for OPLS-DA analysis. The results showed that among the 26 elements, the higher content of earthworm polypeptide was K, Na, and Ca, followed by Zn, Fe, Al, B and other trace elements, In, Sc, Co, Pb, Bi and other elements had little or no detectable content, and there were differences in the content of polypeptide in different batches. This study provides a theoretical basis for the production quality control, quality evaluation and drug efficacy application of earthworm polypeptide through the determination and analysis of the elements and content of earthworm polypeptide.

In this study, deep eutectic solvents (DESs) were used as excipients to prepare solid dispersion (SD) of scutellarin. The SD of scutellarin were prepared by melting method with cumulative dissolution rate as the index of investigation. The preparation conditions of SD of scutellarin were optimized by single factor experiment, which investigated the type of the carrier material, the type of DESs, and the ratio of the drug to the carrier. The optimum preparation conditions of DESs-SD were as follows: using Poloxamer 407 as the carrier material, PEG 200/urea (2∶1) as the DESs system, and the ratio of carrier, DESs, and drug was 6∶1∶1. The drug loading capacity of scutellarin in SD was 12.53% under the optimum preparation conditions. Differential scanning calorimetry, Fourier transform infrared spectroscopy, X-ray powder diffraction and scanning electron microscope exhibited that scutellarin was amorphous form in the SD system. Furthermore, the stability of the DESs-based SD of scutellarin was evaluated by high temperature, high humidity, and strong light tests, which showed that the cumulative dissolution rate and scutellarin content of SD decreased with time under these conditions. Finally, the result of pharmacokinetic studies indicated that the oral absorption of the scutellarin could be increased using DESs as an excipient in the preparation of SD. The animal experiment was approved by the Experimental Animal Ethics Committee of Fujian University of Traditional Chinese Medicine (approval number: FJTCMIACUC 2023048). Consequently, this research offers a novel and effective approach for using DESs to enhance the oral bioavailability of active substances with low water solubility.