In this study,a sensitive lateral flow immunoassay(LFIA)platform based on carbon dots-mesoporous silica nanocomposite(CD-MSNs)fluorescent probes was constructed for high-performance detection of inflammatory marker interleukin-6(IL-6).Green fluorescent carbon dots(CDs)were prepared by hydrothermal method with 3,9-perylenic acid and 3-aminopropyltriethoxysilane(APTES)as raw materials,and highly fluorescent CD-MSNs composites were then constructed by encapsulating the prepared CDs in mesoporous silica nanoparticles(MSNs).Fluorescent probes were prepared by covalent coupling of CD-MSNs with IL-6 antibody.Fluorescent immunochromatographic test strips were constructed by spraying IL-6 capture antibody and goat anti-mouse IgG on nitrocellulose membrane as detection line(T-line)and quality control line(C-line),respectively.The fluorescence immunoassay analyzer was used to quantitatively detect the fluorescence intensity of T-line,and the experimental results showed that the LFIA platform based on this probe had a good linear relationship in IL-6 concentration range of 102-106 pg/mL,and the detection limit was 64 pg/mL,which was two orders of magnitude more sensitive than that of the traditional colloidal gold test strips.This method effectively solved the issue of insufficient sensitivity of traditional LFIA technique,and provided a rapid and highly sensitive detection method for early diagnosis of inflammatory diseases.

OBJECTIVE: To assess the efficacy of Qingda Granule (QDG) in ameliorating hypertension-induced cardiac damage and investigate the underlying mechanisms involved. METHODS: Twenty spontaneously hypertensive rats (SHRs) were used to develope a hypertension-induced cardiac damage model. Another 10 Wistar Kyoto (WKY) rats were used as normotension group. Rats were administrated intragastrically QDG [0.9 g/(kg•d)] or an equivalent volume of pure water for 8 weeks. Blood pressure, histopathological changes, cardiac function, levels of oxidative stress and inflammatory response markers were measured. Furthermore, to gain insights into the potential mechanisms underlying the protective effects of QDG against hypertension-induced cardiac injury, a network pharmacology study was conducted. Predicted results were validated by Western blot, radioimmunoassay immunohistochemistry and quantitative polymerase chain reaction, respectively. RESULTS: The administration of QDG resulted in a significant decrease in blood pressure levels in SHRs (P<0.01). Histological examinations, including hematoxylin-eosin staining and Masson trichrome staining revealed that QDG effectively attenuated hypertension-induced cardiac damage. Furthermore, echocardiography demonstrated that QDG improved hypertension-associated cardiac dysfunction. Enzyme-linked immunosorbent assay and colorimetric method indicated that QDG significantly reduced oxidative stress and inflammatory response levels in both myocardial tissue and serum (P<0.01). CONCLUSIONS Both network pharmacology and experimental investigations confirmed that QDG exerted its beneficial effects in decreasing hypertension-induced cardiac damage by regulating the angiotensin converting enzyme (ACE)/angiotensin II (Ang II)/Ang II receptor type 1 axis and ACE/Ang II/Ang II receptor type 2 axis.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/pathology* ; Renin-Angiotensin System/drug effects* ; Rats, Inbred SHR ; Oxidative Stress/drug effects* ; Male ; Rats, Inbred WKY ; Blood Pressure/drug effects* ; Myocardium/pathology* ; Rats ; Inflammation/pathology*

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Aim To explore the mechanism of clar-ithromycin in treating inflammatory bowel disease(IBD)by inhibiting Kv1.3 channel protein in colonic epithelial cells.Methods A chronic IBD rat model was induced using dextran sulfate sodium(DSS)in vi-vo experiments,with clarithromycin intervention.The physical signs of each group of rats were observed,and the disease activity index(DAI)score and colonic mu-cosal damage index(CMDI)score were calculated.RT-qPCR was used to detect the levels of relevant cyto-kines in colonic tissue of rats.Flow cytometry was em-ployed to detect the relative proportions of immune cells in the peripheral blood and colonic tissue of each group of rats.Lipopolysaccharide(LPS)was used to establish an inflammation model of colon epithelial cells(NCM460)to clarify the inhibitory effect of clar-ithromycin on Kv1.3 channel protein.Results In vi-vo experiments:compared to the model group,the clar-ithromycin intervention group exhibited a reduced de-gree of weight loss(P＜0.01),and a significant de-crease in DAI scores(P＜0.01).There was an in-crease in colon length,a reduction in weight,and a de-crease in CMDI scores(P＜0.05).Levels of TNF-α,IL-1 β,and IL-6 in colon tissue were significantly re-duced(P＜0.01).The numbers of peripheral blood and colonic regulatory T lymphocytes(Th),cytotoxic T lymphocytes(CTL),natural killer cells(NK),B lym-phocytes(B),and dendritic cells(DC)were signifi-cantly decreased(P＜0.05).Clarithromycin reduced the expression of Kv1.3 channel protein in colon tissue(P＜0.05).In vitro experiments:compared to the model group,the clarithromycin group significantly pro-moted the proliferation of NCM460 cells(P＜0.01)and simultaneously significantly reduced the levels of TNF-α and IL-6 in cells(P＜0.05).Clarithromycin also reduced the expression of Kv1.3 channel protein in NCM460 cells(P＜0.05).Conclusions Clar-ithromycin may play an immunomodulatory role by in-hibiting the expression of Kv1.3 channel protein,re-ducing inflammation in the body,and playing a role in the treatment of IBD.

The study aims to examine the effects and potential mechanisms of disulfiram (DSF) on cardiac hypertrophic injury, focusing on the role of transforming growth factor-β-activated kinase 1 (TAK1)-mediated pan-apoptosis (PANoptosis). H9C2 cardiomyocytes were treated with angiotensin II (Ang II, 1 µmol/L) to establish an in vitro model of myocardial hypertrophy. DSF (40 µmol/L) was used to treat cardiomyocyte hypertrophic injury models, either along or in combination with the TAK1 inhibitor, 5z-7-oxozeaenol (5z-7, 0.1 µmol/L). We assessed cell damage using propidium iodide (PI) staining, measured cell viability with CCK8 assay, quantified inflammatory factor levels in cell culture media via ELISA, detected TAK1 and RIPK1 binding rates using immunoprecipitation, and analyzed the protein expression levels of key proteins in the TAK1-mediated PANoptosis pathway using Western blot. In addition, the surface area of cardiomyocytes was measured with Phalloidin staining. The results showed that Ang II significantly reduced the cellular viability of H9C2 cardiomyocytes and the binding rate of TAK1 and RIPK1, significantly increased the surface area of H9C2 cardiomyocytes, PI staining positive rate, levels of inflammatory factors [interleukin-1β (IL-1β), IL-18, and tumor necrosis factor α (TNF-α)] in cell culture media and p-TAK1/TAK1 ratio, and significantly up-regulated key proteins in the PANoptosis pathway [pyroptosis-related proteins NLRP3, Caspase-1 (p20), and GSDMD-N (p30), apoptosis-related proteins Caspase-3 (p17), Caspase-7 (p20), and Caspase-8 (p18), as well as necroptosis-related proteins p-MLKL, RIPK1, and RIPK3]. DSF significantly reversed the above changes induced by Ang II. Both 5z-7 and exogenous IL-1β weakened these cardioprotective effects of DSF. These results suggest that DSF may alleviate cardiac hypertrophic injury by inhibiting TAK1-mediated PANoptosis.
Animals ; MAP Kinase Kinase Kinases/physiology* ; Rats ; Myocytes, Cardiac/pathology* ; Disulfiram/pharmacology* ; Cardiomegaly ; Apoptosis/drug effects* ; Cell Line ; Angiotensin II ; Necroptosis/drug effects* ; Interleukin-1beta/metabolism* ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism* ; Lactones ; Resorcinols ; Zearalenone/administration & dosage*

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Aim To explore the mechanism of clar-ithromycin in treating inflammatory bowel disease(IBD)by inhibiting Kv1.3 channel protein in colonic epithelial cells.Methods A chronic IBD rat model was induced using dextran sulfate sodium(DSS)in vi-vo experiments,with clarithromycin intervention.The physical signs of each group of rats were observed,and the disease activity index(DAI)score and colonic mu-cosal damage index(CMDI)score were calculated.RT-qPCR was used to detect the levels of relevant cyto-kines in colonic tissue of rats.Flow cytometry was em-ployed to detect the relative proportions of immune cells in the peripheral blood and colonic tissue of each group of rats.Lipopolysaccharide(LPS)was used to establish an inflammation model of colon epithelial cells(NCM460)to clarify the inhibitory effect of clar-ithromycin on Kv1.3 channel protein.Results In vi-vo experiments:compared to the model group,the clar-ithromycin intervention group exhibited a reduced de-gree of weight loss(P＜0.01),and a significant de-crease in DAI scores(P＜0.01).There was an in-crease in colon length,a reduction in weight,and a de-crease in CMDI scores(P＜0.05).Levels of TNF-α,IL-1 β,and IL-6 in colon tissue were significantly re-duced(P＜0.01).The numbers of peripheral blood and colonic regulatory T lymphocytes(Th),cytotoxic T lymphocytes(CTL),natural killer cells(NK),B lym-phocytes(B),and dendritic cells(DC)were signifi-cantly decreased(P＜0.05).Clarithromycin reduced the expression of Kv1.3 channel protein in colon tissue(P＜0.05).In vitro experiments:compared to the model group,the clarithromycin group significantly pro-moted the proliferation of NCM460 cells(P＜0.01)and simultaneously significantly reduced the levels of TNF-α and IL-6 in cells(P＜0.05).Clarithromycin also reduced the expression of Kv1.3 channel protein in NCM460 cells(P＜0.05).Conclusions Clar-ithromycin may play an immunomodulatory role by in-hibiting the expression of Kv1.3 channel protein,re-ducing inflammation in the body,and playing a role in the treatment of IBD.

Objective:To analyze and evaluate the equity of children's health in countries along the"the Belt and Road",promote further attention to children's health in countries along the route,and promote cooperation and exchanges on children's health between China and countries along the"the Belt and Road".Methods:Using concentration index and concentration curve to measure overall equity,and using the Thiel index for intraregional and interregional euqity measurement.Results:The under-five mortality concentration index is 0.349 7,the concentration curve is below the absolute fair line.The Thiel index shows that inequality in low-income countries,lower-middle-income countries,upper-middle-income countries and high-income countries is the leading cause of child health inequities in the"the Belt and Road"countries.Conclusion:There is inequity in the health of children in countries along"the Belt and Road Initiative",countries along the"the Belt and Road"should take comprehensive measures to reduce the under-five mortality rate,at the same time strengthen international cooperation to further promote equity in children's health in"Belt and Road"countries.

Drug literacy is one of the important components of health literacy,and in recent years,the research on drug literacy has gradually deepened.The polypharmacy treatment and irrational drug use in the patients with HIV/AIDS has increased the burden of drug use,therefore improving the level of drug literacy is the key to maintain the medication efficacy,drug self-administered and safe medication.This paper summa-rized the definition,current status of assessment tools,influencing factors and interventional measures of drug literacy of HIV/AIDS patients in domestic and foreign research in order to provide reference for the develop-ment of assessment tools for drug literacy of HIV/AIDS patients,improve the level of drug literacy of pa-tients,and promote safe and rational use of drugs.

Objective:To evaluate the efficiency of the four domestic language models,ERNIE Bot,ChatGLM2,Spark Desk and Qwen-14B-Chat,all with a massive user base and significant social attention,in response to consultations about PCa-related perio-perative nursing and health education.Methods:We designed a questionnaire that includes 15 questions commonly concerned by patients undergoing radical prostatectomy and 2 common nursing cases,and inputted the questions into each of the four language models for simulation consultation.Three nursing experts assessed the model responses based on a pre-designed Likert 5-point scale in terms of accuracy,comprehensiveness,understandability,humanistic care,and case analysis.We evaluated and compared the performance of the four models using visualization tools and statistical analyses.Results:All the models generated high-quality texts with no mis-leading information and exhibited satisfactory performance.Qwen-14B-Chat scored the highest in all aspects and showed relatively sta-ble outputs in multiple tests compared with ChatGLM2.Spark Desk performed well in terms of understandability but lacked comprehen-siveness and humanistic care.Both Qwen-14B-Chat and ChatGLM2 demonstrated excellent performance in case analysis.The overall performance of ERNIE Bot was slightly inferior.All things considered,Qwen-14B-Chat was superior to the other three models in con-sultations about PCa-related perioperative nursing and health education.Conclusion:In PCa-related perioperative nursing,large language models represented by Qwen-14B-Chat are expected to become powerful auxiliary tools to provide patients with more medical expertise and information support,so as to improve the patient compliance and the quality of clinical treatment and nursing.