ObjectiveTo explore the mechanism by which Xiaoyaosan regulates HPT axis dysfunction in the rat model with the syndrome of liver depression and spleen deficiency by observing its effect on the glycoprotein hormone α-subunit （CGA）/glutathione peroxidase 2 （GPX2）/thyroid-stimulating hormone β-subunit （TSHβ） pathway for thyroid hormone synthesis. MethodsSeventy-two male SD rats were randomized into six groups： normal， model， high-dose （16.7 g·kg^-1）， medium-dose （8.35 g·kg^-1）， and low-dose （4.175 g·kg^-1） Xiaoyaosan， and fluoxetine （0.001 8 g·kg^-1） groups， with 12 rats in each group. The rat model of liver depression and spleen deficiency was induced by chronic restraint stress for 21 days. The intervention groups were treated with Xiaoyaosan decoctions or fluoxetine suspension， respectively. After modeling， hematoxylin-eosin staining was employed to observe morphological changes in the thyroid and pituitary tissue of the rats. Serum levels of triiodothyronine （T3）， tetraiodothyronine （T4）， and thyroid-stimulating hormone （TSH） were measured by enzyme-linked immunosorbent assay （ELISA）. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） and Western blot were employed to determine the mRNA and protein levels， respectively， of TSH receptor （TSHR） in the thyroid tissue， thyrotropin-releasing hormone receptor （TRHR） and TSHβ in the pituitary tissue， and thyrotropin-releasing hormone （TRH）， CGA， GPX2， and TSHβ in the hypothalamic tissue. ResultsCompared with the normal group， the model group showed significant atrophy and irregularity of thyroid follicles， a marked reduction in colloid secretion， extensive vacuolar degeneration of adenocytes in the anterior pituitary， lowered serum levels of T3， T4， and TSH （P<0.01）， and down-regulated mRNA and protein levels of TSHR in the thyroid tissue， TRHR and TSHβ in the pituitary tissue， and TRH， CGA， GPX2， and TSHβ in the hypothalamic tissue （P<0.01）. Compared with the model group， high- and medium-dose Xiaoyaosan and fluoxetine alleviated the pathological changes in the thyroid and pituitary tissue， outperforming the low-dose Xiaoyaosan group. Moreover， they elevated the serum levels of T3， T4， and TSH （P<0.05， P<0.01）. The serum TSH level was also elevated in the low-dose Xiaoyaosan group （P<0.05）. The mRNA and protein levels of TSHR in the thyroid， TRHR and TSHβ in the pituitary， and TRH， CGA， GPX2， and TSHβ in the hypothalamus were up-regulated in the high- and medium-dose Xiaoyaosan groups （P<0.05， P<0.01）. Additionally， the mRNA and protein levels of TSHβ in the hypothalamus were up-regulated in the low-dose Xiaoyaosan group （P<0.01）. In the fluoxetine group， the mRNA and protein levels of TSHR in the thyroid， TRHR in the pituitary， and TRH， CGA， and GPX2 in the hypothalamus were up-regulated （P<0.05， P<0.01）. ConclusionThe downregulation of CGA/GPX2/TSHβ pathway may be one of the biological mechanisms underlying HPT axis dysfunction in the rat model with the syndrome of liver depression and spleen deficiency. Xiaoyaosan may regulate the HPT axis dysfunction by up-regulating the CGA/GPX2/TSHβ pathway.

Objective: To investigate the mechanism of action of curcumol on mitochondrial autophagy in hepatic stellate cells and its molecular mechanism against liver fibrosis. Methods : Hepatic stellate cells were divided into blank group, model group(lipopolysaccharide 5 mg/L), and low, medium and high curcumol group(12.5, 25 and 50 mg/L); Thiazolyland(MTT) was used to detect the effects of curcumol on the viability of hepatic stellate cells; flow cytometry was used to detect the effects of curcumol on apoptosis of hepatic stellate cells; 5,5′,6,6′-Tetrachloro-1,1′,3,3′-tetraethylimidacarbocyanine iodide(JC-1) was used to detect the mitochondrial membrane potential; effects of curcumol on mitochondrial morphology and autophagosome were detected by transmission electron microscopy; effect of curcumol on mitochondrial localisation were detected by fluorescent probe; Immunoblotting assay was performed to detect the effects of curcumin on PTEN-induced putative kinase 1(PINK1), Parkinson's disease protein(Parkin), microtubule-associated protein light chain 3(LC3), autophagy-associated protein(Beclin1), mitochondrial inner membrane translocase 23(Timm23), mitochondrial outer membrane translocase 20(Tomm20), Bcl-2 associated X protein(Bax), B lymphocytoma-2(Bcl2), cleaved-cysteine protease 3(Caspase3), α-smooth muscle actin(ɑ-SMA), collagen type Ⅰ(Collagen Ⅰ), and collagen type Ⅲ(Collagen Ⅲ) protein expression. Results : Compared with the blank control group, cell proliferation rate, Caspase3, Bcl2, LC3Ⅱ, Beclin1, PINK1, Parkin, ɑ-SMA, CollagenⅠ, CollagenⅢ proteins significantly increased in the model group(P<0.01), co-localisation of mitochondria and lysosomes increased, and the number of mitochondrial autophagosome significantly increased(P<0.01), while Timm23 and Tomm20 proteins, mitochondrial membrane potential decreased significantly(P<0.01), apoptosis rate decreased, and Bax protein expression decreased. Compared with the model group, after curcumol intervention, cell proliferation rate, Bcl2, Timm23, Tomm20, α-SMA, CollagenⅠ and CollagenⅢ protein expression significantly decreased in the curcumol low-, medium-and high-concentration groups(P<0.01), and the mitochondrial membrane potential significantly decreased(P<0.01), whereas apoptosis rate, Caspase3, Bax, LC3Ⅱ, Beclin1, PINK1 and Parkin proteins significantly increased(P<0.05), the co-localisation of mitochondria and lysosomes increased, and the number of mitochondrial autophagosome significantly increased(P<0.01). Conclusion Curcumol exerts ameliorative effects on hepatic fibrosis by modulating mitochondrial hyperautophagy mediated by the PINK1/Parkin signaling pathway, and promoting hepatic stellate cell apoptosis.

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.

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.

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 explore the experience and feeling of experiential health education for complications in middle-aged and elderly hypertensive patients,and to provide basis for health education model for hypertensive patients. Methods By objective sampling method,10 middle-aged and elderly hypertensive patients who received experiential health education for hypertension complications in Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine from August to September 2023 were selected for semi-structured interviews,and the interview data were sorted out by Colaizzi seven-step analysis method. Results Middle-aged and elderly hypertension patients with complications of experiential health education experience can be summarized as two themes,seven subthemes,namely:the perceived benefits (improve the cognition of hypertension,complications of experience,behavior change and cognitive clarity,promote interpersonal communication),challenges (the lack of early cognitive,the limitations of traditional health education,body burden concerns). Conclusion Worry about long-term complications and lack of knowledge of disease management are common after receiving experiential health education on complications. Therefore,when designing the experiential health education program,clinical medical staff should fully consider the physiological and psychological characteristics of patients,provide personalized support,and gradually guide patients to adapt to the education process.

Objective To explore the experience and feeling of experiential health education for complications in middle-aged and elderly hypertensive patients,and to provide basis for health education model for hypertensive patients. Methods By objective sampling method,10 middle-aged and elderly hypertensive patients who received experiential health education for hypertension complications in Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine from August to September 2023 were selected for semi-structured interviews,and the interview data were sorted out by Colaizzi seven-step analysis method. Results Middle-aged and elderly hypertension patients with complications of experiential health education experience can be summarized as two themes,seven subthemes,namely:the perceived benefits (improve the cognition of hypertension,complications of experience,behavior change and cognitive clarity,promote interpersonal communication),challenges (the lack of early cognitive,the limitations of traditional health education,body burden concerns). Conclusion Worry about long-term complications and lack of knowledge of disease management are common after receiving experiential health education on complications. Therefore,when designing the experiential health education program,clinical medical staff should fully consider the physiological and psychological characteristics of patients,provide personalized support,and gradually guide patients to adapt to the education process.