Iron is an essential trace element for the human body and is critical for vital cellular processes,such as DNA synthesis,respiration,and oxygen transport.The body maintains iron homeostasis through a coordinated balance of absorption,utilization,storage,and distribution.Both iron deficiency and excess can lead to pathologies,with the latter triggering lipid peroxidation and DNA mutations via the Fenton reaction,potentially causing iron-induced cell death in severe cases.Although iron overload can inflict severe damage on multiple organs,including the brain,liver,spleen,heart,ovaries,and kidneys,the mechanisms that regulate iron homeostasis in response to overload are not fully understood.Various animal models have been developed to help elucidate these mechanisms,each reflecting different aspects of iron overload relevant to human diseases,and selection of the most appropriate animal model is needed for the accurate simulation of the pathological and physiological states associated with human iron overload-related diseases.This review synthesizes recent literature on animal models pertinent to iron overload,to offer insights to support the development and analysis of models for diseases related to iron overload.

Objective:To explore the the possibility and potential mechanism of Coptis chinensis in treating caries based on network pharmacology and molecular docking technology.Methods:The active ingredients of Coptis chinensis and the targets of the active in-gredients and caries were obtained through a variety of databases.The intersection targets of Coptis chinensis and caries were obtained by VENNY.Based on the target of the intersection,the protein-protein interaction(PPI)networks relationship diagram was formed on the STRING platform.The core target diagram was formed through the Cytoscape software,and the Coptis chinensis-active ingre-dient-target network diagram was constructed.The intersection targets are analyzed by GO and KEGG.The core targets of Coptis chinensis active ingredients in treating caries was analyzed by molecular docking.Results:The 11 Coptis chinensis active ingredi-ents,465 targets of Coptis chinensis active ingredients and 1 160 targets of caries were obtained from the database.After analysis,71 targets of Coptis chinensis-caries intersection and 12 core targets were obtained.The potential therapeutic effect of Coptis chinensis on caries involved relaxin,phosphatidylinositol 3-kinase-protein kinase B(PI3K-Akt),mitogen activated protein kinase(MAPK),ras-re-lated protein 1(RAP1)and other signaling pathways.Berberine,one of the main active components of Coptis chinense,had strong binding activity on epidermal growth factor receptor(EGFR),matrix metalloproteinase 9(MMP9)and other core targets.Coptis chinen-sis and its active components can play a role in the treatment of caries through EGFR,MMP9 and PI3K-AKT,MAPK and other path-ways.Conclusion:Coptis chinensis and its active components can regulate several targets and signaling pathways such as EGFR,MMP9,PI3K-AKT and MAPK.Coptis chinensis may play a role in the prevention and treatment of caries through multiple pathways.

Iron is an essential trace element for the human body and is critical for vital cellular processes,such as DNA synthesis,respiration,and oxygen transport.The body maintains iron homeostasis through a coordinated balance of absorption,utilization,storage,and distribution.Both iron deficiency and excess can lead to pathologies,with the latter triggering lipid peroxidation and DNA mutations via the Fenton reaction,potentially causing iron-induced cell death in severe cases.Although iron overload can inflict severe damage on multiple organs,including the brain,liver,spleen,heart,ovaries,and kidneys,the mechanisms that regulate iron homeostasis in response to overload are not fully understood.Various animal models have been developed to help elucidate these mechanisms,each reflecting different aspects of iron overload relevant to human diseases,and selection of the most appropriate animal model is needed for the accurate simulation of the pathological and physiological states associated with human iron overload-related diseases.This review synthesizes recent literature on animal models pertinent to iron overload,to offer insights to support the development and analysis of models for diseases related to iron overload.

Objective:To explore the the possibility and potential mechanism of Coptis chinensis in treating caries based on network pharmacology and molecular docking technology.Methods:The active ingredients of Coptis chinensis and the targets of the active in-gredients and caries were obtained through a variety of databases.The intersection targets of Coptis chinensis and caries were obtained by VENNY.Based on the target of the intersection,the protein-protein interaction(PPI)networks relationship diagram was formed on the STRING platform.The core target diagram was formed through the Cytoscape software,and the Coptis chinensis-active ingre-dient-target network diagram was constructed.The intersection targets are analyzed by GO and KEGG.The core targets of Coptis chinensis active ingredients in treating caries was analyzed by molecular docking.Results:The 11 Coptis chinensis active ingredi-ents,465 targets of Coptis chinensis active ingredients and 1 160 targets of caries were obtained from the database.After analysis,71 targets of Coptis chinensis-caries intersection and 12 core targets were obtained.The potential therapeutic effect of Coptis chinensis on caries involved relaxin,phosphatidylinositol 3-kinase-protein kinase B(PI3K-Akt),mitogen activated protein kinase(MAPK),ras-re-lated protein 1(RAP1)and other signaling pathways.Berberine,one of the main active components of Coptis chinense,had strong binding activity on epidermal growth factor receptor(EGFR),matrix metalloproteinase 9(MMP9)and other core targets.Coptis chinen-sis and its active components can play a role in the treatment of caries through EGFR,MMP9 and PI3K-AKT,MAPK and other path-ways.Conclusion:Coptis chinensis and its active components can regulate several targets and signaling pathways such as EGFR,MMP9,PI3K-AKT and MAPK.Coptis chinensis may play a role in the prevention and treatment of caries through multiple pathways.

Objective To evaluate the value of radiomics models and comprehensive models based on multiparametric magnetic resonance imaging(mpMRI)in predicting the expression of human epidermal growth factor receptor 2(HER2)in bladder cancer(BCa).Methods A total of 76 pathologically confirmed BCa patients undergoing pelvic mpMRI during Jan.2022 and Nov.2024 at the Affiliated Zhongda Hospital of Southeast University were retrospectively included.After the volume of interest(VOI)was sketched,7 modal features were obtained,including T2WI,DWI,DCE,T2WI+DWI,T2WI+DCE,DWI+DCE,and T2WI+DWI+DCE,which were analyzed with logistic regression to obtain the predictive values.After that,the best sequences were screened with receiver operating characteristic(ROC)curves,and then combined with support vector machine,logistic regression,K-nearest neighbor,plain Bayes and adaptive enhancement,to construct the radiomics prediction models.Logistic regression analysis was used to screen the predictors of high HER 2 expression and to construct a comprehensive prediction model and a nomogram.Finally,decision curve analysis(DCA)was used to quantify the clinical benefits.Results Among the radiomics models based on the T2WI+DWI+DCE sequence,the AdaBoost model demonstrated the best predictive performance,with area under the ROC curve(AUC)being 0.863(95％CI:0.807-0.920)in the training set and 0.716(95％CI:0.601-0.830)in the validation set.Based on radiomics features and clinical imaging characteristics,logistic regression analysis identified tumor pedicle and risk group as the predictors of high HER2 expression.The comprehensive prediction model based on the two factors achieved the AUC of 0.869(95％CI:0.772-0.965)in the training set and 0.875(95％CI:0.712-0.986)in the validation set.Conclusion The radiomics model based on the T2WI+DWI+DCE sequence has high accuracy in predicting HER2 expression,outperforming single-sequence models.The nomogram based on the comprehensive prediction model has high clinical decision-making efficacy and is useful for non-invasive identification of HER2 expression.

Objective To evaluate the value of radiomics models and comprehensive models based on multiparametric magnetic resonance imaging(mpMRI)in predicting the expression of human epidermal growth factor receptor 2(HER2)in bladder cancer(BCa).Methods A total of 76 pathologically confirmed BCa patients undergoing pelvic mpMRI during Jan.2022 and Nov.2024 at the Affiliated Zhongda Hospital of Southeast University were retrospectively included.After the volume of interest(VOI)was sketched,7 modal features were obtained,including T2WI,DWI,DCE,T2WI+DWI,T2WI+DCE,DWI+DCE,and T2WI+DWI+DCE,which were analyzed with logistic regression to obtain the predictive values.After that,the best sequences were screened with receiver operating characteristic(ROC)curves,and then combined with support vector machine,logistic regression,K-nearest neighbor,plain Bayes and adaptive enhancement,to construct the radiomics prediction models.Logistic regression analysis was used to screen the predictors of high HER 2 expression and to construct a comprehensive prediction model and a nomogram.Finally,decision curve analysis(DCA)was used to quantify the clinical benefits.Results Among the radiomics models based on the T2WI+DWI+DCE sequence,the AdaBoost model demonstrated the best predictive performance,with area under the ROC curve(AUC)being 0.863(95％CI:0.807-0.920)in the training set and 0.716(95％CI:0.601-0.830)in the validation set.Based on radiomics features and clinical imaging characteristics,logistic regression analysis identified tumor pedicle and risk group as the predictors of high HER2 expression.The comprehensive prediction model based on the two factors achieved the AUC of 0.869(95％CI:0.772-0.965)in the training set and 0.875(95％CI:0.712-0.986)in the validation set.Conclusion The radiomics model based on the T2WI+DWI+DCE sequence has high accuracy in predicting HER2 expression,outperforming single-sequence models.The nomogram based on the comprehensive prediction model has high clinical decision-making efficacy and is useful for non-invasive identification of HER2 expression.

Objective:To explore the difficulties and needs of wound ostomy incontinence specialist nurses when applying convex baseplates, with the aim of providing a basis for standardizing ostomy care.Methods:Purposeful sampling was used to select 13 wound ostomy incontinence specialist nurses from tertiary hospitals in Hebei, Jilin, Hunan, Beijing, and Tianjin for semi-structured interviews between November and December 2024. Content analysis was used to analyze the data.Results:Two main themes were identified: application dilemmas and application needs. Application dilemmas included four subthemes: insufficient understanding of convex baseplate characteristics, unclear indications and contraindicated populations, lack of unified standards for assessment timing, and inadequate awareness of health education. Application needs included two subthemes: urgent need for related evidence-based evidence and a desire for clinical guidelines and expert consensus.Conclusions:Currently, wound ostomy incontinence specialist nurses in China face challenges in understanding the characteristics of convex baseplates, scope of application, identifying contraindications, determining the optimal timing for application assessment, and providing patient education. Administrators should develop clinical application standards for convex baseplates based on these challenges. Furthermore, researchers should develop relevant clinical guidelines or expert consensus for convex baseplate application in China, providing a basis for the standardized use of ostomy care products.

Objective:To explore the difficulties and needs of wound ostomy incontinence specialist nurses when applying convex baseplates, with the aim of providing a basis for standardizing ostomy care.Methods:Purposeful sampling was used to select 13 wound ostomy incontinence specialist nurses from tertiary hospitals in Hebei, Jilin, Hunan, Beijing, and Tianjin for semi-structured interviews between November and December 2024. Content analysis was used to analyze the data.Results:Two main themes were identified: application dilemmas and application needs. Application dilemmas included four subthemes: insufficient understanding of convex baseplate characteristics, unclear indications and contraindicated populations, lack of unified standards for assessment timing, and inadequate awareness of health education. Application needs included two subthemes: urgent need for related evidence-based evidence and a desire for clinical guidelines and expert consensus.Conclusions:Currently, wound ostomy incontinence specialist nurses in China face challenges in understanding the characteristics of convex baseplates, scope of application, identifying contraindications, determining the optimal timing for application assessment, and providing patient education. Administrators should develop clinical application standards for convex baseplates based on these challenges. Furthermore, researchers should develop relevant clinical guidelines or expert consensus for convex baseplate application in China, providing a basis for the standardized use of ostomy care products.

Objective To construct risk models for predicting the occurrence of high altitude de-acclimatization syndrome(HADAS)in the population returning from the plateau to the plain based on different machine learning algorithms and validate the predicting efficiency of these models.Methods Field or online surveys were conducted on the individuals who had ended their high-altitude living and returned to the plain areas from November 2020 to February 2024.Basic information,chronic mountain sickness(CMS),HADAS symptoms and other data were collected.With the inclusion and exclusion criteria,totally 1 095 individuals were subjected and assigned into the modeling group.Positive events were defined as HADAS score>5.Then the modelling group was divided into a training set(n=766)and an internal test set(n=329)in a 7∶3 ratio.Least absolute shrinkage and selection operator(LASSO)regression was used to select independent variables.Risk prediction models for high-altitude adaptation symptoms were built based on 8 machine learning methods,including multiple factor logistic regression(LR),decision tree(DT),random forest(RF),eXtreme gradient boosting(XGB),support vector machine(SVM),K-nearest neighbor(KNN),light gradient boosting(LGB)and na?ve bayes(NB).The models were compared and evaluated using receiver operating characteristic(ROC)curves,calibration curves and confusion matrices in the internal test set.The final model was presented using a nomogram or Shapley additive explanations(SHAP)algorithm.In August 2024,another 132 individuals who returned to the plains and met the same criteria were recruited and served as the external validation group.Results There were 549 individuals(50.14%)out of the 1 095 subjects having HADAS symptoms.LASSO regression identified CMS score,age and duration of high-altitude residence as significant predictors.Among the 8 machine learning algorithms,the LR model was identified as the best,with an area under the curve(AUC)value of 0.819(95%CI:0.789～0.850)and 0.841(95%CI:0.799～0.884),and an F1 score of 0.801 in the internal test set,respectively,and the AUC value and F1 score of the LR model were the largest among the 8 models in the internal test set.Spiegelhalter Z test of the calibration curve of the LR model indicated that its P=0.703 in the training set while P=0.281 in the internal test set.The AUC value of the LR model was 0.867(95%CI:0.765～0.969)in the external validation set.Conclusion The LR model constructed based on indicators including CMS score,age and duration of high-altitude residence has a good overall performance in the internal test set,and good discriminating effect in the external validation set.The constructed nomogram is convenient for application.

Long-term spaceflight exposes astronauts to multiple extreme environmental factors, such as cosmic radiation, microgravity, social isolation, and circadian rhythm disruption, that markedly increase the risk of depressive symptoms, posing a direct threat to mental health and mission safety. However, the underlying biological mechanisms remain complex and incompletely understood. The potential mechanisms of spaceflight-induced depressive symptoms involve multiple domains, including alterations in brain structure and function, dysregulation of neurotransmitters and neurotrophic factors, oxidative stress, neuroinflammation, neuroendocrine system imbalance, and gut microbiota disturbances. Collectively, these changes may constitute the biological foundation of depressive in astronauts during spaceflight. Space-related stressors may increase the risk of depressive symptoms through several pathways: impairing hippocampal neuroplasticity, suppressing dopaminergic and serotonergic system function, reducing neurotrophic factor expression, triggering oxidative stress and inflammatory responses, activating the hypothalamic-pituitary-adrenal axis, and disrupting gut microbiota homeostasis. Future research should integrate advanced technologies such as brain-computer interfaces to develop individualized monitoring and intervention strategies, enabling real-time detection and effective prevention of depressive symptoms to safeguard astronauts' psychological well-being and mission safety.
Space Flight ; Humans ; Astronauts/psychology* ; Depression/physiopathology* ; Gastrointestinal Microbiome ; Weightlessness/adverse effects* ; Oxidative Stress ; Brain/physiopathology* ; Hypothalamo-Hypophyseal System ; Neuronal Plasticity ; Pituitary-Adrenal System