A prediction model for the risk of childhood allergic asthma was established through the analysis of public datasets. By using bioinformatics analysis methods, two datasets, GSE40732 and GSE40888, were selected, which included the whole-genome expression profile data of 222 children. Among them, GSE40732 was used as the training dataset to detect differentially expressed genes in peripheral blood mononuclear cells of children with the disease, and the master regulator analysis (MRA) algorithm was used to screen the master regulator genes in the inflammation-related pathway (GO: 0006954). After obtaining the master regulator genes, the expression of these master regulator genes in the GSE40732 and GSE40888 datasets was detected, and a prediction model was constructed through logistic regression, based on which risk scores were assigned to children. By comparing the risk scores of healthy children and children with the disease, the area under the curve (AUC) was used to evaluate the classification performance of the model. The average value of the risk scores of all children with the disease output by the model was calculated as the threshold. According to this threshold, the children with the disease in the two datasets were divided into high-risk and low-risk groups. The CIBERSORT algorithm was applied to analyze the infiltration of immune cells in the high-risk and low-risk groups, and the enrichment analysis of signaling pathways was completed using the msigdbr package in R software. The results showed that compared with healthy children, there were 377 up-regulated genes and 255 down-regulated genes in the peripheral blood mono-nuclear cells of children with the disease. The MRA algorithm analysis showed that there were five genes ( MUC5B, CST4, CCR7, TNF-α, and THBS1) that were the master regulator genes in the regulatory network. Risk score= MUC5B×3.47 +CST4×2.17 +CCR7×0.59 +TNF- α×0.54 +THBS1×1.67. The AUC in the GSE40732 and GSE40888 datasets were 0.874 and 0.682, respectively. Compared with the low-risk group, the resting memory CD4 +T cells and regulatory T cells in the peripheral blood of children with the disease in the high-risk group significantly decreased ( P<0.05), and both the IL-33 and IL-13 pathways were highly enriched. In conclusion, the model constructed in this study has a good predictive efficiency for the risk of allergic asthma and also has a certain effect on risk stratification.

A prediction model for the risk of childhood allergic asthma was established through the analysis of public datasets. By using bioinformatics analysis methods, two datasets, GSE40732 and GSE40888, were selected, which included the whole-genome expression profile data of 222 children. Among them, GSE40732 was used as the training dataset to detect differentially expressed genes in peripheral blood mononuclear cells of children with the disease, and the master regulator analysis (MRA) algorithm was used to screen the master regulator genes in the inflammation-related pathway (GO: 0006954). After obtaining the master regulator genes, the expression of these master regulator genes in the GSE40732 and GSE40888 datasets was detected, and a prediction model was constructed through logistic regression, based on which risk scores were assigned to children. By comparing the risk scores of healthy children and children with the disease, the area under the curve (AUC) was used to evaluate the classification performance of the model. The average value of the risk scores of all children with the disease output by the model was calculated as the threshold. According to this threshold, the children with the disease in the two datasets were divided into high-risk and low-risk groups. The CIBERSORT algorithm was applied to analyze the infiltration of immune cells in the high-risk and low-risk groups, and the enrichment analysis of signaling pathways was completed using the msigdbr package in R software. The results showed that compared with healthy children, there were 377 up-regulated genes and 255 down-regulated genes in the peripheral blood mono-nuclear cells of children with the disease. The MRA algorithm analysis showed that there were five genes ( MUC5B, CST4, CCR7, TNF-α, and THBS1) that were the master regulator genes in the regulatory network. Risk score= MUC5B×3.47 +CST4×2.17 +CCR7×0.59 +TNF- α×0.54 +THBS1×1.67. The AUC in the GSE40732 and GSE40888 datasets were 0.874 and 0.682, respectively. Compared with the low-risk group, the resting memory CD4 +T cells and regulatory T cells in the peripheral blood of children with the disease in the high-risk group significantly decreased ( P<0.05), and both the IL-33 and IL-13 pathways were highly enriched. In conclusion, the model constructed in this study has a good predictive efficiency for the risk of allergic asthma and also has a certain effect on risk stratification.

Objective:To determine the relationship between health self-empowerment level and health self-management skill in older adults.Methods:A household survey was conducted from June 2020 to April 2021, among older adults in two communities each in Zhuzhou and Changsha selected by stratified cluster sampling. Data were collected through a general questionnaire, simplified. Elders Health Empowerment Scale, and Rating Scale of Health Self-Management Skill for Adults. A total of 444 questionnaires were distributed, of which 425 were valid. Pearson correlation and hierarchical regression analyses were used to analyze the relationship between health self-empowerment level and health self-management skill.Results:Final sample included 425 elderly people. Health self-empowerment was at medium to high level (30.9±5.6), whereas health self-management was at medium level (146.2±21.7); and the two factors were significantly correlated (Pearson coefficient was 0.724). After controlling for confounding factors (demographic characteristics), health self-empowerment independently accounted for 34.3% of the variation in health self-management ability.Conclusion:Health self-empowerment positively affects health self-management ability among older adults, which suggests the importance of stimulating health self-empowerment awareness in the process of health self-management.

Objective To evaluate 3D visualization technology in the preoperative planning of hepatic malignancy surgery.Methods The clinical data of 42 patients with hepatic malignancy undergoing radical resection after 3D reconstruction assessment from Feb 2015 to Feb 2018 in our center was retrospectively studied.The predicted resected liver volume were compared with that of resected specimen.Results Surgery was successful in all the 42 patients,and there were no operative deaths.The average operation time was (303 ± 109) minutes,the average intraoperative blood loss was (510 ±482) ml,and 28 patients had intraoperative hepatic inflew occlusion.Postoperative Clavien Ⅲ complications occurred in 4 cases.In terms of the resection liver volume,there was no significant difference between the predicted results (PELV) and actual results [resection liver volume (AELV):(1 143 ± 584) ml vs.(1 091 ± 570) ml,t =0.414,P ＞ 0.05].There is a strong positive correlation between AELV and PELV (r =0.996,P ＜ 0.01).PELV was highly consistent with AELV data (ICC =0.998).Conclusion Three-dimensional visualization technology can accurately reflect the anatomic relationship between intrahepatic tumors and vessels,and correctly assess liver volume,guide surgical resection,thus,it can instruct radical resection of liver malignancy.

Objective To assess the value of Stroop smartphone application (EncephalApp-Stroop) in screening minimal hepatic encephalopathy (MHE).Methods Ninety hepatitis B cirrhotic patients without overt hepatic encephalopathy (OHE),admitted to our hospital from January 2015 to February 2016,and other 90 healthy volunteers were chosen in our study.Psychometric hepatic encephalopathy scale (PHES) and EncephalApp-Stroop were performed.Normal reference value formulas were established based on the 5 psychometric tests at PHES in healthy controls.EncephalApp-Stroop contained "off'state (neutral stimuli) and "on" state (incongruent stimuli).The time of completing five correct runs and the number of rtms in "on" and "off'states were calculated.PHES was used to diagnose MHE as gold standard to compare the results of OHE patients and healthy subjects and the results of patients with/without MHE.EncephalApp-Stroop results were compared with PHES results by receiver operator characteristic analysis.Results OHE patients had significantly longer time than healthy subjects in Stroop "on" and "off'states (P＜0.05).Traditionally,the total scores of PHES below-4 were diagnosed as MHE,and all the 90 patients taking PHES as gold standard,52 patients were diagnosed as MHE.The time at Stroop "on" and "off'states and the number of trials completing 5 correct runs were longer/larger in patients with MHE as compared with those in patients without MHE,with significant differences (P＜0.05).The validation cohort of EncephalApp-Stroop showed 87％ sensitivity and 79％ specificity.Conclusion The EncephalApp-Stroop is a valid,short and reliable tool for screening MHE.

Liver cancer remains one of the leading cause of cancer death in the world.Animal models, especially mouse models, are important tools for studying the biological characteristics, pathogenesis, new drug screening and therapy of liver cancer.Up to now, although the development of various animal models accelerates the research of liver cancer, all the existing models have their own disadvantages.Lacking of economical and applicable animal models that can mimic the human liver cancer seriously restrict the further study of liver cancer.With the development of genetically modified technologies, it provides a fast, easy and reliable method to establish liver cancer models.In this review, we describe the different types of mouse models used in liver cancer research, with emphasis on genetically engineered mice used in this field, which may open an avenue for functional cancer genomics and generation of liver cancer models by using gene editing technologies.