Effect of macrophage polarization regulated by miR-29b,B7H3 on CD4^(+)T cell differentiation in asthma

Objective:To explore the mechanism that miR-29b and B7H3 regulate the polarization of macrophages and thus affect the differentiation of CD4^(+)T.Methods:1.PBMC was extracted from peripheral blood mononuclear cells of children with asthma and normal children in the affiliated Children's Hospital of Soochow University,and RNA was extracted and reverse transcribed.The expression of miR-29b and B7H3mRNA was determined by real-time quantitative polymerase chain reaction(Q-PCR).The family history of asthma and history of allergic diseases were collected.2.THP-1 cells were induced into macrophages,miR-29b interference,miR-29b overexpression and normal control were induced by LV526,LV527 and NC virus infection.After 24 hours of culture,the cells were collected to detect the expression of STAT3 and B7H3 genes and proteins.3.It was verified that STAT3 was the target gene of miR-29b:after inoculating THP-1 cells and culturing with PMA with final concentration of 50ng/ml for 6 hours,the macrophages without PMA were cultured for 24 hours,then the macrophages infected by LV528,LV529 and NC virus were induced to form miR-29b interference,miR29b overexpression and normal control group.Luciferase analysis was performed at 48 hours to verify that STAT3 was the target gene of miR-29b.STAT3-3'UTR luciferase reporter gene plasmids were constructed and divided into three groups:"miR-29b+STAT3-3'UTR","miR-29b+STAT3-mut-3'UTR"and"miR-29b+luciferase empty load".4.Macrophages with different treatments were co-cultured with initial T cells for 3 days.The relative expressions of T-bet,GATA3 and ROR-γt were detected by Q-PCR.Result:1.The incidence of allergic disease in the acute attack group(68%)was higher than that in the other two groups(34.8%,33.3%),and the family history of asthma in the normal group(0%)was much lower than that in the other two groups(52%,60.9%).The difference was statistically significant(P<0.05).2.The expression of B7H3 in PBMC in acute attack group was higher than that in non-acute attack group and normal group.The expression of miR-29b in PBMC in normal group was significantly higher than that in non-acute attack group and acute attack group(P<0.0001).The expression of miR-29b in non-acute attack group was significantly higher than that in acute attack group(P=0.007).3.After silencing the expression of miR-29b,IL-4Rα,IL-4,IL-5,IL-13 and CD206 of macrophages increased significantly,while IFN-γdecreased,suggesting that miR-29b can promote the polarization of macrophages to M2.4.The overexpression of miR-29b,STAT3 and B7H3 gene and protein level in macrophages decreased,while the increase of miR-29b,STAT3 and B7H3 gene and protein expression was inhibited.5.There was a significant positive correlation between the expression of STAT3 and B7H3mRNA in macrophages(r=0.9737,P<0.0001).6.STAT3 is the target gene of miR-29b.7.Co-culture of macrophages with CD4^(+)T cells can promote the differentiation of primary T cells,namely Th 0 cells,into Th2,and the promoting effect of macrophages with downregulation of miR-29b is more obvious.Conclusion:The expression of miR-29b in PBMC of children with asthma is lower than that of normal children,while the expression of B7H3 is higher than that of normal children.It is speculated that miR-29b has a protective effect on children with asthma,while B7H3 aggravates the inflammatory response.Down-regulation of miR-29b,in macrophages can promote macrophages to M2 polarization,increase the expression of B7H3 and STAT3 in macrophages,make Th0 cells differentiate into Th2 cells,and aggravate the inflammatory response in patients with asthma.

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

This study investigates the performance of four machine learning(ML)algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network(BBN)learning software Netica.The BBN structures that were developed by ML algorithms-K2,hill climbing(HC),tree augmented naive(TAN)Bayes,and Tabu search were adopted to perform parameter learning in Netica,thereby fixing the BBN models.The performance measure indexes,namely,overall accuracy(OA),precision,recall,F-measure,and area under the receiver operating characteristic curve,were used to evaluate the training and testing BBN models’performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models.The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors,whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential.The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability:Exploration from historical data

The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability(LLDV)when determining whether liquefaction is likely to cause damage at the ground's surface.This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network(BBN)methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model.The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learming(ML)algorithm-K2 and domain knowledge(DK)data fusion methodology.Compared with the C4.5 decision tree-J48 model,naive Bayesian(NB)classifier,and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen's kappa coefficient,the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage.The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations,and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development.The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling.This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefed sites based on an engineering point of view.