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Abstract Background The proliferation of airway smooth muscle cells (ASMCs) contributes to the contractility and inflammation in the pathophysiology of asthma. This intrigued us to clarify the effect of microRNA (miR)-224-5p on biological characteristics of ASMCs in mice with asthma-like airway inflammation and responses through the FHL1-dependent MAPK pathway. Methods An ovalbumin (OVA)-induced asthma mouse model was established, where ASMCs were isolated. The expression of FHL1 was determined in asthmatic mice. Artificial modulation of FHL1 expression was performed to explore its effect on airway inflammation of asthmatic mice and ASMC proliferation and apoptosis. Afterwards, we analyzed the interaction among miR-224-5p, FHL1 and the MAPK pathway, and explored their combined impacts on airway inflammation of asthmatic mice and ASMC proliferation and apoptosis. Results FHL1 was highly expressed and miR-224-5p was poorly expressed in asthmatic mice. FHL1 was verified to be a target of miR-224-5p. Loss of FHL1 function reduced airway inflammation in asthmatic mice and proliferation of ASMCs while inducing their apoptosis. Besides, miR-224-5p inhibited the MAPK pathway by binding to FHL1. Overexpression of miR-224-5p relieved airway inflammation, inhibited ASMC proliferation, and increased apoptosis, which could be reversed by overexpression of FHL1. Conclusion Altogether, miR-224-5p inhibited airway inflammation in asthmatic mice and ASMC proliferation through blocking the MAPK pathway by down-regulating FHL1.

College students learn words always under both teachers' and school administrators' control. Based on multi-modal discourse analysis theory, the analysis of English words under the synergy of different modalities, students improve the motivation and effectiveness of word learning, but there are still some problems, such as the lack of visual modal memory of pictures, incomplete word meanings, little interaction between users, and lack of resource expansion function. To this end, this paper proposes a stepped image semantic segmentation network structure based on multi-scale feature fusion and boundary optimization. The network aims at improving the accuracy of the network model, optimizing the spatial pooling pyramid module in Deeplab V3+ network, using a new activation function Funnel ReLU (FReLU) for vision tasks to replace the original non-linear activation function to obtain accuracy compensation, improving the overall image segmentation accuracy through accurate prediction of the boundaries of each class, reducing the intra-class error in the prediction results. The accuracy compensation is obtained by replacing the original linear activation function with FReLU. Experimental results on the Englishhnd dataset demonstrate that the improved network can achieve 96.35% accuracy for English characters with the same network parameters, training data and test data.