Abstract. The present study assessed the existence of mast cell chymase in hypertrophic scars and determined whether chymase promotes fibrosis via the transforming growth factor (TGF)-β1/Smads signaling pathway. Five patients with hypertrophic scars and another five patients subjected to repair and reconstruction of other tissue defects were included in the present study. To detect the existence of mast cells and mast cell chymase in hypertrophic scars, immunohistochemistry was employed. To test the effect of chymase on TGF-β1, angiotensin, and type I and III collagen mRNA expression in isolated hypertrophic scar fibroblasts in vitro, reverse-transcription quantitative PCR was performed. To investigate how chymase affects TGF-β1, phosphorylated (P)-Smad2/3 as well as Smad4 and Smad7 protein expression, western blot analysis was used. Mast cell chymase was identified to promote the mRNA expression of TGF-β1, angiotensin, and type I and III collagen in hypertrophic scar fibroblasts in a time-and dose-dependent manner. Furthermore, treatment with 60 ng/ml mast cell chymase for 12 h led to the upregulation of TGF-β1, P-Smad2/3, Smad4 and Smad7 in hypertrophic scar fibroblasts. The present study demonstrated that mast cells and chymase are present in hypertrophic scars, and chymase promotes hypertrophic scar fibroblast proliferation and collagen synthesis by activating the TGF-β1/Smads signaling pathway.

At present, with the rapid growth of manufacturing and big data, reliability technology has gradually become a topical issue in the industrial field. Aiming at the operation reliability assessment of rolling bearings, this paper proposes a bearings operational reliability assessment using an ensemble deep autoencoder based on asymmetric structure. In this method, an ensemble deep autoencoder is used to adaptively learn degradation features from condition monitoring data, where the ensemble deep autoencoder adopts an asymmetric structure with different activation functions in the encoder and decoder. Then, the learned features are classified by correlation analysis, and the typical features in each category are selected. Finally, the operation reliability of rolling bearings is evaluated through the definition of reliability based on Mahalanobis distance. Through the example evaluation of rolling bearing operation reliability and comparison with other feature extraction methods, it can be concluded that this method has stronger feature extraction ability and can effectively show the trend of bearing degradation.

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