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The current study aims to investigate the effects of matrine on the JAK-STAT signaling transduction pathways in bleomycin (BLM)-induced pulmonary fibrosis (PF) and to explore its action mechanism. A total of 72 male C57BL/6 mice were randomized into the control, model, and treatment groups. PF models were established by instilling BLM intratracheally.The treatment group was given daily matrine through gastric lavage. Six mice were sacrificed in each group at 3, 7, 14, and 28 days. The lung tissues were observed using hematoxylin-eosin staining. The expression of JAK, STAT1, and STAT3 was observed using immunohistochemistry and then determined using real-time polymerase chain reaction. Alveolitis and PF significantly improved in the treatment group compared with the model group (P < 0.05). The expression of JAK, STAT1, and STAT3 in the model group increased at day 7, peaked at day 14 and then decreased, but the expression was still higher than that in the control group at day 28 (P < 0.05). The three indices in the treatment group were significantly lower than those in the model group at any detection time point (P < 0.05). PF causes high expression of JAK, STAT1, and STAT3.Matrine exerts an anti-PF effect by inhibiting the JAK-STAT signaling transduction pathways.

The research on incomplete soft sets is an integral part of the research on soft sets and has been initiated recently. However, the existing approach for dealing with incomplete soft sets is only applicable to decision making and has low forecasting accuracy. In order to solve these problems, in this paper we propose a novel data filling approach for incomplete soft sets. The missing data are filled in terms of the association degree between the parameters when a stronger association exists between the parameters or in terms of the distribution of other available objects when no stronger association exists between the parameters. Data filling converts an incomplete soft set into a complete soft set, which makes the soft set applicable not only to decision making but also to other areas. The comparison results elaborated between the two approaches through UCI benchmark datasets illustrate that our approach outperforms the existing one with respect to the forecasting accuracy.