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BackgroundAcute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are severe inflammatory lung diseases. Methylprednisolone (MP) is a common drug against inflammation in clinic. In this study, we aim to investigate the protective effect of MP on ALI and potential mechanisms.MethodsMale BABL/c mice were injected through tail vein using lipopolysaccharide (LPS, 5 mg/kg) with or without 5 mg/kg MP. Lung mechanics, tissue injury and inflammation were examined. Macrophage subsets in the lung were identified by flow cytometry. Macrophages were cultured from bone marrow of mice with or without MP. Then, we analyzed and isolated the subsets of macrophages. These isolated macrophages were then co-cultured with CD4+ T cells, and the percentage of regulatory T cells (Tregs) was examined. The expression of IL-10 and TGF-β in the supernatant was measured. The Tregs immunosuppression function was examined by T cell proliferation assay. To disclose the mechanism of the induction of Tregs by M2c, we blocked IL-10 or/and TGF-β using neutralizing antibody.ResultsRespiratory physiologic function was significantly improved by MP treatment. Tissue injury and inflammation were ameliorated in the MP-treated group. After MP treatment, the number of M1 decreased and M2 increased in the lung. In in vitro experiment, MP promoted M2 polarization rather than M1. We then induced M1, M2a and M2c from bone marrow cells. M1 induced more Th17 while M2 induced more CD4+CD25+Fxop3+ Tregs. Compared with M2a, M2c induced more Tregs, and this effect could be blocked by anti-IL-10 and anti-TGF-β antibodies. However, M2a and M2c have no impact on Tregs immunosuppression function.ConclusionIn conclusion, MP ameliorated ALI by promoting M2 polarization. M2, especially M2c, induced Tregs without any influence on Tregs immunosuppression function.

It is increasingly important to accurately and comprehensively estimate the effects of particular clinical treatments. Although randomization is the current gold standard, randomized controlled trials (RCTs) are often limited in practice due to ethical and cost issues. Observational studies have also attracted a great deal of attention as, quite often, large historical datasets are available for these kinds of studies. However, observational studies also have their drawbacks, mainly including the systematic differences in baseline covariates, which relate to outcomes between treatment and control groups that can potentially bias results.Propensity score methods, which are a series of balancing methods in these studies, have become increasingly popular by virtue of the two major advantages of dimension reduction and design separation. Within this approach, propensity score matching (PSM) has been empirically proven, with outstanding performances across observational datasets. While PSM tutorials are available in the literature, there is still room for improvement. Some PSM tutorials provide step-by-step guidance, but only one or two packages have been covered, thereby limiting their scope and practicality. Several articles and books have expounded upon propensity scores in detail, exploring statistical principles and theories; however, the lack of explanations on function usage in programming language has made it difficult for researchers to understand and follow these materials. To this end, this tutorial was developed with a six-step PSM framework, in which we summarize the recent updates and provide step-by-step guidance to the R programming language. This tutorial offers researchers with a broad survey of PSM, ranging from data preprocessing to estimations of propensity scores, and from matching to analyses. We also explain generalized propensity scoring for multiple or continuous treatments, as well as time-dependent PSM. Lastly, we discuss the advantages and disadvantages of propensity score methods.