BackgroundOxidative stress is one of the important factors involved in the pathogenesis of idiopathic pulmonary fibrosis (IPF). The equilibrium of Nuclear factor-erythroid-related factor 2 (Nrf2)/[BTB (broad-complex, tramtrack and bric-a-brac) and CNC (cap‘n’collar protein) homology 1, Bach1] determines the expression level of antioxidant factors, further regulating the function of oxidation/antioxidation capacity. Pirfenidone (PFD) is one of two currently for IPF therapy approved drugs. PFD regulates intracellular antioxidants, inhibits secretion of inflammatory cytokines and collagen synthesis. However the mechanisms of its antioxidant effects remain elusive.MethodsEffects of PFD treatment were studied in mouse lung fibroblasts (MLF) following induction by transforming-growth factor beta 1 (TGF-β1) and in mice following bleomycin-induced lung fibrosis. The mRNA and protein levels of oxidative stress-related factors Nrf2/Bach1 and their downstream antioxidant factors heme oxygenase-1 (Ho-1) and glutathione peroxidase 1 (Gpx1) were determined by RT-PCR and Western blot. Fibrosis-related cytokines interleukin-6 (IL-6) and myofibroblast markers type 1 collagen α1 (COL1A1) levels in supernate of MLF, serum, and bronchoalveolar lavage fluid (BALF) as well as malondialdehyde (MDA) in serum and BALF were detected by ELISA, reactive oxygen species (ROS) generation was measured by 2′,7′- dichlorofluorescin diacetate (DCFH-DA) assay and lung pathological/morphological alterations in mice were observed by HE and Masson to assess the antioxidant mechanism and therapeutic effects on pulmonary fibrosis induced by bleomycin.ResultsPFD inhibited Bach1 mRNA and protein expressions in mouse lung fibroblasts induced by TGF-β1 and lung tissues with pulmonary fibrosis induced by bleomycin. Furthermore, it improved Nrf2, Ho-1 and Gpx1 mRNA and protein expressions. After PFD treatment, COL1A1and IL-6 levels in supernate of MLF, serum, and BALF as well as ROS in lung tissues and MDA in serum and BALF from a mouse with pulmonary fibrosis were significantly decreased, and the infiltration of lung inflammatory cells and fibrosis degree were alleviated.ConclusionsTheraputic effects of PFD for IPF were involved in Nrf2/Bach1 equilibrium which regulated the capacity of oxidative stress. The study provided new insights into the antioxidant mechanism of PFD.Electronic supplementary materialThe online version of this article (doi:10.1186/s12890-017-0405-7) contains supplementary material, which is available to authorized users.

BackgroundCombination therapy with DMARDs for treating RA is considered as standard of care. However, certain rates of adverse events (AEs) are unavoidable. The stigma is which drug should be stopped first once AEs emerge. The decisions made by clinicians are always empirically.ObjectivesTo develop an algorithm for decision making on drug withdraw sequence in face of adverse events with combination therapy based on data mining and machine learning from the SSDM.MethodsSSDM is an interactive mobile disease management tool, including the doctors’ and patients’ application system (App). The patients can input medical records and perform self-evaluation via App. The data synchronises to the mobiles of authorised rheumatologists through cloud and advices could be delivered. In order to develop the master algorithm, abnormal white blood cell (WBC) counts in blood were first targeted. WBC and medication data was collected, extracted, validated, and then based on Bayesian networks, data mining, modelling, calculating, analysing were performed. WBC under 4,000/ml is defined as leukocytopenia (LP), and over 10,000/ml as infection predisposing (IP).ResultsFrom Jun 2014 to Jan 2018, 24,731 RA patients from 486 centres registered in SSDM. 6099 are male and 18 632 are female with mean age of 49.28±16.08 (18 to 99) years. 19 different drugs and 126 types of combination therapies are identified. Lab test results showed LP happened in 87 and IP in 123 treatment regiments. Among them we selected prednisone (Pred), leflunomide (LEF), methotrexate (MTX) and hydroxycholoqine (HCQ) as an example to develop a master algorithm based on Bayesian networks and learning model. Figure 1 shows Bayesian network and data processing, in which, quartet are correlating with 15 different regiments. Based on Bayesian method and network data, the calculation for LP and IP probabilities is generated through 32 modelling, and the algorithm for drug withdraw strategies are generated. Drug withdraw sequence for LP is HCQ, then LEF and then Pre, and the risks of LP are reduced by 64%, 52% and 26%, respectively. For IP, withdraw sequence is Pred, then MTX and then LEF, and the risks of IP are reduced by 57%, 63%, and 14%, respectively.Abstract AB0322 – Figure 1Bayesian network and data processing: patients’ number (black bold as blow showed) and the rate of either LP or IP in 15 regiments.Pred: prednisone; HCQ: hydroxycholoqine; MTX: methotrexate; LEF: leflunomide; LP: leukocytopenia; IP: infection predisposingConclusionsBig data system can be built using SSDM via empowering patient. Through data mining, networking, modelling, and Bayesian calculation, a master algorithm for drug withdraw strategy in reduction of adverse events with combination therapy is developed, which can be applied on the other AEs in SSDM and may replicated in other diseases. Following the continuing data inputs and machine leaning, an artificial intelligent system in assisting clinical decision making may be achieved.Limitations: This study only focus on rate of AE without considerin...

BackgroundHepatic, hematologic and other adverse events (AE) during treatment in RA patients are unavoidable. And monitoring AE in long-term treatment is quite necessary as a part of chronic disease management. SSDM is a smart mobile tool to help patients upload their therapeutic regimens, lab test records and report AEs. Our previous study showed that patients in China can master the application of SSDM after training.ObjectivesTo determine and compare the incidence of adverse events during treatment of RA with different therapeutic regimens, focusing on mono and combination therapy.MethodsThe SSDM includes interfaces of both physicians’ and patients’ application. Patients were educated to enter the data of lab test records and treatment regiments once a month, all data can be synchronised automatically to the authorised physicians’ mobile tool, which formed a large patient reported database. The rheumatologists can also adjust treatment regiments base on patients’ profile.ResultsFrom Aug 2014 to Jan 2018, a total of 7048 RA patients from 480 centres in China were entered in the cohort study. These patients contributed more than 12 600 patient-years (PY) of total followup. The mean age was 48.98±16.08 (18 to 99) years and the median disease duration was 23.27 months. The treatment regimens include mono or combination of leflunomide(LEF), MTX, hydroxycholoqine (HCQ), sulfasalazine(SSZ), glucocorticoid(GC), biologic DMARD, Tripterygium wilfordii, meloxicam, celecoxib, iguratimod, etc. In this database the five most common treatment regimens is LEF monotherapy (3801 PY), MTX monotherapy (1321 PY), LEF +MTX (1086 PY), HCQ monotherapy (715 PY), LEF +HCQ (576 PY). The incidence rate of hepatic events was lower for LEF monotherapy (35 events/1000 PY) than MTX (52 events/1000 PY) and LEF +MTX combination therapy (115 events/1000 PY) (p<0.01). The incidence rate of leukopenia was lower for LEF monotherapy (42 events/1000 PY) and MTX monotherapy (39 events/1000 PY) than LEF +MTX combination therapy (84 events/1000 PY) (p<0.01).ConclusionsThe findings show that mono or combination of csDMARDs are the most commonly used drugs in Chinese RA patients. And AEs may be well described in this patient report database because of the large sample sizes and empowering patient themselves. RA patients can get better safety in the long-term treatment via SSDM.Disclosure of InterestNone declared