Genetic and clinical prediction models for the efficacy and hepatotoxicity of methotrexate in patients with rheumatoid arthritis: a multicenter cohort study

Silibinin's Effects against Methotrexate-Induced Hepatotoxicity in Adjuvant-Induced Arthritis Rat Model

Methotrexate (MTX) is the first drug of choice to treat several diseases, including rheumatoid arthritis. However, its administration is accompanied by severe side effects, most commonly hepatotoxicity. Hence, alternative therapies with a lower toxicity and fewer side effects are needed. This study aimed to investigate the antioxidant and hepatoprotective effects of silibinin (SIL, natural agent) against MTX-induced hepatotoxicity in an adjuvant-induced arthritis (AIA) rat model. Arthritic rats were treated with SIL (100 mg/kg) and/or methotrexate (2 mg/kg). Non-arthritic rats, arthritic untreated rats, and arthritic rats who received the vehicle were followed in parallel. SIL alleviated the systemic consequences of arthritis by restoring lost weight, decreasing the erythrocyte sedimentation rate, and ameliorating joint damage, which was evident both micro- and macroscopically. Additionally, SIL prevented the histopathological alterations in the liver and significantly reduced the liver damage caused by MTX and AIA, as shown by a decrease in the markers of liver damage (ALT and AST). Furthermore, SIL relieved the oxidative stress induced by AIA and MTX in liver tissue by decreasing the lipid peroxidation (MDA) levels and enhancing the antioxidant defense system (GSH levels; catalase and superoxide dismutase (SOD) activities). In conclusion, our results suggest that SIL is a potent antioxidant and hepatoprotective agent in arthritic rats. It markedly attenuated the progression and severity of the arthritic disease and eased the oxidative stress in liver tissue by improving the pro-oxidant/antioxidant balance.

Clinical prediction models of rheumatoid arthritis and its complications: focus on cardiovascular disease and interstitial lung disease

Rheumatoid arthritis (RA) is a chronic, systemic, autoimmune disease of unknown etiology with erosive, symmetric polyarthritis as the main clinical manifestations. Its basic pathological changes are the formation of synovitis, and patients gradually develop destruction of articular cartilage destruction and bone erosion, which eventually leads to joint deformity, disability, and various extra-articular manifestations. Clinical prediction models (CPMs), also known as risk prediction models or risk scores, are mathematical formulas used to estimate the probability that a given individual will have a disease or an outcome in the future. The models are mainly divided into two categories: diagnostic models and prognostic models, which can be used to provide information on disease diagnosis or prognosis to help make better medical decisions. Currently, there is no cure for RA, but effective early diagnosis and treatment are crucial for limiting the severity of the disease and preventing the occurrence and development of complications. This paper reviews the CPMs associated with RA and its related complications, including cardiovascular disease (CVD) and interstitial lung disease (ILD), in order to provide reference and evidence for the early diagnosis and treatment of these diseases and personalized medicine for patients. In addition, the possible pathogenesis and risk factors of these comorbidities are summarized, and possible directions for future related research are prospected.

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction

Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.

Systematic investigation on the anti-rheumatoid arthritis material basis and mechanism of Juan Bi Tang. Part 1: Integrating metabolic profiles and network pharmacology

Previously, our cooperative team confirmed the chemical composition and anti-rheumatoid arthritis (RA) efficacy of Juanbi-Tang (JBT), a clinically and historically used traditional Chinese medicine formula, in two model animals. In this study, we developed an in vivo-in silico strategy to elucidate the anti-RA material basis and mechanism of JBT. With the aid of high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF), the metabolic profiles were investigated in normal and collagen-induced arthritis RA rats following oral administration of JBT. Based on the absorbed constituents in RA rats, network pharmacology was employed to predict the anti-RA mechanisms, followed by molecular docking validation. Consequently, there were 18 absorbed compounds with 6 chemical structures, which were absolutely identified by matching with standard compounds in plasma, and 17 generated metabolites involved of 7 biotransformation pathways, including glucuronidation, sulfation, hydroxylation, deglycosylation, methylation, taurine, and glycine conjugation. Moreover, RA disease affected the absorption and metabolism of the constituents in JBT, given the undetected 2 absorbed compounds and 4 metabolites in RA rats. The analysis of network pharmacology indicated that those absorbed compounds in JBT may fight against RA through the MAPK, FoxO, and Rap1 pathways. Molecular docking also validated these results. Overall, this is the first study to describe the metabolic profiles of JBT-treated healthy and RA rats, and it provides a possible anti-RA mechanism through multiple absorbed compounds and targets by network pharmacology.

Effectiveness and safety of methotrexate versus leflunomide in 12-month treatment for Takayasu arteritis

Aims:

The study investigates the effectiveness and safety of methotrexate (MTX) versus leflunomide (LEF) in 12-month treatment of Takayasu arteritis (TAK).

Methods:

This was a cohort study. Patients diagnosed with TAK between 1 January 2013 and 1 January 2019 were enrolled from First Hospital of China Medical University and Zhongshan Hospital of Fudan University. Patients had active disease and were treated with glucocorticoid combined with LEF or MTX. Treatment response, imaging assessment and side-effects were evaluated during 12-month follow-up.

Results:

In total, 68 patients were enrolled (40 cases treated with LEF and 28 treated with MTX). At baseline, age, sex, disease duration and disease activity index showed no significant differences between groups. Prevalence of complete remission (CR) at 6 months was significantly higher in the LEF group than that in the MTX group (LEF versus MTX: 72.50% versus 53.57%, p = 0.04), though the CR prevalence at 9 months and 12 months showed no significant differences between groups. At 9 months, the prevalence of treatment resistance was much lower in the LEF group compared with MTX group (5.41% versus 11.54%, p = 0.03). Furthermore, prevalence of disease relapse in the LEF group was lower than that in MTX group at 12 months (7.24% versus 16.67%, p = 0.03). Patients with high baseline C-reactive protein levels (⩾15 mg/L) carried a higher risk of treatment resistance (OR = 1.36, 95% CI 1.07–13.41, p = 0.06) and disease relapse (HR = 2.51, 95% CI 1.36–12.98, p = 0.04).

Conclusion:

LEF might provide a quicker treatment response with lower prevalence of disease relapse compared with that elicited in MTX during 12 months follow-up for TAK.