BackgroundCalcitriol (1 alpha, 25-dihydroxy vitamin D3) is a good vitamin D supplement but can cause hypercalcemia. Whereas, 22-oxa-1 alpha, 25-dihydroxy vitamin D3 (22-oxa-calcitriol) has less hypercalcemic activity than calcitriol and is reported to be effective for cell-proliferative diseases. The objective of the study was to compare renal function and blood tests of arthritis patients receiving calcitriol supplements with those receiving 22-oxa-calcitriol supplements.Material/MethodsA total of 369 patients with clinically confirmed rheumatoid arthritis were included in this phase II trial. Patients received lactose powder (the placebo group, n=123), 50 000 IU/week of 22-oxa-calcitriol (the treatment group, n=123), or 50 000 IU/week of calcitriol (the control group, n=123) for 6 weeks. At the time of enrollment and after 6 weeks of supplementation, renal function tests, blood tests, and secondary outcome measures were evaluated. One-way ANOVA and the chi-squared test for independence were performed for continuous data and constant data at a 95% of confidence level.ResultsBoth 22-oxa-calcitriol and calcitriol successfully decreased swollen joints in patients with rheumatoid arthritis, and both improved Health Assessment Questionnaire Disease Activity Index scores and serum vitamin D levels. The intensity of improvement of serum vitamin D levels in both groups was the same (P<0.0001, q=0.24); however, calcitriol caused hypercalcemia (P<0.0001, q=12.59).ConclusionsThis study found that 22-oxa-calcitriol was a good option for vitamin D supplementation in rheumatoid arthritis patients.
Background and aim: Interferon-γ (IFN-γ) is a versatile cytokine which broadly involves in the inflammatory diseases, mediating both immune activation and tolerance. Here, we aimed to investigate the role of IFN-γ in the initiation of adjuvant-induced arthritis (AIA). Methods and Results:In an AIA mice model, increasing IFN-γ mRNA was observed at day 3 and peaked on day 7. At day 3, the majority of IFN-γ-producing cells were located around vessels observed by immunofluorescent staining. Recombinant IFN-γ or anti-IFN-γ antibody was injected into the AIA paw on day 2 to study the outcome of AIA. The recipients of IFN-γ showed increased synovial inflammation, whereas anti-IFN-γ antibody injection repressed the expansion of inflammatory cells. As the percentages of blood monocytes were approximately equivalent, we hypothesized that IFN-γ might impact the access of innate leucocytes from blood to expand local inflammation at this stage. Analysis of tissue CD31 and vascular cell adhesion molecule-1 (VCAM-1) expressions suggested a positive effect of these factors in the development of inflammation, and IFN-γ affected the VCAM-1 expression. To further verify this idea, mice regionally injected with IFN-γ were systematically administrated with anti-VCAM-1 antibody during AIA induction. The IFN-γ expression was inhibited, and the development of AIA was partly abolished in these mice regardless of regional IFN-γ injection. Conclusion: These data suggested that IFN-γ might be critical for the expansion of AIA at early stage through helping inflammatory cell access.
Objective. The differential diagnosis between Adult-onset Still's disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model. Methods. All data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1·0·2) in Python(version 3·9·7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python. Results. The results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0·9916, ACC: 0·9457, Sens: 0·9556, Spec: 0·9578) and the external validation also achieved satisfactory results (AUC: 0·9800, ACC: 0·9333, Sens: 0·8000, Spec: 1·000). We named this discrimination method AIADSS (AI-assisted discrimination of Still's disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/. Conclusion. We created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.
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