[Application value of anti-carbamylated protein antibody in the diagnosis of rheumatoid arthritis].

OBJECTIVE: To investigate the expression level and application value of anti-carbamylated protein (CarP) antibody in rheumatoid arthritis (RA). METHODS: Demographic data and laboratory test results of RA patients, non-RA patients and healthy controls in the physical examination center were reviewed from December 2018 to June 2019 in the Rheumatology and Immunology Department of the People' s Hospital of Xinjiang Uygur Autonomous Region. The serum concentrations of anti-CarP antibodies in all the subjects were measured by ELISA and statistically analyzed. RESULTS: A total of 259 subjects were included in this study, including 158 in the RA group (45 serum-negative RA patients), 59 in the non-RA group and 42 in the healthy control group. The concentration of anti-CarP antibody in RA group [8.31 (5.22, 15.26) U/mL] was higher than that in non-RA group [4.50 (3.35, 5.89) U/mL] and healthy control group [3.46 (2.76, 4.92) U/mL]. The concentration of anti-CarP antibody in non-RA group was not significantly different from that in healthy control group (P=0.10). Receiver operating characteristic (ROC) curve analysis showed that the sensitivity of anti-CarP antibody in the diagnosis of RA was 58.2%, and the specificity was 93.1%. The sensitivity of the combined detection of anti-CarP antibody, anti-cyclic peptide containing citrulline (CCP) antibody and rheumatoid factor (RF) was 82.3%, and the specificity was 96.5%. The positive rate of anti-CarP antibody in serum-negative RA patients was 44.4% (20/45). Univariate Logisitic regression analysis showed that age, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), RF, glucose-6-phosphate isomerase (GPI), anti-CCP antibody and anti-CarP antibody were risk factors for RA. Multivariate Logisitic regression analysis showed that anti-CCP antibody and anti-CarP antibody were independent risk factors for RA. Spearman correlation analysis showed that there was no significant correlation between anti-CarP antibody and swollen joint count (SJC), tenderness joints count (TJC), ESR, disease activity score for 28 joints (DAS28), clinical disease activity index (CDAI), simplified disease activity index (SDAI). The concentration of anti-CarP antibody in RA with bone erosion (n=88) was higher than that in RA without bone erosion (n=70), and there was significant difference between the two groups (P < 0.05). CONCLUSIONS: Anti-CarP antibody is an effective serological marker for the diagnosis of RA. The combined detection of RF, anti-CCP antibody and anti-CarP antibody can improve its diagnostic value, and anti-CarP antibody may be an effective assistant diagnostic tool for serum negative RA. The high serum concentration of anti-CarP antibody in patients with RA may indicate an increased risk of bone erosion and should be treated early, but further cohort studies are needed for follow-up observation.

Correlations of baseline neutrophil-lymphocyte ratio with prognosis of patients with lupus nephritis: A single-center experience.

Objective: We aimed to evaluate the correlations among the neutrophil-to-lymphocyte ratio (NLR), lupus nephritis (LN) clinical characteristics, and renal prognosis of patients with LN. Methods: We enrolled 122 patients who were diagnosed with LN at the Rheumatology Department of the People's Hospital, Xinjiang Uygur Autonomous Region from January 2013 to April 2022. We determined the occurrence of renal adverse events in patients with LN by reviewing medical records and follow-up data. Correlations were analyzed using the Spearman test, and the quartile method was applied to classify all of the 122 patients who had completed follow-up into low, medium, and high NLR groups. The Kaplan-Meier survival curve was used to conduct survival analysis, and Cox regression analyses were used to explore possible potential risk factors. Results: The baseline NLR of patients with LN was positively correlated with C-reactive protein (CRP), serum creatinine, blood urea nitrogen, and systemic lupus erythematosus disease activity index scores (P < 0.05) and negatively correlated with estimated glomerular filtration rate (eGFR) and serum albumin (P < 0.05). Patients who completed follow-up were divided into three NLR groups based on their NLR values: 30 in the low (NLR ≤ 2.21), 62 in the medium (NLR > 2.21 and NLR ≤ 6.17), and 30 in the high NLR group (NLR > 6.17). The patient survival time before developing poor renal prognosis was significantly different among the three groups (P < 0.05). High NLR (hazard ratio [HR] = 3.453, 95% confidence interval [CI]: 1.260-9.464), CRP (HR = 1.009, 95% CI: 1.002-1.017), eGFR (HR = 0.979, 95% CI: 0.963-0.995), and 24-h proteinuria values (HR = 1.237, 95% CI: 1.025-1.491) as well as anti-double stranded DNA antibody positivity (HR = 3.056, 95% CI:1.069-8.736) were independent risk factors associated with a poor renal prognosis for patients with LN. Conclusion: The baseline NLR in peripheral blood can be used as a reference index for evaluating renal function and disease activity in patients with LN, and a high NLR has predictive value for the prognosis of patients with LN.

Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms.

Introduce: Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in severe cases, they can cause lifelong disability and even death. Studies have shown that early diagnosis and treatment are key to improving patient outcomes. Therefore, a rapid and accurate method for rapid diagnosis of diseases has been established, which is of great clinical significance for realizing early diagnosis of diseases and improving patient prognosis. Methods: This study was based on Fourier transform infrared spectroscopy (FTIR) combined with a deep learning model to achieve non-invasive, rapid, and accurate differentiation of AS, RA, OA, and healthy control group. In the experiment, 320 serum samples were collected, 80 in each group. AlexNet, ResNet, MSCNN, and MSResNet diagnostic models were established by using a machine learning algorithm. Result: The range of spectral wave number measured by four sets of Fourier transform infrared spectroscopy is 700-4000 cm-1. Serum spectral characteristic peaks were mainly at 1641 cm-1(amide I), 1542 cm-1(amide II), 3280 cm-1(amide A), 1420 cm-1(proline and tryptophan), 1245 cm-1(amide III), 1078 cm-1(carbohydrate region). And 2940 cm-1 (mainly fatty acids and cholesterol). At the same time, AlexNet, ResNet, MSCNN, and MSResNet diagnostic models are established by using machine learning algorithms. The multi-scale MSResNet classification model combined with residual blocks can use convolution modules of different scales to extract different scale features and use resblocks to solve the problem of network degradation, reduce the interference of spectral measurement noise, and enhance the generalization ability of the network model. By comparing the experimental results of the other three models AlexNet, ResNet, and MSCNN, it is found that the MSResNet model has the best diagnostic performance and the accuracy rate is 0.87. Conclusion: The results prove the feasibility of serum Fourier transform infrared spectroscopy combined with a deep learning algorithm to distinguish AS, RA, OA, and healthy control group, which can be used as an effective auxiliary diagnostic method for these rheumatic immune diseases.