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.

Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome.

The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value.

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.

Raman spectroscopy combined with machine learning algorithms for rapid detection Primary Sjögren's syndrome associated with interstitial lung disease.

BACKGROUND: Interstitial lung disease (ILD) is a major complication of Primary Sjögren's syndrome (pSS) patients.It is one of the main factors leading to death. The aim of this study is to evaluate the value of serum Raman spectroscopy combined with machine learning algorithms in the discriminatory diagnosis of patients with Primary Sjögren's syndrome associated with interstitial lung disease (pSS-ILD). METHODS: Raman spectroscopy was performed on the serum of 30 patients with pSS, 28 patients with pSS-ILD and 30 healthy controls (HC). First, the data were pre-processed using baseline correction, smoothing, outlier removal and normalization operations. Then principal component analysis (PCA) is used to reduce the dimension of data. Finally, support vector machine(SVM), k nearest neighbor (KNN) and random forest (RF) models are established for classification. RESULTS: In this study, SVM, KNN and RF were used as classification models, where SVM chooses polynomial kernel function (poly). The average accuracy, sensitivity, and precision of the three models were obtained after dimensionality reduction. The Accuracy of SVM (poly) was 5.71% higher than KNN and 6.67% higher than RF; Sensitivity was 5.79% higher than KNN and 8.56% higher than RF; Precision was 6.19% higher than KNN and 7.45% higher than RF. It can be seen that the SVM (poly) had better discriminative effect. In summary, SVM (poly) had a fine classification effect, and the average accuracy, sensitivity and precision of this model reached 89.52%, 91.27% and 89.52%, respectively, with an AUC value of 0.921. CONCLUSIONS: This study demonstrates that serum RS combined with machine learning algorithms is a valuable tool for diagnosing patients with pSS-ILD. It has promising applications.

Facile and versatile access to substituted hexabenzoovalene derivatives: characterization and optoelectronic properties.

We describe the design and modular synthesis of a library of substituted hexabenzoovalene derivatives (SHBO), along with the key precursor dinaphthopyrenes (3), highlighting the influence of a wide array of substituent variation on the photophysical properties via UV-vis absorption, fluorescence spectra and electrochemical methods. The results show that the cyclized hexabenzoovalenes present a stronger spectroscopic red-shift than the corresponding dinaphthopyrenes. X-ray diffraction analysis suggests that intermediate 3hx containing two nitro groups forms a trans-configuration with twisted structures. Our systematic investigation might provide a realistic design strategy to construct large one-dimensional and two-dimensional materials via bottom-up approaches.

Investigation of C1-complex regions reveals new C1Q variants associated with protection from systemic lupus erythematosus, and affect its transcript abundance.

Although rare variant C1Q deficiency was identified as causative risk for systemic lupus erythematosus (SLE), there are limited and inconsistent reports regarding the common polymorphisms of C1Q genes in SLE susceptibility. Furthermore, there are no reports concerning polymorphisms of C1S, C1R, and C1RL and whether they confer susceptibility to SLE. We therefore evaluated 22 SNPs across six C1-complex genes in two independent case-control cohorts, and identified four novel SNPs that confer protection from SLE. The four SNPs are all located in C1Q. Particularly, the variant rs653286 displayed an independent reduced risk on SLE susceptibility (OR 0.75, P = 2.16 × 10-3) and anti-dsDNA antibodies (OR 0.68, P = 0.024). By bioinformatics analysis, SNPs rs653286 and rs291985 displayed striking cis-eQTL effects on C1Q genes expression. Individuals homozygous for the 'protective' allele at four SNPs had significantly higher levels of serum C1q (rs680123-rs682658: P = 0.0022; rs653286-rs291985: P = 0.0076). To our knowledge, this is the first study to demonstrate that only C1Q polymorphisms are associated with SLE. The C1Q SNP rs653286 confers an independent protective effect on SLE susceptibility and affects transcript abundance.

Impact of the leucocyte immunoglobulin-like receptor A3 (LILRA3) on susceptibility and subphenotypes of systemic lupus erythematosus and Sjögren's syndrome.

BACKGROUND: Recently, our research group identified the non-deleted (functional) leucocyte immunoglobulin-like receptor A3 (LILRA3) as a new genetic risk for rheumatoid arthritis. OBJECTIVES: To further investigate whether the functional LILRA3 is a new susceptibility factor for other autoimmune diseases-for example, systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS). METHODS: The LILRA3 deletion polymorphism and its tagging single nucleotide polymorphism rs103294 were genotyped for 1099 patients with SLE, 403 patients with pSS and 2169 healthy controls. Association analyses were performed in whole dataset or clinical/serological subsets. The impact of LILRA3 on SLE activity and LILRA3 expression was evaluated. RESULTS: The functional LILRA3 conferred high susceptibility to both SLE (p=3.51×10(-7), OR=2.03) and pSS (p=1.40×10(-3), OR=2.32). It was associated with almost all the clinical/serological features in SLE, especially with leucopenia (p=4.09×10(-7), OR=2.19) and thrombocytopenia (p=1.68×10(-5), OR=1.70). In pSS, functional LILRA3 was specifically associated with leucopenia (p=4.39×10(-4), OR=3.25), anti-Ro/SSA-positive subphenotypes (p=4.54×10(-3), OR=2.34) and anti-La/SSB-positive subphenotypes (p=0.012, OR=2.49). Functional LILRA3 conferred higher disease activity in patients with SLE (p=0.044) and higher LILRA3 expression in both SLE (p=5.57×10(-8)) and pSS (p=1.49×10(-7)) than in controls. CONCLUSIONS: Functional LILRA3 is a new susceptibility factor for SLE and pSS. It highly predisposes to certain phenotypes such as leucopenia and thrombocytopenia in SLE, and may confer increased disease activity in SLE and a higher risk of leucopenia and autoantibody-positive subphenotypes in pSS.

[Applications of multi-micro-volume pressure-assisted derivatization reaction device for analysis of polar heterocyclic aromatic amines by gas chromatography-mass spectrometry].

A multi-micro-volume pressure-assisted derivatization reaction device has been designed and made for the silylation derivatization of polar heterocyclic aromatic amines by N-(tert-butyldimethylsilyl )-N-methyl-trifluoroacetamide (MTBSTFA) with 1% catalyst tert-butyldimethylchlorosilane (TBDMCS) at a high temperature. The tert-butyldimethylsilyl derivatives then could be automatically analyzed by gas chromatography-mass spectrometry. Using the pressure-assisted device, the silylation reaction may occur at a temperature higher than the boiling points of the reagents, and several micro-volume samples can be simultaneously pretreated in the same device to shorten the sample-preparation time and to improve the repeatability. The derivatization conditions including the headspace volume of the vial, the evaporative surface area of the reagent, derivatization temperature and time have been discussed for the use of the pressure-assisted device. The experimental results proved that the device is an effective way for the simultaneous derivatization of several micro-volume samples at a high temperature. Compared with a common device, the derivative amounts were obviously increased when using the pressure-assisted device at 90 degrees C. Quantitative derivatization can be achieved even at 150 degrees C while there was no common device could be applied at such a high temperature due to the heavy losses of reagents by evaporation. However, no obviously higher reaction speed has been observed in such a circumstance with a higher temperature and a higher pressure using the pressure-assisted device.