Highly active and regioselective hydroaminomethylation of olefins catalyzed by Rh/sulfoxantphos with ZSM-5.

Rh-catalyzed hydroaminomethylation has been developed with acid sulfoxantphos and ZSM-5. Linear amines were obtained in good yields (71-95%) with high l/b ratios (up to 132.4) and excellent TON values (up to 23 760). The ZSM-5 and SO3H group of ligands improved the performances of hydroformylation and reductive amination.

Hot air-assisted radio frequency drying of corn kernels: the effect on structure and functionality properties of corn starch.

Hot air (HA) drying caused quality damage of grains with long treatment time. Radio frequency (RF) heating as an emerging technology was applied to improve drying quality of cereals effectively. The effects of HA-RF drying (50 °C, 70 °C, 90 °C) of corn kernels on the morphology, structure, and physicochemical properties of starch were investigated and compared with HA drying. The surface of treated starch became rough, along with fragments and pores. Drying treatments increased the amylose content from 10.59 % to 23.88 % and the residual protein content of starch from 0.58 % to 1.23 %, and reduced the crystallinity from 31.95 % to 17.15 % and short-range order structures of starch from 0.918 to 0.868. The change of structures in turn resulted in the increase of pasting viscosity, gelatinization temperature, storage modulus and loss modulus. Furthermore, the HA-RF dried starch displayed stronger thermal stability, higher gelatinization degree and better gelation properties than the HA-treated starch at the same temperature. The data proved that the synergistic effects of HA and RF were more effective in modulating the starch structure and improving the functional characteristics of corn starch. This paper would like to provide potential reference for better application of HA-RF technologies to corn.

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism.

FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

Exploring physical function and physical activity in axial spondyloarthritis: Beyond clinical remission or low disease activity.

OBJECTIVES: To investigate the crucial roles of physical function (PF) and physical activity (PA) in axial spondyloarthritis (axSpA) patients, as well as their correlation with disease activity (DA), and to explore the influence of general characteristics among them. METHODS: We enrolled axSpA patients from Xijing Hospital, spanning March 2022 to August 2022. Spearman rank correlation coefficients were used to assess correlations between PA (measured by the Global Physical Activity Questionnaire [GPAQ]), PF (measured by the Assessment of Spondyloarthritis international Society Health Index [ASAS-HI], the Short Form 36-Item Health Survey [SF-36], and the Bath Ankylosing Spondylitis Functional Index [BASFI]), DA, and their influencing factors. A Mann-Whitney U-test and Kruskal-Wallis H-test were used to compare variables between different patients grouped by sex, human leukocyte antigen B27 (HLA-B27), hip involvement, and intensity of PA and DA. RESULTS: Three hundred fifty-five axSpA patients were included. We observed a moderate to strong correlation between DA and PF in axSpA patients. PA was weakly correlated with DA or PF. DA varied significantly at different PA levels, and patients with low PA levels had poorer PF. Active patients had worse PF, less transport-related PA, and a higher rate of hip involvement with a worse Harris Hip Score (HHS). CONCLUSIONS: We identified a close relationship between DA, PF, and PA in axSpA patients. Further, gender, HLA-B27, and hip involvement affected the clinical manifestation of axSpA patients. These findings demonstrate that clinical remission of axSpA patients requires a comprehensive assessment rather than a single remission of DA.

Position-based anchor optimization for point supervised dense nuclei detection.

Nuclei detection is one of the most fundamental and challenging problems in histopathological image analysis, which can localize nuclei to provide effective computer-aided cancer diagnosis, treatment decision, and prognosis. The fully-supervised nuclei detector requires a large number of nuclei annotations on high-resolution digital images, which is time-consuming and needs human annotators with professional knowledge. In recent years, weakly-supervised learning has attracted significant attention in reducing the labeling burden. However, detecting dense nuclei of complex crowded distribution and diverse appearances remains a challenge. To solve this problem, we propose a novel point-supervised dense nuclei detection framework that introduces position-based anchor optimization to complete morphology-based pseudo-label supervision. Specifically, we first generate cellular-level pseudo labels (CPL) for the detection head via a morphology-based mechanism, which can help to build a baseline point-supervised detection network. Then, considering the crowded distribution of the dense nuclei, we propose a mechanism called Position-based Anchor-quality Estimation (PAE), which utilizes the positional deviation between an anchor and its corresponding point label to suppress low-quality detections far from each nucleus. Finally, to better handle the diverse appearances of nuclei, an Adaptive Anchor Selector (AAS) operation is proposed to automatically select positive and negative anchors according to morphological and positional statistical characteristics of nuclei. We conduct comprehensive experiments on two widely used benchmarks, MO and Lizard, using ResNet50 and PVTv2 as backbones. The results demonstrate that the proposed approach has superior capacity compared with other state-of-the-art methods. In particularly, in dense nuclei scenarios, our method can achieve 95.1% performance of the fully-supervised approach. The code is available at https://github.com/NucleiDet/DenseNucleiDet.

HDAC3 improves intestinal function of mice by regulating cGAS-Sting pathway of intestinal glial cells.

It is found that HDAC3 may be a potential therapeutic target for intestinal related diseases. At present, the role and mechanism of HDAC3 in the pathogenesis of severe acute pancreatitis (SAP) have not been reported, which needs to be further explored. The SAP mouse model was established and the expression of HDAC3 was detected by immunohistochemistry. H&E staining showed the intestinal pathological state of SAP mice. The expression of HDAC3 was measured by real-time quantitative PCR (RT qPCR) and Western blot. Apoptosis kit was used to determine cell apoptosis rate. The level of inflammatory factors was detected by ELISA kits. The expressions of HDAC3, cGAS and Sting were significantly increased in SAP patients and SAP mice. Silencing HDAC3 promoted the proliferation and adhesion of intestinal glial cells and inhibited the inflammation and apoptosis of intestinal epithelial cells. In addition, silencing HDAC3 inhibited oxidative stress in intestinal epithelial cells. Furthermore, silencing HDAC3 inhibited the activation of cGAS-Sting pathway in intestinal glial cells. More importantly, silencing HDAC3 alleviates intestinal barrier function in SAP mice. HDAC3 inhibition improves acute pancreatitis in mice by regulating cGAS-Sting pathway of intestinal glial cells.

Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a severe complication of systemic lupus erythematosus (SLE). This study aims to explore the clinical characteristics and prognosis in SLE-PAH based on consensus clustering and risk prediction model. METHODS: A total of 205 PAH (including 163 SLE-PAH and 42 idiopathic PAH) patients were enrolled retrospectively based on medical records at the First Affiliated Hospital of Zhengzhou University from July 2014 to June 2021. Unsupervised consensus clustering was used to identify SLE-PAH subtypes that best represent the data pattern. The Kaplan-Meier survival was analyzed in different subtypes. Besides, the least absolute shrinkage and selection operator combined with Cox proportional hazards regression model were performed to construct the SLE-PAH risk prediction model. RESULTS: Clustering analysis defined two subtypes, cluster 1 (n = 134) and cluster 2 (n = 29). Compared with cluster 1, SLE-PAH patients in cluster 2 had less favorable levels of poor cardiac, kidney, and coagulation function markers, with higher SLE disease activity, less frequency of PAH medications, and lower survival rate within 2 years (86.2% vs. 92.8%) (P < 0.05). The risk prediction model was also constructed, including older age at diagnosis (≥ 38 years), anti-dsDNA antibody, neuropsychiatric lupus, and platelet distribution width (PDW). CONCLUSIONS: Consensus clustering identified two distinct SLE-PAH subtypes which were associated with survival outcomes. Four prognostic factors for death were discovered to construct the SLE-PAH risk prediction model.

Combination immunotherapy of glioblastoma with dendritic cell cancer vaccines, anti-PD-1 and poly I:C.

Glioblastoma (GBM) is a lethal cancer with limited therapeutic options. Dendritic cell (DC)-based cancer vaccines provide a promising approach for GBM treatment. Clinical studies suggest that other immunotherapeutic agents may be combined with DC vaccines to further enhance antitumor activity. Here, we report a GBM case with combination immunotherapy consisting of DC vaccines, anti-programmed death-1 (anti-PD-1) and poly I:C as well as the chemotherapeutic agent cyclophosphamide that was integrated with standard chemoradiation therapy, and the patient remained disease-free for 69 months. The patient received DC vaccines loaded with multiple forms of tumor antigens, including mRNA-tumor associated antigens (TAA), mRNA-neoantigens, and hypochlorous acid (HOCl)-oxidized tumor lysates. Furthermore, mRNA-TAAs were modified with a novel TriVac technology that fuses TAAs with a destabilization domain and inserts TAAs into full-length lysosomal associated membrane protein-1 to enhance major histocompatibility complex (MHC) class I and II antigen presentation. The treatment consisted of 42 DC cancer vaccine infusions, 26 anti-PD-1 antibody nivolumab administrations and 126 poly I:C injections for DC infusions. The patient also received 28 doses of cyclophosphamide for depletion of regulatory T cells. No immunotherapy-related adverse events were observed during the treatment. Robust antitumor CD4+ and CD8+ T-cell responses were detected. The patient remains free of disease progression. This is the first case report on the combination of the above three agents to treat glioblastoma patients. Our results suggest that integrated combination immunotherapy is safe and feasible for long-term treatment in this patient. A large-scale trial to validate these findings is warranted.

Progressive pseudorheumatoid dysplasia involving a novel WISP3 mutation and sacroiliac and hip arthritis: A case report and literature review.

INTRODUCTION: Progressive pseudorheumatoid dysplasia (PPRD) is a rare autosomal recessive genetic disease caused by mutations in the Wnt1-inducible signaling pathway protein 3 gene. PPRD is considered a noninflammatory disease, and involvement of the sacroiliac joint and hip arthritis have not been reported previously. PATIENT CONCERNS: We report a case of PPRD in an 11-year-old boy, who presented with bilateral pain and swelling in the knees, elbows, and ankles, and bilateral pain without swelling in the shoulders, wrists, knuckles, and proximal and distal interphalangeal joints for the past 5 years. He had been misdiagnosed with juvenile idiopathic arthritis for more than 6 years. DIAGNOSIS: The correct PPRD diagnosis was made using whole-exome sequencing for Wnt1-inducible signaling pathway protein 3 gene mutations (c.589 + 2T>C and c.721T>G; both mutations have rarely been reported) and magnetic resonance imaging examination; moreover, the latter showed inflammation of the sacroiliac joint and hip joint. INTERVENTION: The patient was administered supplemental calcium, active vitamin D, and glucosamine sulfate. OUTCOME: The patient experienced alleviation of joint pain following treatment initiation; however, joint motion improvement was not obvious. Above all, the long-term use of biologic or targeted synthetic disease-modifying antirheumatic drugs in the future was avoided. CONCLUSION: The findings of the inflammatory aspects in PPRD will enrich our understanding of this rheumatological disease.

Isoindigo-Based Dual-Acceptor Conjugated Polymers Incorporated Conjugation Length and Intramolecular Charge Transfer for High-Efficient Photothermal Conversion.

Photothermal tumor therapy (PTT) and photoacoustic imaging (PA) have emerged as promising noninvasive diagnostic and therapeutic approaches for cancer treatment. However, the development of efficient PTT agents with high photostability and strong near-infrared (NIR) absorption remains challenging. This study synthesizes three isoindigo-based dual-acceptor conjugated polymers (CPs) (P-IIG-TPD, P-IIG-DPP, and P-IIG-EDOT-BT) via a green and nontoxic direct arylation polymerization (DArP) method and characterizes their optical, electrochemical, and NIR photothermal conversion properties. By incorporating two acceptors into the backbone, the resulting polymers exhibit enhanced photothermal conversion efficiency (PCE) due to improved synergy among conjugation length, planarity, and intramolecular charge transfer (ICT). The nanoparticles (NPs) of P-IIG-EDOT-BT and P-IIG-DPP have a uniform size distribution around 140 nm and exhibit remarkable NIR absorption at 808 nm. In addition, P-IIG-EDOT-BT and P-IIG-DPP NPs exhibit high PCEs of 62% and 78%, respectively. This study promotes the molecular design of CPs as NIR photothermal conversion materials and provides guidance for the development of novel dual-acceptor CPs for tumor diagnosis and treatment.

Selective hydrogenation of dimethyl terephthalate over a potassium-modified Ni/SiO2 catalyst.

Selective hydrogenation of dimethyl terephthalate (DMT) is an ideal way to prepare 1,4-cyclohexane dicarboxylate (DMCD), an important intermediate and monomer. Even though noble metal-based catalysts (e.g., Ru, Pd) have been developed for selective hydrogenation of DMT, the use of non-precious Ni catalysts to achieve high activity and selectivity is still challenging. In this study, we present that only 0.5 wt% of KF by post-impregnated doping can significantly improve the performance of Ni/SiO2 catalysts (83% vs. 96% selectivity; 41% vs. 95% conversion). The selectivity of DMCD can be up to 97%, which is the highest reported over Ni catalysts. The boosting effect of KF modification might be due to higher amounts of Ni(0) species and lower amounts of moderate acidic sites, which are beneficial for selective hydrogenation of phenyl rings over hydrogenolysis of ester groups.

Laboratory risk factors for coexistent primary biliary cholangitis in patients with Sjögren's syndrome: a retrospective study.

BACKGROUND: Limited research exists on the laboratory characteristics of coexistent primary biliary cholangitis (PBC) and Sjögren's syndrome (SS). This study aimed to investigate the laboratory risk factors for the coexistence of PBC in patients with SS. METHODS: Eighty-two patients with coexistent SS and PBC (median age 52.50 years) and 82 age- and sex-matched SS controls were retrospectively enrolled between July 2015 and July 2021. The clinical and laboratory characteristics of the two groups were compared. Laboratory risk factors for the coexistence of PBC in patients with SS were analyzed using logistic regression analysis. RESULTS: Both groups had a similar prevalence of hypertension, diabetes, thyroid disease, and interstitial lung disease. Compared with the SS group, patients in the SS + PBC group had higher levels of liver enzymes, immunoglobulins M (IgM), G2, and G3 (P < 0.05). The percentage of patients with an antinuclear antibody (ANA) titre > 1:10000 in the SS + PBC group was 56.1%, higher than that in the SS group (19.5%, P < 0.05). Additionally, cytoplasmic, centromeric, and nuclear membranous patterns of ANA and positive anti-centromere antibody (ACA) were observed more frequently in the SS + PBC group (P < 0.05). Logistic regression analysis showed that elevated IgM levels, high ANA titre, cytoplasmic pattern, and ACA were independent risk factors for PBC coexistence in SS. CONCLUSIONS: In addition to established risk factors, elevated IgM levels, positive ACA, and high ANA titre with cytoplasmic pattern provide clues to clinicians for the early screening and diagnosis of PBC in patients with SS.

Engineering an Efficient Expression Using Heterologous GAL Promoters and Transcriptional Activators in Saccharomyces cerevisiae.

Galactose-inducible (GAL) promoters have been widely used in metabolic engineering in Saccharomyces cerevisiae for production of valuable products. Endogenous GAL promoters and GAL transcription factors have often been engineered to improve GAL promoter activities. Heterologous GAL promoters and GAL activator (Gal4p-like transcriptional activators), although existing in other yeasts or fungi, have not been well explored. In this study, we comprehensively characterized the activation effects of Gal4p activators from different yeasts or fungi on a variant of GAL promoters. Overexpressing endogenous Gal4p driven by PHHF1 increased the activities of native PGAL1 and heterologous PSkGAL2 by 131.20% and 72.45%, respectively. Furthermore, eight transcriptional activators from different organisms were characterized and most of them exhibited functions that were consistent with ScGal4p. Expression of KlLac9p from Kluyveromyces lactis further increased the activity of PScGAL1 and PSkGAL2 by 41.56% and 100.63%, respectively, compared to ScGal4p expression, and was able to evade Gal80p inhibition. This optimized GAL expression system can be used to increase the production of ß-carotene by 9.02-fold in S. cerevisiae. Our study demonstrated that a combination of heterologous transcriptional activators and GAL promoters provided novel insights into the optimization of the GAL expression system.

Reliable Mutual Distillation for Medical Image Segmentation Under Imperfect Annotations.

Convolutional neural networks (CNNs) have made enormous progress in medical image segmentation. The learning of CNNs is dependent on a large amount of training data with fine annotations. The workload of data labeling can be significantly relieved via collecting imperfect annotations which only match the underlying ground truths coarsely. However, label noises which are systematically introduced by the annotation protocols, severely hinders the learning of CNN-based segmentation models. Hence, we devise a novel collaborative learning framework in which two segmentation models cooperate to combat label noises in coarse annotations. First, the complementary knowledge of two models is explored by making one model clean training data for the other model. Secondly, to further alleviate the negative impact of label noises and make sufficient usage of the training data, the specific reliable knowledge of each model is distilled into the other model with augmentation-based consistency constraints. A reliability-aware sample selection strategy is incorporated for guaranteeing the quality of the distilled knowledge. Moreover, we employ joint data and model augmentations to expand the usage of reliable knowledge. Extensive experiments on two benchmarks showcase the superiority of our proposed method against existing methods under annotations with different noise levels. For example, our approach can improve existing methods by nearly 3% DSC on the lung lesion segmentation dataset LIDC-IDRI under annotations with 80% noise ratio. Code is available at: https://github.com/Amber-Believe/ReliableMutualDistillation.

Localization Distillation for Object Detection.

Previous knowledge distillation (KD) methods for object detection mostly focus on feature imitation instead of mimicking the prediction logits due to its inefficiency in distilling the localization information. In this paper, we investigate whether logit mimicking always lags behind feature imitation. Towards this goal, we first present a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student. Second, we introduce the concept of valuable localization region that can aid to selectively distill the classification and localization knowledge for a certain region. Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and the absence of localization distillation is a critical reason for why logit mimicking under-performs for years. The thorough studies exhibit the great potential of logit mimicking that can significantly alleviate the localization ambiguity, learn robust feature representation, and ease the training difficulty in the early stage. We also provide the theoretical connection between the proposed LD and the classification KD, that they share the equivalent optimization effect. Our distillation scheme is simple as well as effective and can be easily applied to both dense horizontal object detectors and rotated object detectors. Extensive experiments on the MS COCO, PASCAL VOC, and DOTA benchmarks demonstrate that our method can achieve considerable AP improvement without any sacrifice on the inference speed. Our source code and pretrained models are publicly available at https://github.com/HikariTJU/LD.

Co-catalysis of rhodium/phosphoramidite catalyst and ZSM-35(10) for the tandem hydroformylation-acetalization of olefins.

The Rh/BINAPa and ZSM-35(10) co-catalyzed tandem hydroformylation-acetalization of olefins has been developed. A series of olefins with various alcohols performed well in the process, affording the corresponding acetals with high regioselectivities (l/b ≥ 30.5) and excellent catalytic activities (TON of the Rh catalyst up to 4.3 × 104). Control experiments and DFT calculations indicated that the Rh/L11-catalyzed hydroformylation occurred in the solvent outside the molecular sieve, while the acetalization of intermediate aldehydes with alcohols takes place mainly in the interior of the molecular sieve.

Relationships of beans intake with chronic kidney disease in rural adults: A large-scale cross-sectional study.

Background and aims: Dietary factors play an important role in the development of chronic kidney disease (CKD). However, evidence on the relationship of beans consumption with CKD remains limited and inconclusive, especially in the middle-and low-income populations. The current study aimed to investigate the relationships of beans intake with indicators of kidney injury and CKD prevalence in rural adults. Methods: A total of 20,733 rural adults from the Henan Rural Cohort Study in 2018-2022 were included. The total beans intake was collected using a validated food frequency questionnaire. Indicators of kidney injury and CKD was determined by the estimated glomerular filtration rate and the urinary albumin to creatinine ratio. Generalized linear regression and logistic regression models were applied to estimate the relationship of beans intake with continuous and dichotomized indicators of renal function, respectively. Results: Of the 20,733 participants, 2,676 (12.91%) subjects were identified as CKD patients. After adjusting for potential confounders, participants in the higher quartiles of beans intake had a lower prevalence of CKD (odds ratio and 95% confidence interval, OR (95%CI); Q2: 0.968(0.866-1.082); Q3: 0.836(0.744-0.939); Q4: 0.854(0.751-0.970)) and albuminuria (Q2: 0.982(0.875-1.102); Q3: 0.846(0.750-0.954); Q4: 0.852 (0.746-0.973)), compared with the Q1. Per 50 g/day increment in beans intake was significantly associated with a 5 and 4% decreased prevalence of albuminuria and CKD, respectively. These inverse relationships were also significant in the subgroups of men, elder, and high-income participants (p < 0.05). Conclusion: Dietary beans intake was inversely associated with the prevalence of albuminuria and CKD in rural adults, suggesting that promoting soy food intake might help reduce the occurrence of CKD in rural adults.

Deep-learning based quantification model for hip bone marrow edema and synovitis in patients with spondyloarthritis based on magnetic resonance images.

Objectives: Hip inflammation is one of the most common complications in patients with spondyloarthritis (SpA). Herein, we employed use of a deep learning-based magnetic resonance imaging (MRI) evaluation model to identify irregular and multiple inflammatory lesions of the hip. Methods: All of the SpA patients were enrolled at the Xijing Hospital. The erythrocyte sediment rate (ESR), C-reactive protein (CRP), hip function Harris score, and disease activity were evaluated by clinicians. Manual MRI annotations including bone marrow edema (BME) and effusion/synovitis, and a hip MRI scoring system (HIMRISS) assessment was performed by experienced musculoskeletal radiologists. The segmentation accuracies of four deep learning models, including U-Net, UNet++, Attention-Unet, and HRNet, were compared using five-fold cross-validation. The clinical agreement of U-Net was evaluated with clinical symptoms and HIMRISS results. Results: A total of 1945 MRI slices of STIR/T2WI sequences were obtained from 195 SpA patients with hip involvement. After the five-fold cross-validation, U-Net achieved an average segmentation accuracy of 88.48% for the femoral head and 69.36% for inflammatory lesions, which are higher than those obtained by the other three models. The UNet-score, which was calculated based on the same MRI slices as HIMRISS, was significantly correlated with the HIMRISS scores and disease activity indexes (p values <0.05). Conclusion: This deep-learning based automatic MRI evaluation model could achieve similar quantification performance as an expert radiologist, and it has the potential to improve the accuracy and efficiency of clinical diagnosis for SpA patients with hip involvement.

Cysteine carboxyethylation generates neoantigens to induce HLA-restricted autoimmunity.

Autoimmune diseases such as ankylosing spondylitis (AS) can be driven by emerging neoantigens that disrupt immune tolerance. Here, we developed a workflow to profile posttranslational modifications involved in neoantigen formation. Using mass spectrometry, we identified a panel of cysteine residues differentially modified by carboxyethylation that required 3-hydroxypropionic acid to generate neoantigens in patients with AS. The lysosomal degradation of integrin αIIb [ITGA2B (CD41)] carboxyethylated at Cys96 (ITGA2B-ceC96) generated carboxyethylated peptides that were presented by HLA-DRB1*04 to stimulate CD4+ T cell responses and induce autoantibody production. Immunization of HLA-DR4 transgenic mice with the ITGA2B-ceC96 peptide promoted colitis and vertebral bone erosion. Thus, metabolite-induced cysteine carboxyethylation can give rise to pathogenic neoantigens that lead to autoreactive CD4+ T cell responses and autoantibody production in autoimmune diseases.

Alterations in the human oral microbiota in systemic lupus erythematosus.

BACKGROUND: Alterations in oral microbiota in patients with systemic lupus erythematosus (SLE) is less evaluated. The aim of this study was to compare the characteristics of the oral microbiome in SLE patients and healthy controls, and construct an SLE classifier based on the oral microbiota. METHODS: We sequenced tongue-coating samples of individuals in treatment-naïve SLE (n = 182) and matched healthy controls (n = 280). We characterized the oral microbiome and constructed a microbial classifier in the derivation cohort and validated the results in the validation cohorts. Furthermore, the oral microbiome of posttreatment SLE (n = 73) was characterized. RESULTS: The oral microbial diversity of SLE was increased, and the microbial community was different between SLE and healthy controls. The genera Prevotella and Veillonella were enriched, while Streptococcus and Porphyromonas were reduced in SLE. In addition, an increase was noted in 27 predicted microbial functions, while a decrease was noted in 34 other functions. Thirty-nine operational taxonomy units (OTUs) were identified to be related with seven clinical indicators. Two OTUs were identified to construct a classifier, which yielded area under the curve values of 0.9166 (95% CI 0.8848-0.9483, p < 0.0001), 0.8422 (95% CI 0.7687-0.9157, p < 0.0001), and 0.8406 (95% CI 0.7677-0.9135, p < 0.0001) in the derivation, validation, and cross-regional validation groups, respectively. Moreover, as disease activity increased, Abiotrophia and Lactobacillales increased, while Phyllobacterium and unclassified Micrococcusaceae decreased. Finally, nine OTUs were selected to construct a classifier distinguishing posttreatment SLE patients from healthy controls, which achieved a diagnostic efficacy of 0.9942 (95% CI 0.9884-1, p < 0.0001). CONCLUSIONS: Our study comprehensively characterizes the oral microbiome of SLE and shows the potential of the oral microbiota as a non-invasive diagnostic biomarker in SLE.