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UNASSIGNED: Quantitatively assess the severity and predict the mortality of interstitial lung disease (ILD) associated with Rheumatoid arthritis (RA) was a challenge for clinicians. This study aimed to construct a radiomics nomogram based on chest computed tomography (CT) imaging by using the ILD-GAP (gender, age, and pulmonary physiology) index system for clinical management.
UNASSIGNED: Chest CT images of patients with RA-ILD were retrospectively analyzed and staged using the ILD-GAP index system. The balanced dataset was then divided into training and testing cohorts at a 7:3 ratio. A clinical factor model was created using demographic and serum analysis data, and a radiomics signature was developed from radiomics features extracted from the CT images. Combined with the radiomics signature and independent clinical factors, a nomogram model was established based on the Rad-score and clinical factors. The model capabilities were measured by operating characteristic curves, calibration curves and decision curves analyses.
UNASSIGNED: A total of 177 patients were divided into two groups (Group I, n = 107; Group II, n = 63). Krebs von den Lungen-6, and nineteen radiomics features were used to build the nomogram, which showed favorable calibration and discrimination in the training cohort [AUC, 0.948 (95% CI: 0.910-0.986)] and the testing validation cohort [AUC, 0.923 (95% CI: 0.853-0.993)]. Decision curve analysis demonstrated that the nomogram performed well in terms of clinical usefulness.
UNASSIGNED: The CT-based radiomics nomogram model achieved favorable efficacy in predicting low-risk RA-ILD patients.

Rheumatoid arthritis (RA) and arthrofibrosis (AF) are both chronic synovial hyperplasia diseases that result in joint stiffness and contractures. They shared similar symptoms and many common features in pathogenesis. Our study aims to perform a comprehensive analysis between RA and AF and identify novel drugs for clinical use. Based on the text mining approaches, we performed a correlation analysis of 12 common joint diseases including arthrofibrosis, gouty arthritis, infectious arthritis, juvenile idiopathic arthritis, osteoarthritis, post infectious arthropathies, post traumatic osteoarthritis, psoriatic arthritis, reactive arthritis, rheumatoid arthritis, septic arthritis, and transient arthritis. 5 bulk sequencing datasets and 4 single-cell sequencing datasets of RA and AF were integrated and analyzed. A novel drug repositioning method was found for drug screening, and text mining approaches were used to verify the identified drugs. RA and AF performed the highest gene similarity (0.77) and functional ontology similarity (0.84) among all 12 joint diseases. We figured out that they share the same key pathogenic cell including CD34 + sublining fibroblasts (CD34-SLF) and DKK3 + sublining fibroblasts (DKK3-SLF). Potential therapeutic target database (PTTD) was established with the differential expressed genes (DEGs) of these key pathogenic cells. Based on the PTTD, 15 potential drugs for AF and 16 potential drugs for RA were identified. This work provides a new perspective on AF and RA study which enhances our understanding of their pathogenesis. It also shed light on their underlying mechanism and open new avenues for drug repositioning studies.

Clinical studies have shown that Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) is associated with aggressive periodontitis and can potentially trigger or exacerbate rheumatoid arthritis (RA). However, the mechanism is poorly understood. Here, we show that systemic infection with A. actinomycetemcomitans triggers the progression of arthritis in mice anti-collagen antibody-induced arthritis (CAIA) model following IL-1β secretion and cell infiltration in paws in a manner that is dependent on caspase-11-mediated inflammasome activation in macrophages. The administration of polymyxin B (PMB), chloroquine, and anti-CD11b antibody suppressed inflammasome activation in macrophages and arthritis in mice, suggesting that the recognition of lipopolysaccharide (LPS) in the cytosol after bacterial degradation by lysosomes and invasion via CD11b are needed to trigger arthritis following inflammasome activation in macrophages. These data reveal that the inhibition of caspase-11-mediated inflammasome activation potentiates aggravation of RA induced by infection with A. actinomycetemcomitans. This work highlights how RA can be progressed by inflammasome activation as a result of periodontitis-associated bacterial infection and discusses the mechanism of inflammasome activation in response to infection with A. actinomycetemcomitans.

Heparan sulfate (HS) proteoglycans are important regulators of cellular responses to soluble mediators such as chemokines, cytokines and growth factors. We profiled changes in expression of genes encoding HS core proteins, biosynthesis enzymes and modifiers during macrophage polarisation, and found that the most highly regulated gene was Sulf2, an extracellular HS 6-O-sulfatase that was markedly downregulated in response to pro-inflammatory stimuli. We then generated Sulf2+/- bone marrow chimeric mice and examined inflammatory responses in antigen-induced arthritis, as a model of rheumatoid arthritis. Resolution of inflammation was impaired in myeloid Sulf2+/- chimeras, with elevated joint swelling and increased abundance of pro-arthritic Th17 cells in synovial tissue. Transcriptomic and in vitro analyses indicated that Sulf2 deficiency increased type I interferon signaling in bone marrow-derived macrophages, leading to elevated expression of the Th17-inducing cytokine IL6. This establishes that dynamic remodeling of HS by Sulf2 limits type I interferon signaling in macrophages, and so protects against Th17-driven pathology.

UNASSIGNED: Interactions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diagnosis of RA patients who also had MetS.
UNASSIGNED: Three RA datasets and one MetS dataset were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) were employed to identify hub genes in MetS-RA. Enrichment analysis was used to explore underlying common pathways between MetS and RA. Receiver operating characteristic curves were applied to assess the diagnostic performance of nomogram constructed based on hub genes. Protein-protein interaction, Connectivity Map (CMap) analyses, and molecular docking were utilized to predict the potential small molecule compounds for MetS-RA treatment. qRT-PCR was used to verify the expression of hub genes in fibroblast-like synoviocytes (FLS) of MetS-RA. The effects of small molecule compounds on the function of RA-FLS were evaluated by wound-healing assays and angiogenesis experiments. The CIBERSORT algorithm was used to explore immune cell infiltration in MetS and RA.
UNASSIGNED: MetS-RA key genes were mainly enriched in immune cell-related signaling pathways and immune-related processes. Two hub genes (TYK2 and TRAF2) were selected as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning and proved to have a high diagnostic value (area under the curve, TYK2, 0.92; TRAF2, 0.90). qRT-PCR results showed that the expression of TYK2 and TRAF2 in MetS-RA-FLS was significantly higher than that in non-MetS-RA-FLS (nMetS-RA-FLS). The combination of CMap analysis and molecular docking predicted camptothecin (CPT) as a potential drug for MetS-RA treatment. In vitro validation, CPT was observed to suppress the cell migration capacity and angiogenesis capacity of MetS-RA-FLS. Immune cell infiltration results revealed immune dysregulation in MetS and RA.
UNASSIGNED: Two hub genes were identified in MetS-RA, a nomogram for the diagnosis of RA and MetS was established based on them, and a potential therapeutic small molecule compound for MetS-RA was predicted, which offered a novel research perspective for future serum-based diagnosis and therapeutic intervention of MetS-RA.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.

Inflammatory diseases including rheumatoid arthritis are major health problems. Although different techniques and drugs are clinically available for the diagnosis and therapy of the disease, novel approaches regarding radiolabeled drug delivery systems are researched. Hence, in the present study, it was aimed to design, prepare, and characterize 99mTc-radiolabeled and tofacitinib citrate-encapsulated microsphere loaded poloxamer in situ gel formulations for the intra-articular treatment. Among nine different microsphere formulations, MS/TOFA-9 was chosen as the most proper one due to particle size, high encapsulation efficiency, and in vitro drug release behavior. Poloxamer 338 at a concentration of 15% was used to prepare in situ gel formulations. For intra-articular administration, microspheres were dispersed in an in situ gel containing 15% Poloxamer 338 and characterized in terms of gelation temperature, viscosity, rheological, mechanical, and spreadability properties. After the determination of the safe dose for MS/TOFA-9 and PLX-MS/TOFA-9 as 40 µL/mL in the cell culture study performed on healthy cells, the high anti-inflammatory effects were due to significant cellular inhibition of fibroblasts. In the radiolabeling studies with 99mTc, the optimum radiolabeling condition was determined as 200 ppm SnCl2 and 0.5 mg ascorbic acid, and both 99mTc-MS/TOFA-9 and 99mTc-PLX-MS/TOFA-9 exhibited high cellular binding capacity. In conclusion, although further in vivo experiments are required, PLX-MS/TOFA-9 was found to be a promising agent for intra-articular injection in rheumatoid arthritis.

BACKGROUND: Cryptococcosis is an infectious disease caused by encapsulated heterobasidiomycete yeasts. As an opportunistic pathogen, cryptococcal inhalation infection is the most common. While Primary cutaneous cryptococcosis is extremely uncommon.
METHODS: A 61-year-old woman with a history of rheumatoid arthritis on long-term prednisone developed a red plaque on her left thigh. Despite initial antibiotic treatment, the erythema worsened, leading to rupture and fever. Microbiological analysis of the lesion\'s secretion revealed Candida albicans, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus epidermidis. Skin biopsy showed thick-walled spores, and culture confirmed primary cutaneous infection with Cryptococcus neoformans. Histopathological stains were positive, and mass spectrometry identified serotype A of the pathogen. The patient was treated with oral fluconazole and topical nystatin, resulting in significant improvement and near-complete healing of the skin lesion within 2.5 months.
CONCLUSIONS: Primary cutaneous cryptococcosis was a primary skin infection exclusively located on the skin. It has no typical clinical manifestation of cutaneous infection of Cryptococcus, and culture and histopathology remain the gold standard for diagnosing. The recommended medication for Primary cutaneous cryptococcosis is fluconazole. When patients at risk for opportunistic infections develop skin ulcers that are unresponsive to antibiotic, the possibility of primary cutaneous cryptococcosis needs to be considered.

UNASSIGNED: Sarcopenia is linked to an unfavorable prognosis in individuals with rheumatoid arthritis (RA). Early identification and treatment of sarcopenia are clinically significant. This study aimed to create and validate a nomogram for predicting sarcopenia risk in RA patients, providing clinicians with a reliable tool for the early identification of high-risk patients.
UNASSIGNED: Patients with RA diagnosed between August 2022 and January 2024 were included and randomized into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multifactorial logistic regression analysis were used to screen the risk variables for RA-associated muscle loss and to create an RA sarcopenia risk score. The predictive performance and clinical utility of the risk model were evaluated by plotting the receiver operating characteristic curve and calculating the area under the curve (AUC), along with the calibration curve and clinical decision curve (DCA).
UNASSIGNED: A total of 480 patients with RA were included in the study (90% female, with the largest number in the 45-59 age group, about 50%). In this study, four variables (body mass index, disease duration, hemoglobin, and grip strength) were included to construct a nomogram for predicting RA sarcopenia. The training and validation set AUCs were 0.915 (95% CI: 0.8795-0.9498) and 0.907 (95% CI: 0.8552-0.9597), respectively, proving that the predictive model was well discriminated. The calibration curve showed that the predicted values of the model were basically in line with the actual values, demonstrating good calibration. The DCA indicated that almost the entire range of patients with RA can benefit from this novel prediction model, suggesting good clinical utility.
UNASSIGNED: This study developed and validated a nomogram prediction model to predict the risk of sarcopenia in RA patients. The model can assist clinicians in enhancing their ability to screen for RA sarcopenia, assess patient prognosis, make early decisions, and improve the quality of life for RA patients.