Artificial intelligence (AI) is rapidly evolving and reshaping everyday life for everyone, including healthcare. The general-purpose models, such as the GPT series, Claude, Gemini, Grok, and others, have demonstrated remarkable capabilities in the solution of medical problems and presenting opportunities and challenges for medical research and clinical practice [1]. Rheumatology, characterized by complex chronic diseases, benefits a lot from rapid advances in LLMs, but must also carefully manage the associated risks.
Include applications in Table 1.
Despite rapid and extensive resource investment in AI research, significant limitations persist. First, a primary concern for general use remains the validation of AI accuracy. AI hallucination has yet to be fully resolved within current training paradigms, although numerous approaches to solve this issue have already been proposed. Second, the various training datasets lead to data bias, potentially exacerbating health inequities. Third, cloud-based deployment raises significant concerns regarding patient privacy and data security. Conversely, running AI locally is often constrained by limited computing power on personal devices, thereby further restricting its practical application. Fourth, even with current reasoning models, the internal cognitive mechanisms remain inadequately understood. Therefore, these concerns must be carefully considered when applying AI to clinical scenarios.
LLMs offer significant promise as tools for rheumatology, with potential improvement of clinical practice, medical research, and personal life, but several challenges remain. To effectively utilize these technologies, their differences in ability, accuracy, adherence to the highest ethical standards, and flexibility to continuous learning need to be carefully evaluated.
Po-Cheng Shih wrote the manuscript. James Cheng Chung Wei supervise and edit the manuscript. All authors are responsible for the final version of the manuscript.
The views expressed in this article represent those of the authors and do not necessarily reflect the official position of the institutions with which they are affiliated.
Dr. James Wei is the Editor-in-Chief of IJRD and a co-author of this article. They were excluded from editorial decision-making related to the acceptance and publication of this article. Editorial decision-making was handled independently by other editors to minimize bias. The other authors declare no conflicts of interest.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Systemic sclerosis (SSc) is a rare autoimmune disease with high mortality, often due to cardiopulmonary complications. Hydroxychloroquine (HCQ), commonly used in other rheumatic diseases, has immunomodulatory and potentially cardioprotective effects, but its role in SSc remains unclear. This study aimed to evaluate the association between HCQ use and the risk of mortality, ischemic heart disease (IHD), and pulmonary hypertension (PH) in a large real-world SSc cohort.
This retrospective cohort study utilized de-identified electronic medical records from the TriNetX Research network. Adults with an SSc diagnosis (ICD-10-CM: M34) were divided into HCQ users and non-users. After 1:1 propensity score matching for demographics, comorbidities and medications, outcomes including mortality, PH, acute myocardial infarction (MI), cerebral infarction, conduction heart disease, and myocarditis were assessed over 5 years. Risk ratios (RR) and Kaplan–Meier hazard ratios were calculated.
Out of 17 395 HCQ users and 58 576 non-users, 15 485 propensity score matched pairs were analyzed. Over 5 years, HCQ users showed higher PH (RR 1.124, 95% CI, 1.052–1.200, p < 0.001) but lower mortality (RR 0.719, 95% CI, 0.674–0.767, p < 0.001), indicating potential survival benefits. No significant differences were observed for IHD, MI, cerebral infarction, conduction disorders, or myocarditis.
Our research indicated that although patients on HCQ had a higher prevalence of PH, they exhibited lower mortality rates, suggesting a possible survival benefit. Further prospective studies are needed to explore these findings and clarify HCQ's role in SSC management.
Neutrophil extracellular traps (NETs) significantly contribute to rheumatoid arthritis (RA) pathogenesis, though their exact mechanisms remain unclear. In this research, we explored how NETs influence RA development through the PANoptosis core molecule AIM2.
Enzyme-linked immunosorbent assay and Quant-iT Pico Green measured AIM2 and cell-free DNA (cfDNA) levels in synovial fluid. Immunohistochemistry examined the levels of AIM2 in synovial tissues. In vitro, the CCK-8 assay evaluated cell proliferation. Western blot detected the expression changes of AIM2, PANoptosis-related proteins, and NF-κB pathway-related proteins. Real-time quantitative PCR measured mRNA levels of IL-1β, IL-18, and IL-6. Immunofluorescence quantified AIM2, Caspase-8, GSDMD, and p-MLKL fluorescence intensity and cfDNA/AIM2 co-localization. An AIM2-silenced cell model was established to examine changes in the AIM2, PANoptosis-related proteins, and NF-κB pathway-related proteins (Western blot), fluorescence intensity (IF), inflammatory cytokine transcription (RT-qPCR), and RA-FLS migration (scratch and transwell assays).
Our results showed an increase in AIM2 expression in RA synovial fluid and tissues, which correlates with both cfDNA high levels and clinical disease activity scores. In vitro, in RA fibroblast-like synoviocytes (RA-FLSs), NETs elevated AIM2 and PANoptosis-related protein levels, activated the NF-κB signaling pathway, and enhanced the release of inflammatory cytokines. Treatment with DNase1 and AIM2 silencing resulted in lower levels of PANoptosis proteins and AIM2, inhibited NF-κB signaling, and decreased cytokine production.
This is the first time exploring the pathogenic mechanism of NETs-induced PANoptosis in RA, and targeting AIM2 may represent a potential new therapeutic approach for RA.
Aim: This study discussed the treatment effects and underlying mechanisms of the Qianggu Kangshu (QGKS) formula in rheumatoid arthritis (RA).
Methods: Rat models of collagen-induced arthritis (CIA) were established and divided into six groups: control group, model group, low-dose QGKS group (QGKS-L, 6.3 g/kg/day), middle-dose QGKS group (QGKS-M, 12.6 g/kg/day), high-dose QGKS group (QGKS-H, 25.2 g/kg/day), and methotrexate (MTX, 1 mg/kg/week). The histopathological changes regarding synovial and ankle tissues were evaluated using hematoxylin-eosin staining and safranin-O staining. Serum levels of TNF-α, IL-1β, and IL-6 were measured using ELISA kits. UPLC/MS-MS was used to analyze serum components, and 16S rDNA sequencing was conducted for analyzing the intestinal contents of the treated CIA rats.
Results: Arthritis symptoms were alleviated in QGKS-treated CIA rats, evidenced by decreased paw swelling and lower arthritis scores compared to control groups. QGKS treatment resulted in a marked decrease in proinflammatory cytokines (TNF-α, IL-1β, and IL-6). Histological analyses revealed reduced synovial inflammation and preserved cartilage integrity following QGKS intervention. Metabolomic profiling identified significant alterations in metabolic pathways related to glycerophospholipid metabolism and fatty acid degradation after addition of QGKS in CIA rats. Moreover, QGKS treatment improved gut microbiota composition, along with increased beneficial bacteria Lactobacillus and decreased pathogenic strains.
Conclusion: QGKS alleviated CIA symptoms by reducing inflammation, correcting metabolic disturbances, and modulating gut microbiota, suggesting its potential as a novel therapeutic option for RA.
Lupus nephritis (LN) represents one of the most severe complications associated with systemic lupus erythematosus (SLE) characterized by a complex pathogenesis. Animal models play a pivotal role in elucidating the pathogenesis of LN, facilitating drug discovery, and evaluating therapeutic interventions. A diverse array of LN animal models is currently employed in research. Concurrently, the evaluation of these animal models' validity is of paramount importance. This paper provides a comprehensive review of the key characteristics of common spontaneous, inducible, humanized and knockout models, along with an assessment of the effectiveness of LN models. Through a comparative analysis of these models, this study offers valuable references and guidance for LN research.
Objective: This study aimed to evaluate the diagnostic utility of combining musculoskeletal ultrasound (MSUS) with serum levels of hypoxia-inducible factor-1α (HIF-1α) and cyclooxygenase-2 (COX-2) for acute gouty arthritis (AGA).
Methods: The study included 112 patients hospitalized for gouty arthritis between February 2021 and February 2022. Of these, 60 patients presented with AGA, while 62 had non-acute gouty arthritis (NAGA). Semi-quantitative musculoskeletal ultrasound (MSUS) scores and serum levels of hypoxia-inducible factor-1α (HIF-1α) and cyclooxygenase-2 (COX-2) were compared between the two groups. A Disease Activity Score-28 (DAS28) was used to further classify the AGA group into low-activity and high-activity subgroups.
Results: MSUS scores, DAS28, and serum levels of HIF-1α and COX-2 were significantly elevated in the AGA group compared to the NAGA group (p < 0.05). Furthermore, within the AGA group, the high-activity subgroup exhibited higher MSUS semi-quantitative scores, DAS28 scores, and serum levels of HIF-1α and COX-2 compared to the low-activity subgroup (p < 0.05). Pearson correlation analysis revealed a positive correlation between the MSUS semi-quantitative score and serum levels of HIF-1α, COX-2, as well as the DAS28 score in patients diagnosed with AGA (p < 0.05). The combined application of MSUS semi-quantitative scoring and serological markers provided significantly enhanced diagnostic accuracy for AGA and better assessment of disease activity compared to any individual marker.
Conclusions: MSUS and serum HIF-1α and COX-2 represent valuable tools for assessing the acute phase and disease activity of AGA. The combined diagnostic approach demonstrates superior effectiveness and holds potential for broader clinical application.
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