Introduction: Activated platelets exert a proinflammatory action that can be largely ascribed to their ability to interact with monocytes. However, the mechanisms that promote dynamic changes in monocyte subsets in rheumatoid arthritis (RA) have not been clearly identified. The aim of this study was to determine whether platelet activation and the consequent formation of monocyte-platelet aggregates (MPA) might induce a proinflammatory phenotype in circulating monocytes in RA. Methods: The surface phenotype of platelets and the frequencies of monocyte subpopulations in the peripheral blood of RA patients were determined using flow cytometry. Platelets were sorted and co-cultured with monocytes. In addition, monocyte activation was assessed by measuring the nuclear factor kappa B (NF-κB) pathway. The disease activity was evaluated using the 28-joint disease activity score. Results: Platelet activation, circulating intermediate monocytes (Mon2) and MPA formation were significantly elevated in RA, especially in those with active disease status. Furthermore, Mon2 monocytes showed higher CD147 expression and responded to direct cell contact with activated platelets with higher cytokine production and matrix metallopeptidase 9 (MMP-9) secretion, which increased the expression of CD147. After the addition of specific antibodies for CD147, those effects were abolished. Furthermore, the NF-κB-driven inflammatory pathway may be involved in this process. Conclusions: These findings indicate an important role of platelet activation and the consequent formation of MPA in the generation of the proinflammatory cytokine milieu and for the promotion and maintenance of the pathogenically relevant Mon2 monocyte compartment in RA, which is likely to play an important role in the pathogenesis of autoimmunity.

Introduction: Cyclophilin A (CypA) is implicated in rheumatoid arthritis (RA) pathogenesis. We studied whether a novel anti-CypA single domain antibody (sdAb) treatment would modulate the severity of the disease in two different animal models of RA. Methods: A novel sdAb, named sdAbA1, was screened from an immunized camel sdAb library and found to have a high binding affinity (K_D = 6.9 × 10^-9 M) for CypA. The SCID-HuRAg model and the collagen-induced arthritis (CIA) in mice were used to evaluate the effects of sdAbA1 treatment on inflammation and joint destruction. For in vitro analysis, monocytes/macrophages were purified from synovial fluid and peripheral blood of patients with RA and were tested for the effect of anti-CypA sdAb on metalloproteinase (MMP) production. Human monocyte cell line THP-1 cells were selected and western blot analyses were performed to examine the potential signaling pathways. Results: In the CIA model of RA, the sdAbA1 treatment resulted in a significant decrease in clinical symptoms as well as of joint damage (P <0.05). In the SCID-HuRAg model, treatment with anti-CypA antibody sdAbA1 significantly reduced cartilage erosion, inflammatory cell numbers and MMP-9 production in the implanted tissues (P <0.05). It also significantly reduced the levels of human inflammatory cytokines IL-6 and IL-8 in mouse serum (P <0.05). No toxic effects were observed in the two animal models. In vitro results showed that sdAbA1 could counteract CypA-dependent MMP-9 secretion and IL-8 production by interfering with the ERK-NF-κB pathway. Conclusions: Blockade of CypA significantly inhibited synovitis and cartilage/bone erosion in the two tested animal models of RA. Our findings provide evidence that sdAbA1 may be a potential therapeutic agent for RA.

Purpose: The purpose of this study was to conduct research in mice to determine the radioprotective effects of troxerutin. Thirty-day survival, oxidative status and histological changes in the liver were all evaluated. Materials and methods: Troxerutin was administered orally to mice for six days before irradiation with different doses (6, 7, 8 and 10 Gy) of γ-rays and the mice were observed for 30 days after radiation to calculate 30-day survival and median lethal dose of 30 days (LD50/30). Its radioprotective efficacy was compared with the positive drug amifostine which is currently the most effective radioprotector. The animals pretreated with troxerutin for 6 days were sacrificed on day 11 after radiation (6 Gy) in order to make 10% homogenate and histological sections of liver. Antioxidant enzymes were detected in strict accordance with kit requirements. Histological liver sections were examined by hematoxylin-eosin (HE) staining. Results: Pretreatment with troxerutin resulted in a significantly higher 30-day survival rate for 70% of mice compared with 30% of irradiation group after exposure to a potentially lethal dose of 8 Gy; LD50/30 of drug treatment group was 9 Gy compared with 7.7 Gy for irradiation group. The results indicated that troxerutin increased the activity of various antioxidant enzymes, such as superoxide dismutase (SOD) in mice liver, which was reduced by radiation treatment. Maleic dialdehyde (MDA) levels were significantly reduced by troxerutin, which was increased after 6 Gy radiation. These results were further confirmed by histopathological examinations which indicated that pre-administration with the effective dose of troxerutin reduced the hepatic damage induced by radiation. Conclusions: Administration of troxerutin before irradiation, provided higher survival of experimental mice, improvement of biochemical parameters, and preserved major histological parameters of the liver. These results collectively indicate that troxerutin is an effective radioprotective agent.

Adiponectin (ADPN) plays an important role in cerebral ischemia‐reperfusion injury. Although previous studies have confirmed that ADPN pretreatment has a protective effect on ischemic stroke, the therapeutic effect of ADPN on ischemic stroke and the underlying mechanism are still unclear. In order to clarify these questions, focal transient cerebral ischemia was induced by middle cerebral artery occlusion (MCAO) in mice and ADPN was administered for three times at 6 h, 24 h, and 48 h after reperfusion. Meanwhile, a virus‐delivered HIF‐1α siRNA was used before ADPN administration. The infarct volume, neurological score, cellular apoptosis, and oxidative stress were assessed at 72 h after reperfusion. The long‐term outcome of mice after stroke was recorded as well. The results indicated that ADPN treatment reduced the infarct volume (P = 0.032), neurological deficits (P = 0.047), cellular apoptosis (P = 0.041), and oxidative responses (P = 0.031) at 72 h after MCAO. Moreover, ADPN increased both the protein level and transcriptional activity of HIF‐1α as evidenced by the transcription levels of VEGF (P = 0.046) and EPO (P = 0.043) at 72 h after MCAO. However, knockdown of HIF‐1α partially reversed the antioxidant and treatment effect of ADPN after cerebral ischemia. In the observation of long‐term outcome after ADPN treatment, it demonstrated that ADPN not only prevented the cerebral atrophy (P = 0.031) and the neurological function decline (P = 0.048), but also promoted angiogenesis (P = 0.028) after stroke. In conclusion, our findings suggest that ADPN is effective in treatment of ischemic stroke which could be attributed to the increased antioxidant capacity regulated by HIF‐1α.

Pulmonary arterial hypertension (PAH) is a severe complication of connective tissue disease (CTD) and is one of the leading causes of morbidity and mortality among patients with this condition. To establish an expert-based consensus on the diagnosis and treatment of CTD-associated PAH, a multidisciplinary consensus development panel was established. The consensus panel is composed of 45 experts in rheumatology, cardiology, pulmonology, and radiology, most of whom are members of the Group of Pulmonary Vascular and Interstitial Lung Diseases (ILD) Associated with Rheumatic Diseases. The consensus development panel compiled 9 recommendations for the diagnosis and treatment of CTD-associated PAH. It covers screening, diagnosis, disease evaluation, risk assessment, the use of immunosuppressive agents, and PAH-specific therapy with a treat-to-target approach. The consensus is intended to facilitate decision-making and standardize the care of CTD-associated PAH in China.

Background: Machine learning (ML) has been investigated for its predictive value in knee osteoarthritis (KOA) progression. However, systematic evidence on the effectiveness of ML is still lacking, posing a challenge to precision prevention. Objective: This systematic review aimed to systematically assess the application status and accuracy of ML in predicting KOA progression and to compare the predictive performance of ML, traditional methods, and deep learning under different datasets, model types, modeling variables, and definitions of KOA progression. Methods: Following the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) statement, a systematic search was conducted in Embase, Web of Science, PubMed, and Cochrane Library up to October 10, 2025. Two investigators were independently responsible for study screening, data extraction, and risk-of-bias assessment in included studies using the Prediction Model Risk of Bias Assessment Tool. Meta-analyses were conducted on the concordance index (C-index) and diagnostic 4-fold table using a random effects model, with prediction intervals (PIs) reported. In addition, subgroup analyses were performed by model type, modeling variable, and definition of KOA progression. Results: A total of 32 studies were included. The overall risk of bias was considered low in 8 studies, high in 13 studies, and unclear in 11 studies. For predicting all progression, the pooled C-index was 0.773 (95% CI 0.727‐0.821; 95% PI 0.567‐1.000) for the clinical feature–based model, 0.798 (95% CI 0.755‐0.843; 95% PI 0.646‐0.984) for the magnetic resonance imaging (MRI)–based model, 0.712 (95% CI 0.657‐0.772; 95% PI 0.526‐0.965) for the X-ray–based model, 0.806 (95% CI 0.765‐0.849; 95% PI 0.639‐1.000) for the MRI+clinical feature–based model, 0.772 (95% CI 0.731‐0.815; 95% PI 0.610‐0.976) for the X-ray+clinical feature–based model, and 0.731 (95% CI 0.669‐0.798; 95% PI 0.518‐1.000) for the clinical feature+X-ray+MRI–based model. The clinical feature–based model was established mainly using logistic regression and exhibited accuracy comparable to other ML models. Among image-based models, traditional ML or deep learning possessed higher accuracy. Conclusions: This systematic review used CIs to estimate mean effects and PIs to estimate the potential range of effects in future scenarios. It systematically compared the performance of ML in predicting KOA progression under different model types, modeling variables, and definitions of KOA progression. ML models demonstrate certain discriminatory power in predicting KOA progression, but current evidence should be interpreted with caution due to various sources of significant heterogeneity, such as variations in the definition of KOA progression and validation strategies. Future research should standardize the definition of KOA progression, enhance methodological rigor, and conduct stringent external validation to improve model reliability and facilitate clinical translation.