Osteoarthritis (OA) is characterized by cartilage degeneration and inflammation. Procyanidin B2 (PCB2), a natural flavonoid compound, exhibits potential anti-inflammatory and anti-oxidative effects against several diseases. However, its curative effects on OA remain unclear. Herein, we explored the anti-arthritic effects of PCB2 on OA onset and progress and its potential mechanism. CCK-8 assays and EdU staining were used to assess the cytotoxic effects and cell proliferation activity of PCB2. Flow cytometry was used to detect apoptosis in chondrocytes. ELISA, qPCR, and western blotting, were applied to explore the expression of apoptosis and senescence-associated secretion phenotype (SASP) factors. The Nrf2/NF-κB signaling cascade was explored using immunofluorescence and western blotting. Additionally, we silenced the Nrf2 gene using siRNAs to verify its function in PCB2 regulation of senescence and apoptosis phenotypes. Safranin O-Fast Green (SO) and immunohistochemical staining were used to explore the effects of PCB2 on OA model rats. PCB2 dampened interleukin (IL)-1β-triggered expression of SASP factors in vitro. Additionally, PCB2 diminished IL-1β-triggered destruction of the extracellular matrix (ECM) via downregulating the expression of MMPs, while upregulating the expression of collagen II and aggrecan. In addition, PCB2 treatment reduced IL-1β-induced apoptosis of chondrocytes. Mechanistically, PCB2 could attenuated chondrocyte senescence in vitro via the Nrf2/NF-κB pathway. Moreover, PCB2 exhibited anti-apoptotic properties via the Nrf2/BAX/Bcl-2 pathway. PCB2 alleviated knee cartilage degeneration in an OA rat model. Our results suggest that PCB2 may be used as a therapeutic agent for OA.

Sphingolipid metabolism (SM) fuels tumorigenesis and the malignant progression of osteosarcoma (OS), which leads to an unfavorable prognosis. Elucidating the molecular mechanisms underlying SM in osteosarcoma and developing a SM-based prognostic signature could be beneficial in the clinical setting. This study included 88 frozen OS samples to recognize the vital SM-relevant genes in the development of OS utilizing univariate Cox regression. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was conducted on the SM- relevant genes to minimize the risk of overfitting. The prognostic signature was generate utilizing the multivariable Cox regression analysis and was verified in the validation cohort. Moreover, cellular and molecular mechanisms associated with SM have an unfavorable prognosis for OS patients and have been widely studied. Resultantly, an SM-based prognostic risk model was established according to critical prognostic genes (CBS, GLB1, and HACD1), which had an excellent ability to predict the prognosis of OS patients (AUC for the train cohort was 0.887 and AUC for validation cohort was 0.737). The high-risk OS patients identified based on this prognostic signature had significantly poor immune microenvironment, indicated by significantly low immune score (mean=216.290 ± 662.463), reduced infiltrations of 25 immune cells, including NK cells (LogFC= -0.3597), CD8+T cells ((LogFC=-0.2346), Cytolytic activity ((LogFC=-0.1998), etc. The immunosuppressive microenvironment could be due to dysregulated SM of glycolipids. Further, a nomogram was constructed by integrating the SM-based prognostic signature and clinical paraments to facilitate clinical application. The nomogram could accurately predict the prognosis of OS invalids. Collectively, this study clarified the function of SM in the development of OS and helped develop a tool for risk stratification based on SM-related genes with application in clinical settings. The results of our study will aid in identifying high-risk patients and provide individualized treatments.

There are very few longitudinal studies which have previously conducted an investigation into whether eye diseases are a risk for arthritis, and how this occurs. The study employed a variety of machine-learning algorithms, including random forest for investigating the risks, and to elucidate these underlying mechanisms by focusing on five aspects containing 389 characterized variables (mental health and wellbeing; physical health; disability, functional impairment and helpers; health behavior; and health measures). The study population included 8,423 individuals. Cataracts, glaucoma, and other eye diseases increase the likelihood of arthritis after two years by 131.8% (odds ratio (OR)=2.318, 95% confidence interval: 1.748 to 3.038), 123.1% (OR=2.231, 1.306 to 3.626), and 91.1% (OR=1.911, 1.501 to 2.415). Random forest corroborated that cataract contributes the most to arthritis risks after two years, followed by other eye diseases and glaucoma (mean Gini-index: 5.20, 2.11, 1.31). It is of note that the potential mechanisms of cataract-induced arthritis risk were elucidated extensively. The control domains of life quality, negative aging self-perceptions, mobility (steadiness, physical limitations, and muscle strength) and memory impairments, and sleep quality mediated the relationship between cataracts and arthritis significantly. Furthermore, different eye diseases affected osteoarthritis, rheumatoid arthritis, and other arthritis to varying degrees. Eye diseases increased the risk of arthritis, whereby cataracts were the most significant. Interventions which target these discovered mechanisms may be the preferred levers for reducing cataract-related arthritis risk.