Naringenin nanoparticles targeting cyclin B1 suppress the progression of rheumatoid arthritis-associated lung cancer by inhibiting fibroblast-to-myofibroblast transition.

There is growing evidence of an elevated risk of lung cancer in patients with rheumatoid arthritis. The poor prognosis of rheumatoid arthritis-associated lung cancer and the lack of therapeutic options pose an even greater challenge to the clinical management of patients. This study aimed to identify potential molecular targets associated with the progression of rheumatoid arthritis-associated lung cancer and examine the efficacy of naringenin nanoparticles targeting cyclin B1. Mendelian randomizatio analysis revealed that rheumatoid arthritis has a positive correlation with the risk of lung cancer. Cyclin B1 was significantly upregulated in patients with rheumatoid arthritis-associated lung cancer and was significantly overexpressed in synovial tissue fibroblasts. Furthermore, the overexpression of cyclin B1 in rheumatoid arthritis fibroblast-like synoviocytes, which promotes their proliferation and fibroblast-to-myofibroblast transition, can significantly contribute to the growth and infiltration of lung cancer cells. Importantly, our prepared naringenin nanoparticles targeting cyclin B1 effectively attenuated proliferation and fibroblast-to-myofibroblast transition by blocking cells at the G2/M phase. In vivo experiments, naringenin nanoparticles targeting cyclin B1 significantly alleviated the development of collagen-induced arthritis and lung orthotopic tumors. Collectively, our results reveal that naringenin nanoparticles targeting cyclin B1 can suppress the progression of rheumatoid arthritis-associated lung cancer by inhibiting fibroblast-to-myofibroblast transition. These findings provide new insights into the treatment of rheumatoid arthritis-associated lung cancer therapy.

Style Uncertainty Based Self-Paced Meta Learning for Generalizable Person Re-Identification.

Domain generalizable person re-identification (DG ReID) is a challenging problem, because the trained model is often not generalizable to unseen target domains with different distribution from the source training domains. Data augmentation has been verified to be beneficial for better exploiting the source data to improve the model generalization. However, existing approaches primarily rely on pixel-level image generation that requires designing and training an extra generation network, which is extremely complex and provides limited diversity of augmented data. In this paper, we propose a simple yet effective feature based augmentation technique, named Style-uncertainty Augmentation (SuA). The main idea of SuA is to randomize the style of training data by perturbing the instance style with Gaussian noise during training process to increase the training domain diversity. And to better generalize knowledge across these augmented domains, we propose a progressive learning to learn strategy named Self-paced Meta Learning (SpML) that extends the conventional one-stage meta learning to multi-stage training process. The rationality is to gradually improve the model generalization ability to unseen target domains by simulating the mechanism of human learning. Furthermore, conventional person Re-ID loss functions are unable to leverage the valuable domain information to improve the model generalization. So we further propose a distance-graph alignment loss that aligns the feature relationship distribution among domains to facilitate the network to explore domain-invariant representations of images. Extensive experiments on four large-scale benchmarks demonstrate that our SuA-SpML achieves state-of-the-art generalization to unseen domains for person ReID.