Graphene oxide quantum dots-loaded sinomenine hydrochloride nanocomplexes for effective treatment of rheumatoid arthritis via inducing macrophage repolarization and arresting abnormal proliferation of fibroblast-like synoviocytes.

The characteristic features of the rheumatoid arthritis (RA) microenvironment are synovial inflammation and hyperplasia. Therefore, there is a growing interest in developing a suitable therapeutic strategy for RA that targets the synovial macrophages and fibroblast-like synoviocytes (FLSs). In this study, we used graphene oxide quantum dots (GOQDs) for loading anti-arthritic sinomenine hydrochloride (SIN). By combining with hyaluronic acid (HA)-inserted hybrid membrane (RFM), we successfully constructed a new nanodrug system named HA@RFM@GP@SIN NPs for target therapy of inflammatory articular lesions. Mechanistic studies showed that this nanomedicine system was effective against RA by facilitating the transition of M1 to M2 macrophages and inhibiting the abnormal proliferation of FLSs in vitro. In vivo therapeutic potential investigation demonstrated its effects on macrophage polarization and synovial hyperplasia, ultimately preventing cartilage destruction and bone erosion in the preclinical models of adjuvant-induced arthritis and collagen-induced arthritis in rats. Metabolomics indicated that the anti-arthritic effects of HA@RFM@GP@SIN NPs were mainly associated with the regulation of steroid hormone biosynthesis, ovarian steroidogenesis, tryptophan metabolism, and tyrosine metabolism. More notably, transcriptomic analyses revealed that HA@RFM@GP@SIN NPs suppressed the cell cycle pathway while inducing the cell apoptosis pathway. Furthermore, protein validation revealed that HA@RFM@GP@SIN NPs disrupted the excessive growth of RAFLS by interfering with the PI3K/Akt/SGK/FoxO signaling cascade, resulting in a decline in cyclin B1 expression and the arrest of the G2 phase. Additionally, considering the favorable biocompatibility and biosafety, these multifunctional nanoparticles offer a promising therapeutic approach for patients with RA.

PRTA:Joint extraction of medical nested entities and overlapping relation via parameter sharing progressive recognition and targeted assignment decoding scheme.

Nested entities and relationship extraction are two tasks for analysis of electronic medical records. However, most of existing medical information extraction models consider these tasks separately, resulting in a lack of consistency between them. In this paper, we propose a joint medical entity-relation extraction model with progressive recognition and targeted assignment (PRTA). Entities and relations share the information of sequence and word embedding layers in the joint decoding stage. They are trained simultaneously and realize information interaction by updating the shared parameters. Specifically, we design a compound triangle strategy for the nested entity recognition and an adaptive multi-space interactive strategy for relationship extraction. Then, we construct a parameter-shared information space based on semantic continuity to decode entities and relationships. Extensive experiments were conducted on the Private Liver Disease Dataset (PLDD) provided by Beijing Friendship Hospital of Capital Medical University and public datasets (NYT, ACE04 and ACE05). The results show that our method outperforms existing SOTA methods in most indicators, and effectively handles nested entities and overlapping relationships.

Inhibition of IL-17 signaling in macrophages underlies the anti-arthritic effects of halofuginone hydrobromide: Network pharmacology, molecular docking, and experimental validation.

BACKGROUND: Rheumatoid arthritis (RA) is a prevalent autoimmune disease marked by chronic synovitis as well as cartilage and bone destruction. Halofuginone hydrobromide (HF), a bioactive compound derived from the Chinese herbal plant Dichroa febrifuga Lour., has demonstrated substantial anti-arthritic effects in RA. Nevertheless, the molecular mechanisms responsible for the anti-RA effects of HF remain unclear. METHODS: This study employed a combination of network pharmacology, molecular docking, and experimental validation to investigate potential targets of HF in RA. RESULTS: Network pharmacology analyses identified 109 differentially expressed genes (DEGs) resulting from HF treatment in RA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses unveiled a robust association between these DEGs and the IL-17 signaling pathway. Subsequently, a protein-protein interaction (PPI) network analysis revealed 10 core DEGs, that is, EGFR, MMP9, TLR4, ESR1, MMP2, PPARG, MAPK1, JAK2, STAT1, and MAPK8. Among them, MMP9 displayed the greatest binding energy for HF. In an in vitro assay, HF significantly inhibited the activity of inflammatory macrophages, and regulated the IL-17 signaling pathway by decreasing the levels of IL-17 C, p-NF-κB, and MMP9. CONCLUSION: In summary, these findings suggest that HF has the potential to inhibit the activation of inflammatory macrophages through its regulation of the IL-17 signaling pathway, underscoring its potential in the suppression of immune-mediated inflammation in RA.

3D ARCNN: An Asymmetric Residual CNN for False Positive Reduction in Pulmonary Nodule.

Lung cancer is with the highest morbidity and mortality, and detecting cancerous lesions early is essential for reducing mortality rates. Deep learning-based lung nodule detection techniques have shown better scalability than traditional methods. However, pulmonary nodule test results often include a number of false positive outcomes. In this paper, we present a novel asymmetric residual network called 3D ARCNN that leverages 3D features and spatial information of lung nodules to improve classification performance. The proposed framework uses an internally cascaded multi-level residual model for fine-grained learning of lung nodule features and multi-layer asymmetric convolution to address the problem of large neural network parameters and poor reproducibility. We evaluate the proposed framework on the LUNA16 dataset and achieve a high detection sensitivity of 91.6%, 92.7%, 93.2%, and 95.8% for 1, 2, 4, and 8 false positives per scan, respectively, with an average CPM index of 0.912. Quantitative and qualitative evaluations demonstrate the superior performance of our framework compared to existing methods. 3D ARCNN framework can effectively reduce the possibility of false positive lung nodules in the clinical.

Multifunctional nanoparticles of sinomenine hydrochloride for treat-to-target therapy of rheumatoid arthritis via modulation of proinflammatory cytokines.

Sinomenine is a bioactive alkaloid isolated from the Chinese medicinal plant of Sinomenium acutum (Thunb.) Rehd.et Wils. Currently, sinomenine hydrochloride (SIN) preparations, classified as a natural disease-modifying anti-rheumatic drug (nDMARD), have been used for therapy of rheumatoid arthritis (RA); however, the efficacy of SIN was seriously limited by its short half-life, low bioavailability, and dose-dependent adverse reactions. In this study, a biomimetic nanocomplex based on Prussian blue nanoparticles (PB NPs) was developed for overcoming clinical limitations of SIN and accordingly improving its efficacy. In vitro studies showed that the nanocomplexes significantly inhibited abnormal proliferation of fibroblast-like synoviocytes (FLSs) by scavenging reactive oxygen species (ROS) and inhibiting secretion of proinflammatory cytokines. In vivo imaging demonstrated that the improved immune-escape properties of the nanocomplexes resulted in markedly increased half-life of circulation and levels of accumulated drugs at arthritic sites of adjuvant-induced arthritis (AIA) rats. Notably, the nanocomplexes significantly suppressed joint inflammation and protected against bone destruction of AIA rats by inhibiting inflammatory cytokine secretion of the synovial macrophages and FLSs. These results indicate that the nanocomplexes provide an excellent carrier for controlled release and targeted accumulation of SIN within the arthritic sites, which consequently achieve disease-remitting effects of SIN on RA.

Lactate metabolism in rheumatoid arthritis: Pathogenic mechanisms and therapeutic intervention with natural compounds.

BACKGROUND: Rheumatoid arthritis (RA) is a common chronic and systemic autoimmune disease characterized by persistent inflammation and hyperplasia of the synovial membrane, the degradation of cartilage, and the erosion of bones in diarthrodial joints. The inflamed joints of patients with RA have been recognized to be a site of hypoxic microenvironment which results in an imbalance of lactate metabolism and the accumulation of lactate. Lactate is no longer considered solely a metabolic waste product of glycolysis, but also a combustion aid in the progression of RA from the early stages of inflammation to the late stages of bone destruction. PURPOSE: To review the pathogenic mechanisms of lactate metabolism in RA and investigate the potential of natural compounds for treating RA linked to the regulation of imbalance in lactate metabolism. METHODS: Research advances in our understanding of lactate metabolism in the pathogenesis of RA and novel pharmacological approaches of natural compounds by targeting lactate metabolic signaling were comprehensively reviewed and deeply discussed. RESULTS: Lactate produced by RA synovial fibroblasts (RASFs) acts on targeted cells such as T cells, macrophages, dendritic cells and osteoclasts, and affects their differentiation, activation and function to accelerate the development of RA. Many natural compounds show therapeutic potential for RA by regulating glycolytic rate-limiting enzymes to limit lactate production, and affecting monocarboxylate transporter and acetyl-CoA carboxylase to inhibit lactate transport and conversion. CONCLUSION: Regulation of imbalance in lactate metabolism offers novel therapeutic approaches for RA, and natural compounds capable of targeting lactate metabolic signaling constitute potential candidates for development of drugs RA.

A pH-Driven indomethacin-loaded nanomedicine for effective rheumatoid arthritis therapy by combining with photothermal therapy.

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterised by inflammatory micro-environments in the joints. Indomethacin (IND), a conventional nonsteroidal anti-inflammatory drug (NSAID), has been used for the therapy of RA. However, the poor solubility and serious side effects of oral administration of IND significantly limit its efficacy. In this study, we have synthesized biomimetic IND-loaded Prussian blue (PB) nanoparticles (IND@PB@M@HA) with hyaluronic acid (HA) modification for increasing the solubility and targeting the ability of IND to the inflamed joints. The application of hybrid cell membranes on the NPs endowed immune escape of IND@PB@M@HA NPs, which accordingly extended the circulation time in the blood. In vitro assay demonstrated that the combination of nanomedicine and photothermal therapy produced a powerful anti-inflammatory effect by reducing the levels of inflammatory factors and cell viability of activated macrophages and NPs possessed obvious pH-responsiveness. In vivo assay demonstrated that the nanomedicine for synergistic photothermal therapy exhibited desirable pharmacodynamics and pharmacokinetic properties at ultra-low drug dosage in a rat model of adjuvant-induced arthritis, which was confirmed by inflammatory suppression, bone erosion remission, and negligible adverse effects. In summary, the proposed nanomedicine has the potential role for targeted anti-inflammatory therapy of RA.