North Sichuan Medical College

Juvenile idiopathic arthritis (JIA), superseding juvenile rheumatoid arthritis (JRA), is a chronic autoimmune disease affecting children and characterized by various types of childhood arthritis. JIA manifests clinically with joint inflammation, swelling, pain, and limited mobility, potentially leading to long-term joint damage if untreated. This study aimed to identify genes associated with the progression and prognosis of JIA polyarticular to enhance clinical diagnosis and treatment.

We analyzed the gene expression omnibus (GEO) dataset GSE1402 to screen for differentially expressed genes (DEGs) in peripheral blood single nucleated cells (PBMCs) of JIA polyarticular patients. Weighted gene co-expression network analysis (WGCNA) was applied to identify key gene modules, and protein-protein interaction networks (PPIs) were constructed to select hub genes. The random forest model was employed for biomarker gene screening. Functional enrichment analysis was conducted using David's online database, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to annotate and identify potential JIA pathways. Hub genes were validated using the receiver operating characteristic (ROC) curve.

PHLDA1, EGR3, CXCL2, and PF4V1 were identified as significantly associated with the progression and prognosis of JIA polyarticular phenotype, demonstrating high diagnostic and prognostic assessment value.

These genes can be utilized as potential molecular biomarkers, offering valuable insights for the early diagnosis and personalized treatment of JIA polyarticular patients.

The surface characteristics of nanoparticles play a pivotal role in modulating the efficiency and functionality of drug delivery systems, particularly when addressing the complex challenges of targeted therapeutics. This study presents the development of a core-shell nanoparticle system (PMAA@DOX-PVA), incorporating poly(vinyl alcohol) (PVA) as a dynamic shell component to establish dual responsiveness to pH and glutathione levels. The hydrophilic PVA shell is covalently conjugated to the poly (methylacrylic acid) (PMAA) core via a boronic ester bond, establishing a robust platform for controlled release with tunable surface properties. Notably, our findings demonstrate a remarkable enhancement in drug loading efficiency from a modest 8 % (PMAA@DOX) to an impressive 18 % (PMAA@DOX-PVA-0.2). Furthermore, under physiological conditions (pH 7.4), the drug leakage after 62 hours is significantly reduced, dropping from 37 % (PMAA@DOX) to 21 % (PMAA@DOX-PVA-0.2). This suggests a potential improvement in stability during blood circulation. Intriguingly, the PVA ratio was found to influence drug release profiles under different environments distinctly. The possible mechanism was proposed offering insight into this tunable behavior. In vitro cytotoxicity assays on A549 cancer cells reveal that the blank carriers exhibit excellent biocompatibility, while the PVA-coated nanoparticles significantly boost anti-tumor efficacy. Collectively, these results present a promising strategy for designing core-shell nanoparticles with customizable surface properties, paving the way for next-generation, multifunctional drug delivery systems in diverse biomedical applications.

Alzheimer's disease (AD) and cancer, both age-related diseases, are characterized by abnormal cellular behavior. Epidemiological data indicate an inverse relationship between AD and various cancers. Accordingly, this study seeks to analyze the negatively correlated genes between AD and ovarian cancer and identify closely related compounds through virtual screening technology to explore potential therapeutic drugs.

Microarray data were downloaded from the Gene Expression Omnibus database, and negatively correlated genes between AD and ovarian cancer were identified using bioinformatics analysis. Clinical prognostic and survival analyses were performed to identify genes most negatively associated with these diseases. The top ten compounds with the strongest binding to the target genes were screened from the ChemDiv database using virtual screening technology, considering the blood-brain barrier. Molecular dynamics simulations were used to identify potential sites for the binding of these compounds to the target protein MX1. Additionally, point mutation analysis of the target protein was performed. Finally, the binding site was verified in vitro.

The MX1 gene was most significantly negatively associated with AD and ovarian cancer. Molecular dynamics simulations revealed intersection sites at Glu-227 and Gly-188, where MX1 binds tightly to the head compound.

This study successfully identified MX1 as being negatively associated with AD and ovarian cancer and assessed the potential drug compounds that bind most closely to it. Our findings provide important rationale and candidate targets for the development of novel therapeutic strategies for AD and ovarian cancer.