Beyond smoothness: A general optimization framework for graph neural networks with negative Laplacian regularization.

Graph Neural Networks (GNNs) have drawn great attention in handling graph-structured data. To characterize the message-passing mechanism of GNNs, recent studies have established a unified framework that models the graph convolution operation as a graph signal denoising problem. While increasing interpretability, this framework often performs poorly on heterophilic graphs and also leads to shallow and fragile GNNs in practice. The key reason is that it encourages feature smoothness, but ignores the high-frequency information of node features. To address this issue, we propose a general framework for GNNs via relaxation of the smoothness regularization. In particular, it employs an information aggregation mechanism to learn the low- and high-frequency components adaptively from data, offering more flexible graph convolution operators compared to the smoothness-promoted framework. Theoretical analyses demonstrate that our framework can capture both low- and high-frequency information of node features, effectively. Experiments on nine benchmark datasets show that our framework achieves the state-of-the-art performance in most cases. Furthermore, it can be used to handle deep models and adversarial attacks.

Deciphering the shared mechanisms of Gegen Qinlian Decoction in treating type 2 diabetes and ulcerative colitis via bioinformatics and machine learning.

Background: Although previous clinical studies and animal experiments have demonstrated the efficacy of Gegen Qinlian Decoction (GQD) in treating Type 2 Diabetes Mellitus (T2DM) and Ulcerative Colitis (UC), the underlying mechanisms of its therapeutic effects remain elusive. Purpose: This study aims to investigate the shared pathogenic mechanisms between T2DM and UC and elucidate the mechanisms through which GQD modulates these diseases using bioinformatics approaches. Methods: Data for this study were sourced from the Gene Expression Omnibus (GEO) database. Targets of GQD were identified using PharmMapper and SwissTargetPrediction, while targets associated with T2DM and UC were compiled from the DrugBank, GeneCards, Therapeutic Target Database (TTD), DisGeNET databases, and differentially expressed genes (DEGs). Our analysis encompassed six approaches: weighted gene co-expression network analysis (WGCNA), immune infiltration analysis, single-cell sequencing analysis, machine learning, DEG analysis, and network pharmacology. Results: Through GO and KEGG analysis of weighted gene co-expression network analysis (WGCNA) modular genes and DEGs intersection, we found that the co-morbidity between T2DM and UC is primarily associated with immune-inflammatory pathways, including IL-17, TNF, chemokine, and toll-like receptor signaling pathways. Immune infiltration analysis supported these findings. Three distinct machine learning studies identified IGFBP3 as a biomarker for GQD in treating T2DM, while BACE2, EPHB4, and EPHA2 emerged as biomarkers for GQD in UC treatment. Network pharmacology revealed that GQD treatment for T2DM and UC mainly targets immune-inflammatory pathways like Toll-like receptor, IL-17, TNF, MAPK, and PI3K-Akt signaling pathways. Conclusion: This study provides insights into the shared pathogenesis of T2DM and UC and clarifies the regulatory mechanisms of GQD on these conditions. It also proposes novel targets and therapeutic strategies for individuals suffering from T2DM and UC.

Underlying mechanisms of traditional Chinese medicine in the prevention and treatment of diabetic retinopathy: Evidences from molecular and clinical studies.

As one of the most serious microvascular complications of diabetes mellitus (DM), diabetic retinopathy (DR) can cause visual impairment and even blindness. With the rapid increase in the prevalence of DM, the incidence of DR is also rising year by year. Preventing and effectively treating DR has become a major focus in the medical field. Traditional Chinese medicine (TCM) has a wealth of experience in treating DR and has achieved significant results with various herbs and TCM prescriptions. Traditional Chinese Medicine (TCM) provides a comprehensive therapeutic strategy for diabetic retinopathy (DR), encompassing anti-inflammatory and antioxidant actions, anti-neovascularization, neuroprotection, regulation of glucose metabolism, and inhibition of apoptosis. This review provides an overview of the current status of TCM treatment for DR in recent years, including experimental studies and clinical researches, to explore the clinical efficacy and the underlying modern mechanisms of herbs and TCM prescriptions. Besides, we also discussed the challenges TCM faces in treating DR, such as drug-drug interactions among TCM components and the lack of high-quality evidence-based medicine practice, which pose significant obstacles to TCM's application in DR.

The research trends of ferroptosis in diabetes: a bibliometric analysis.

Objective: Exploring the mechanism of ferroptosis as a potential avenue for investigating the pathogenesis and therapeutic outlook of diabetes mellitus and its complications has emerged as a focal point within recent years. Herein, we employ a bibliometric approach to delineate the current landscape of ferroptosis research in the context of diabetes mellitus. Our objective is to furnish insights and scholarly references conducive to the advancement of comprehensive investigations and innovations in related domains. Methods: We included studies on ferroptosis in diabetes, obtained from the Web of Science Core Collection. All publications were transported in plaintext full-record format and were analyzed by CiteSpace 6.2.R4 for bibliometric analysis. Results: Four hundred and forty-eight records that met the criteria were included. The publications released during the initial 3 years were relatively small, while there was a sudden surge of publications published in 2022 and 2023. Representing 41 countries and 173 institutions, China and Wuhan University led the research on ferroptosis in diabetes. The author with the highest number of published papers is Zhongming Wu, while Dixon SJ is the most frequently cited author. The journal with the highest number of co-citations is Cell. The most common keywords include oxidative stress, cell death, lipid peroxidation, and metabolism. Extracted keywords predominantly focus on NLRP3 inflammatory, diabetic kidney disease, mitochondria, iron overload, and cardiomyopathy. Conclusion: The escalating recognition of ferroptosis as a potential therapeutic target for deciphering the intricate mechanisms underlying diabetes and its complications is underscored by a noteworthy surge in relevant research publications. This surge has catapulted ferroptosis into the spotlight as a burgeoning and vibrant research focus within the field.

Exploring the molecular mechanism of Xuebifang in the treatment of diabetic peripheral neuropathy based on bioinformatics and network pharmacology.

Background: Xuebifang (XBF), a potent Chinese herbal formula, has been employed in managing diabetic peripheral neuropathy (DPN). Nevertheless, the precise mechanism of its action remains enigmatic. Purpose: The primary objective of this investigation is to employ a bioinformatics-driven approach combined with network pharmacology to comprehensively explore the therapeutic mechanism of XBF in the context of DPN. Study design and Methods: The active chemicals and their respective targets of XBF were sourced from the TCMSP and BATMAN databases. Differentially expressed genes (DEGs) related to DPN were obtained from the GEO database. The targets associated with DPN were compiled from the OMIM, GeneCards, and DrugBank databases. The analysis of GO, KEGG pathway enrichment, as well as immuno-infiltration analysis, was conducted using the R language. The investigation focused on the distribution of therapeutic targets of XBF within human organs or cells. Subsequently, molecular docking was employed to evaluate the interactions between potential targets and active compounds of XBF concerning the treatment of DPN. Results: The study successfully identified a total of 122 active compounds and 272 targets associated with XBF. 5 core targets of XBF for DPN were discovered by building PPI network. According to GO and KEGG pathway enrichment analysis, the mechanisms of XBF for DPN could be related to inflammation, immune regulation, and pivotal signalling pathways such as the TNF, TLR, CLR, and NOD-like receptor signalling pathways. These findings were further supported by immune infiltration analysis and localization of immune organs and cells. Moreover, the molecular docking simulations demonstrated a strong binding affinity between the active chemicals and the carefully selected targets. Conclusion: In summary, this study proposes a novel treatment model for XBF in DPN, and it also offers a new perspective for exploring the principles of traditional Chinese medicine (TCM) in the clinical management of DPN.