LncRNAs associated with oxidative stress in diabetic wound healing: Regulatory mechanisms and application prospects

Role of long noncoding RNAs in diabetes-associated peripheral arterial disease

Diabetes mellitus (DM) is a metabolic disease that heightens the risks of many vascular complications, including peripheral arterial disease (PAD). Various types of cells, including but not limited to endothelial cells (ECs), vascular smooth muscle cells (VSMCs), and macrophages (MΦs), play crucial roles in the pathogenesis of DM-PAD. Long non-coding RNAs (lncRNAs) are epigenetic regulators that play important roles in cellular function, and their dysregulation in DM can contribute to PAD. This review focuses on the developing field of lncRNAs and their emerging roles in linking DM and PAD. We review the studies investigating the role of lncRNAs in crucial cellular processes contributing to DM-PAD, including those in ECs, VSMCs, and MΦ. By examining the intricate molecular landscape governed by lncRNAs in these relevant cell types, we hope to shed light on the roles of lncRNAs in EC dysfunction, inflammatory responses, and vascular remodeling contributing to DM-PAD. Additionally, we provide an overview of the research approach and methodologies, from identifying disease-relevant lncRNAs to characterizing their molecular and cellular functions in the context of DM-PAD. We also discuss the potential of leveraging lncRNAs in the diagnosis and therapeutics for DM-PAD. Collectively, this review provides a summary of lncRNA-regulated cell functions contributing to DM-PAD and highlights the translational potential of leveraging lncRNA biology to tackle this increasingly prevalent and complex disease.

Bletilla striata Polysaccharide-/Chitosan-Based Self-Healing Hydrogel with Enhanced Photothermal Effect for Rapid Healing of Diabetic Infected Wounds via the Regulation of Microenvironment

The management of diabetic ulcers poses a significant challenge worldwide, and persistent hyperglycemia makes patients susceptible to bacterial infections. Unfortunately, the overuse of antibiotics may lead to drug resistance and prolonged infections, contributing to chronic inflammation and hindering the healing process. To address these issues, a photothermal therapy technique was incorporated in the preparation of wound dressings. This innovative solution involved the formulation of a self-healing and injectable hydrogel matrix based on the Schiff base structure formed between the oxidized Bletilla striata polysaccharide (BSP) and hydroxypropyltrimethylammonium chloride chitosan. Furthermore, the introduction of CuO nanoparticles encapsulated in polydopamine imparted excellent photothermal properties to the hydrogel, which promoted the release of berberine (BER) loaded on the nanoparticles and boosted the antibacterial performance. In addition to providing a reliable physical protection to the wound, the developed hydrogel, which integrated the herbal components of BSP and BER, effectively accelerated wound closure via microenvironment regulation, including alleviated inflammatory reaction, stimulated re-epithelialization, and reduced oxidative stress based on the promising results from cell and animal experiments. These impressive outcomes highlighted their clinical potential in safeguarding the wound against bacterial intrusion and managing diabetic ulcers.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats

Introduction

With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses.

Method

In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model.

Results and discussion

Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.

Stem Cell-Derived Extracellular Vesicles: Promising Therapeutic Opportunities for Diabetic Wound Healing

Wound healing is a sophisticated and orderly process of cellular interactions in which the body restores tissue architecture and functionality following injury. Healing of chronic diabetic wounds is difficult due to impaired blood circulation, a reduced immune response, and disrupted cellular repair mechanisms, which are often associated with diabetes. Stem cell-derived extracellular vesicles (SC-EVs) hold the regenerative potential, encapsulating a diverse cargo of proteins, RNAs, and cytokines, presenting a safe, bioactivity, and less ethical issues than other treatments. SC-EVs orchestrate multiple regenerative processes by modulating cellular communication, increasing angiogenesis, and promoting the recruitment and differentiation of progenitor cells, thereby potentiating the reparative milieu for diabetic wound healing. Therefore, this review investigated the effects and mechanisms of EVs from various stem cells in diabetic wound healing, as well as their limitations and challenges. Continued exploration of SC-EVs has the potential to revolutionize diabetic wound care.

Gelatin-based dynamic response antioxidant, anti-inflammatory multifunctional hydrogel for enhanced diabetic wound repair

Diabetic wound therapy presents significant challenges in the clinical environment, where persistent bleeding, disturbed inflammatory regulation, impaired cellular proliferation, and impaired tissue remodeling are major features of diabetic wound healing. However, current treatment strategies need to be considered in the context of the dynamic and complex needs of chronic wound healing. Here, multifunctional dynamic boronic acid cross-linked hydrogels were prepared by the reaction of gelatin (Gel) inoculated with 5-carboxy 3-nitrophenylboronic acid (NPBA) and Epigallocatechin gallate (EGCG) to achieve rapid gelation at pH = 7.4, EGCG could interact electrostatically with cationic antimicrobial peptides (AMP) to achieve the effective loading of AMP in the hydrogels. This hydrogel can be injected and adhered to skin defects in diabetic patients to provide a barrier and rapid hemostasis. In a high glucose microenvironment, the rapid release of AMP effectively kills bacteria, while the responsive release of EGCG eliminates reactive oxygen species (ROS) and promotes macrophage M2 polarization. In addition, the hydrogel had excellent biocompatibility and degradability properties, degraded completely after 3 days of subcutaneous injection, and was non-toxic in H&E staining of major organs and serum liver function indices in mice. This multifunctional injectable hydrogel accelerates diabetic skin wound repair and is a promising dressing for the precise treatment of diabetic wounds.

Multifunctional hydrogel bioscaffolds based on polysaccharide to promote wound healing: A review

Various types of skin wounds pose challenges in terms of healing and susceptibility to infection, which can have a significant impact on physical and mental well-being, and in severe cases, may result in amputation. Conventional wound dressings often fail to provide optimal support for these wounds, thereby impeding the healing process. As a result, there has been considerable interest in the development of multifunctional polymer matrix hydrogel scaffolds for wound healing. This review offers a comprehensive review of the characteristics of polysaccharide-based hydrogel scaffolds, as well as their applications in different types of wounds. Additionally, it evaluates the advantages and disadvantages associated with various types of multifunctional polymer and polysaccharide-based hydrogel scaffolds. The objective is to provide a theoretical foundation for the utilization of multifunctional hydrogel scaffolds in promoting wound healing.

Recent advances of hydrogels as smart dressings for diabetic wounds

Chronic diabetic wounds have been an urgent clinical problem, and wound dressings play an important role in their management. Due to the design of traditional dressings, it is difficult to achieve adaptive adhesion and on-demand removal of complex diabetic wounds, real-time monitoring of wound status, and dynamic adjustment of drug release behavior according to the wound microenvironment. Smart hydrogels, as smart dressings, can respond to environmental stimuli and achieve more precise local treatment. Here, we review the latest progress of smart hydrogels in wound bandaging, dynamic monitoring, and drug delivery for treatment of diabetic wounds. It is worth noting that we have summarized the most important properties of smart hydrogels for diabetic wound healing. In addition, we discuss the unresolved challenges and future prospects in this field. We hope that this review will contribute to furthering progress on smart hydrogels as improved dressing for diabetic wound healing and practical clinical application.

EVLncRNAs 3.0: an updated comprehensive database for manually curated functional long non-coding RNAs validated by low-throughput experiments

Long noncoding RNAs (lncRNAs) have emerged as crucial regulators across diverse biological processes and diseases. While high-throughput sequencing has enabled lncRNA discovery, functional characterization remains limited. The EVLncRNAs database is the first and exclusive repository for all experimentally validated functional lncRNAs from various species. After previous releases in 2018 and 2021, this update marks a major expansion through exhaustive manual curation of nearly 25 000 publications from 15 May 2020, to 15 May 2023. It incorporates substantial growth across all categories: a 154% increase in functional lncRNAs, 160% in associated diseases, 186% in lncRNA-disease associations, 235% in interactions, 138% in structures, 234% in circular RNAs, 235% in resistant lncRNAs and 4724% in exosomal lncRNAs. More importantly, it incorporated additional information include functional classifications, detailed interaction pathways, homologous lncRNAs, lncRNA locations, COVID-19, phase-separation and organoid-related lncRNAs. The web interface was substantially improved for browsing, visualization, and searching. ChatGPT was tested for information extraction and functional overview with its limitation noted. EVLncRNAs 3.0 represents the most extensive curated resource of experimentally validated functional lncRNAs and will serve as an indispensable platform for unravelling emerging lncRNA functions. The updated database is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs3/.