Bioinformatics analysis and verification of m6A related genes based on the construction of keloid diagnostic model.

Keloids are formed due to abnormal hyperplasia of the skin connective tissue. We explored the relationship between N6-methyladenosine (m6A)-related genes and keloids. The transcriptomic datasets (GSE44270 and GSE185309) of keloid and normal skin tissues samples were obtained from the Gene Expression Omnibus database. We constructed the m6A landscape and verified the corresponding genes using immunohistochemistry. We extracted hub genes for unsupervised clustering analysis using protein-protein interaction (PPI) network; gene ontology enrichment analysis was performed to determine the biological processes or functions affected by the differentially expressed genes (DEGs). We performed immune infiltration analysis to determine the relationship between keloids and the immune microenvironment using single-sample gene set enrichment analysis and CIBERSORT. Differential expression of several m6A genes was observed between the two groups; insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) was significantly upregulated in keloid patients. PPI analysis elucidated six genes with significant differences between the two keloid sample groups. Enrichment analysis revealed that the DEGs were mainly enriched in cell division, proliferation, and metabolism. Moreover, significant differences in immunity-related pathways were observed. Therefore, the results of this study will provide a reference for the elucidation of the pathogenesis and therapeutic targets of keloids.

Aggregation-Induced Emission-Based Macrophage-Like Nanoparticles for Targeted Photothermal Therapy and Virus Transmission Blockage in Monkeypox.

The recent prevalence of monkeypox has led to the declaration of a Public Health Emergency of International Concern. Monkeypox lesions are typically ulcers or pustules (containing high titers of replication-competent virus) in the skin and mucous membranes, which allow monkeypox virus to transmit predominantly through intimate contact. Currently, effective clinical treatments for monkeypox are lacking, and strategies for blocking virus transmission are fraught with drawbacks. Herein, this work constructs a biomimetic nanotemplate (termed TBD@M NPs) with macrophage membranes as the coat and polymeric nanoparticles loading a versatile aggregation-induced emission featured photothermal molecule TPE-BT-DPTQ as the core. In a surrogate mouse model of monkeypox (vaccinia-virus-infected tail scarification model), intravenously injected TBD@M NPs show precise tracking and near-infrared region II fluorescence imaging of the lesions. Upon 808 nm laser irradiation, the virus is eliminated by the photothermal effect and the infected wound heals rapidly. More importantly, the inoculation of treated lesion tissue suspensions does not trigger tail infection or inflammatory activation in healthy mice, indicating successful blockage of virus transmission. This study demonstrates for the first time monkeypox theranostics using nanomedicine, and may bring a new insight into the development of a viable strategy for monkeypox management in clinical trials.

The functions and clinical application potential of exosomes derived from mesenchymal stem cells on wound repair: a review of recent research advances.

Wound repair is a complex problem for both clinical practitioners and scientific investigators. Conventional approaches to wound repair have been associated with several limitations, including prolonged treatment duration, high treatment expenses, and significant economic and psychological strain on patients. Consequently, there is a pressing demand for more efficacious and secure treatment modalities to enhance the existing treatment landscapes. In the field of wound repair, cell-free therapy, particularly the use of mesenchymal stem cell-derived exosomes (MSC-Exos), has made notable advancements in recent years. Exosomes, which are small lipid bilayer vesicles discharged by MSCs, harbor bioactive constituents such as proteins, lipids, microRNA (miRNA), and messenger RNA (mRNA). These constituents facilitate material transfer and information exchange between the cells, thereby regulating their biological functions. This article presents a comprehensive survey of the function and mechanisms of MSC-Exos in the context of wound healing, emphasizing their beneficial impact on each phase of the process, including the regulation of the immune response, inhibition of inflammation, promotion of angiogenesis, advancement of cell proliferation and migration, and reduction of scar formation.

Therapeutic potential of exosomes from adipose-derived stem cells in chronic wound healing.

Chronic wound healing remains a challenging medical problem affecting society, which urgently requires anatomical and functional solutions. Adipose-derived stem cells (ADSCs), mesenchymal stem cells with self-renewal and multiple differentiation ability, play essential roles in wound healing and tissue regeneration. The exosomes from ADSCs (ADSC-EXOs) are extracellular vesicles that are essential for communication between cells. ADSC-EXOs release various bioactive molecules and subsequently restore tissue homeostasis and accelerate wound healing, by promoting various stages of wound repair, including regulating the inflammatory response, promoting wound angiogenesis, accelerating cell proliferation, and modulating wound remodeling. Compared with ADSCs, ADSC-EXOs have the advantages of avoiding ethical issues, being easily stored, and having high stability. In this review, a literature search of PubMed, Medline, and Google Scholar was performed for articles before August 1, 2022 focusing on exosomes from ADSCs, chronic wound repair, and therapeutic potential. This review aimed to provide new therapeutic strategies to help investigators explore how ADSC-EXOs regulate intercellular communication in chronic wounds.

A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata.

Introduction: Skin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones. Methods: A deep neural network was developed with two encoders for extracting information from image data and metadata. A multimodal fusion module with intra-modality self-attention and inter-modality cross-attention was proposed to effectively combine image features and meta features. The model was trained on tested on a public dataset and compared with other state-of-the-art methods using five-fold cross-validation. Results: Including metadata is shown to significantly improve a model's performance. Our model outperformed other metadata fusion methods in terms of accuracy, balanced accuracy and area under the receiver-operating characteristic curve, with an averaged value of 0.768±0.022, 0.775±0.022 and 0.947±0.007. Conclusion: A deep learning model using smartphone collected images and metadata for skin lesion diagnosis was successfully developed. The proposed model showed promising performance and could be a potential tool for skin cancer screening.