Layered GelMA/PEGDA Hydrogel Microneedle Patch as an Intradermal Delivery System for Hypertrophic Scar Treatment.

Hypertrophic scar (HS) is an unfavorable skin disorder that typically develops after trauma, burn injury, or surgical procedures and causes numerous physical and psychological issues in patients. Currently, intralesional multi-injection of corticosteroid, particularly compound betamethasone (CB), is one of the most prevalent treatments for HS. However, injection administration could result in severe pain and dose-related side effects. Additionally, the vacuum therapeutic efficacy of this treatment relies on the level of expertise of the healthcare professional. To overcome the limitations of conventional injections, a new method that is convenient, painless, and self-administrable is urgently required. In this study, we developed a methacrylate gelatin (GelMA)/polyethylene glycol diacrylate (PEGDA) double-network hydrogel microneedle patch loaded with CB (CB-HMNP) as an intradermal delivery system for HS treatment. The double-network structure conferred the CB-HMNP with sufficient mechanical properties to successfully penetrate scar tissue while also helping to regulate the drug's sustained release rate. Subsequently, we confirmed that the CB-HMNP had a pronounced inhibitory effect on human HS fibroblasts (hHSFs), whereas drug-free HMNPs had no effect on hHSFs, indicating its high biocompatibility. In order to assess the therapeutic efficacy of CB-HMNPs, HS models of New Zealand rabbit ears were developed. The administration of CB-HMNP three times significantly decreased the scar elevation index (SEI), collagen I/III, and transforming growth factor-ß1 (TGF-ß1) protein. Therefore, the CB-HMNP may offer an administration pathway for the treatment of HS that is less painful, more convenient, less invasive, and sustain-released.

Identification of a potential diagnostic signature for postmenopausal osteoporosis via transcriptome analysis.

Purpose: We aimed to establish the transcriptome diagnostic signature of postmenopausal osteoporosis (PMOP) to identify diagnostic biomarkers and score patient risk to prevent and treat PMOP. Methods: Peripheral blood mononuclear cell (PBMC) expression data from PMOP patients were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the "limma" package. The "WGCNA" package was used for a weighted gene co-expression network analysis to identify the gene modules associated with bone mineral density (BMD). Least absolute shrinkage and selection operator (LASSO) regression was used to construct a diagnostic signature, and its predictive ability was verified in the discovery cohort. The diagnostic values of potential biomarkers were evaluated by receiver operating characteristic curve (ROC) and coefficient analysis. Network pharmacology was used to predict the candidate therapeutic molecules. PBMCs from 14 postmenopausal women with normal BMD and 14 with low BMD were collected, and RNA was extracted for RT-qPCR validation. Results: We screened 2420 differentially expressed genes (DEGs) from the pilot cohort, and WGCNA showed that the blue module was most closely related to BMD. Based on the genes in the blue module, we constructed a diagnostic signature with 15 genes, and its ability to predict the risk of osteoporosis was verified in the discovery cohort. RT-qPCR verified the expression of potential biomarkers and showed a strong correlation with BMD. The functional annotation results of the DEGs showed that the diagnostic signature might affect the occurrence and development of PMOP through multiple biological pathways. In addition, 5 candidate molecules related to diagnostic signatures were screened out. Conclusion: Our diagnostic signature can effectively predict the risk of PMOP, with potential application for clinical decisions and drug candidate selection.