Conectar
To evaluate the efficacy of one-step acellular dermis combined with autologous split thickness skin grafting in the treatment of burn or trauma wounds by a multicenter controlled study. In patients with extensive burns, it is even difficult to repair the wounds due to the shortage of autologous skin. The traditional skin grafting method has the disadvantages of large damage to the donor site, insufficient skin source and unsatisfactory appearance, wear resistance and elasticity of the wound tissue after skin grafting. One-step acellular dermis combined with autologous ultra-thin split thickness skin graft can achieve better healing effect in the treatment of burn and trauma wounds. A total of 1208 patients who underwent single-layer skin grafting and one-step composite skin grafting in the First Affiliated Hospital of Wannan Medical College, Wuhan Third People's Hospital and Lu ‘an People's Hospital from 2019 to 2022 were retrospectively analysed. The total hospitalization cost, total operation cost, hospitalization days after surgery, wound healing rate after 1 week of skin grafting and scar follow-up at 6 months after discharge were compared and studied. The total cost of hospitalization and operation in the composite skin grafting group was significantly higher than those in the single-layer autologous skin grafting group. The wound healing rate after 1 week of skin grafting and the VSS score of scar in the follow-up of 6 months after discharge were better than those in the single-layer skin grafting group. One-step acellular dermis combined with autologous ultra-thin split thickness skin graft has high wound healing rate, less scar, smooth appearance and good elasticity in repairing burn and trauma wounds, which can provide an ideal repair method for wounds.
Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA-seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non-negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription-polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single-cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P-Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS.
FreshRSS 