Silab (France)

In vitro tissue-engineered skin models are valuable tools for dermatological research. Yet, they often fail to reproduce the complex interactions among vascular, immune, and cutaneous cells during the wound healing process. In addition, inter-individual variability response remains poorly understood, limiting our knowledge of the genetic and environmental factors that influence wound healing. This study presents a 3D autologous, vascularised, and immunocompetent Tissue Engineered Skin model (aviTES), generated using cells from the same donor to study the wound healing process. The aviTES models were injured using a 2 mm punch biopsy, and wound closure was macroscopically monitored for 7 days in the absence of any stimuli. The re-epithelialisation rate was highly reproducible within the same donor. However, wound closure differed between healthy donors (five distinct donors), highlighting individual variability. Indeed, in most cases, complete re-epithelialisation was observed within 2-4 days, but one did not close completely after 7 days. Immunofluorescence analysis revealed lymphocytes (CD45+/CD3+) migration for all donors, but no migration of CD206-positive cells or neo-angiogenesis into the wound sites. By contrast, platelet lysate treatment promoted epidermal cell migration, capillary organisation/neo-angiogenesis, and altered collagen and MMP secretion at levels that were specific to each donor, highlighting donor-specific responses to treatment. This innovative autologous skin model reproduced several key features of the human wound healing process and may represent a new tool for studying wound healing process and inter-individual variability, paving the way for advances in personalised medicine.

BackgroundLine‐field confocal optical coherence tomography (LC‐OCT) is an imaging technique providing non‐invasive “optical biopsies” with an isotropic spatial resolution of ∼1 μm and deep penetration until the dermis. Analysis of obtained images is classically performed by experts, thus requiring long and fastidious training and giving operator‐dependent results. In this study, the objective was to develop a new automated method to score the quality of the dermal matrix precisely, quickly, and directly from in vivo LC‐OCT images. Once validated, this new automated method was applied to assess photo‐aging‐related changes in the quality of the dermal matrix.Materials and methodsLC‐OCT measurements were conducted on the face of 57 healthy Caucasian volunteers. The quality of the dermal matrix was scored by experts trained to evaluate the fibers’ state according to four grades. In parallel, these images were used to develop the deep learning model by adapting a MobileNetv3‐Small architecture. Once validated, this model was applied to the study of dermal matrix changes on a panel of 36 healthy Caucasian females, divided into three groups according to their age and photo‐exposition.ResultsThe deep learning model was trained and tested on a set of 15 993 images. Calculated on the test data set, the accuracy score was 0.83. As expected, when applied to different volunteer groups, the model shows greater and deeper alteration of the dermal matrix for old and photoexposed subjects.ConclusionsIn conclusion, we have developed a new method that automatically scores the quality of the dermal matrix on in vivo LC‐OCT images. This accurate model could be used for further investigations, both in the dermatological and cosmetic fields.