Optical coherence tomography (OCT) and OCT angiography (OCTA) techniques offer numerous advantages in clinical skin applications but the field of view (FOV) of current commercial systems are relatively limited to cover the entire skin lesion.

. Significance: In order to elucidate therapeutic treatment to accelerate wound healing, it is crucial to understand the process underlying skin wound healing, especially re-epithelialization. Epidermis and scab detection is of importance in the wound healing process as their thickness is a vital indicator to judge whether the re-epithelialization process is normal or not. Since optical coherence tomography (OCT) is a real-time and non-invasive imaging technique that can perform a cross-sectional evaluation of tissue microstructure, it is an ideal imaging modality to monitor the thickness change of epidermal and scab tissues during wound healing processes in micron-level resolution. Traditional segmentation on epidermal and scab regions was performed manually, which is time-consuming and impractical in real time. Aim: We aim to develop a deep-learning-based skin layer segmentation method for automated quantitative assessment of the thickness of in vivo epidermis and scab tissues during a time course of healing within a rodent model. Approach: Five convolution neural networks were trained using manually labeled epidermis and scab regions segmentation from 1000 OCT B-scan images (assisted by its corresponding angiographic information). The segmentation performance of five segmentation architectures was compared qualitatively and quantitatively for validation set. Results: Our results show higher accuracy and higher speed of the calculated thickness compared with human experts. The U-Net architecture represents a better performance than other deep neural network architectures with 0.894 at -score, 0.875 at mean intersection over union, 0.933 at Dice similarity coefficient, and at an average symmetric surface distance. Furthermore, our algorithm is able to provide abundant quantitative parameters of the wound based on its corresponding thickness maps in different healing phases. Among them, normalized epidermal thickness is recommended as an essential hallmark to describe the re-epithelialization process of the rodent model. Conclusions: The automatic segmentation and thickness measurements within different phases of wound healing data demonstrates that our pipeline provides a robust, quantitative, and accurate method for serving as a standard model for further research into effect of external pharmacological and physical factors.

Significance: Morphological changes in the epidermis layer are critical for the diagnosis and assessment of various skin diseases. Due to its noninvasiveness, optical coherence tomography (OCT) is a good candidate for observing microstructural changes in skin. Convolutional neural network (CNN) has been successfully used for automated segmentation of the skin layers of OCT images to provide an objective evaluation of skin disorders. Such method is reliable, provided that a large amount of labeled data is available, which is very time-consuming and tedious. The scarcity of patient data also puts another layer of difficulty to make the model more generalizable.Aim: We developed a semisupervised representation learning method to provide data augmentations.Approach: We used rodent models to train neural networks for accurate segmentation of clinical data. Result:The learning quality is maintained with only one OCT labeled image per volume that is acquired from patients. Data augmentation introduces a semantically meaningful variance, allowing for better generalization. Our experiments demonstrate the proposed method can achieve accurate segmentation and thickness measurement of the epidermis.Conclusion: This is the first report of semisupervised representative learning applied to OCT images from clinical data by making full use of the data acquired from rodent models. The proposed method promises to aid in the clinical assessment and treatment planning of skin diseases.

Viscoelastic characterization of the tissue-engineered corneal stromal model is important for our understanding of the cell behaviors in the pathophysiologic altered corneal extracellular matrix (ECM). The effects of the interactions between stromal cells and different ECM characteristics on the viscoelastic properties during an 11-day culture period were explored. Collagen-based hydrogels seeded with keratocytes were used to replicate human corneal stroma. Keratocytes were seeded at 8 Â 10 3 cells per hydrogel and with collagen concentrations of 3, 5 and 7 mg/ml. Air-pulse-based surface acoustic wave optical coherence elastography (SAW-OCE) was employed to monitor the changes in the hydrogels' dimensions and viscoelasticity over the culture period. The results showed the elastic modulus increased by 111%, 56% and 6%, and viscosity increased by 357%, 210% and 25% in the 3, 5 and 7 mg/ml hydrogels, respectively. To explain the SAW-OCE results, scanning electron microscope was also performed. The results confirmed the increase in elastic modulus and viscosity of the hydrogels, respectively, arose from increased fiber density and force-dependent unbinding of bonds between collagen fibers. This study reveals the influence of cell-matrix interactions on the viscoelastic properties of corneal stromal models and can provide quantitative guidance for mechanobiological investigations which require collagen ECM with tuneable viscoelastic properties.

Cutaneous wound healing typically results in scarring; however, chronic wounds (CWs) represent a global and escalating health burden causing substantial morbidity and mortality. Estimated to cost Medicare up to $96.8 billion pa and with a profound paucity of effective therapeutics, novel interventions to improve healing are urgently needed. In this study, we assess the impact of manipulating the melanocortin 1 receptor (MC1R) on acute wound healing using a selective agonist, BMS-470539 (MC1R-Ag). MC1R agonism resulted in accelerated wound closure and reepithelialisation in wildtype but not MC1Re/e mice, which harbour a non-functional receptor. MC1R-Ag improved wound perfusion and lymphatic drainage by promoting angiogenesis and lymphangiogenesis, reducing local oxidative stress and inflammation with a knock-on effect of reduced scarring. To assess whether manipulating MC1R would be of benefit in pathological healing, we developed a novel murine model of chronic cutaneous wounds. By combining advanced age and locally elevated oxidative stress, factors shown to be present in most human CWs regardless of their category, resultant wounds expand 5-fold into the surrounding tissue, produce exudate and generate slough. Histological comparisons to human CWs demonstrate robust recapitulation of the hallmarks of human disease, including hyperproliferative epidermis, fibrinous exudate and vasculitis. Crucially, our model facilitates the in vivo study of candidate therapies to rescue derailed healing responses. We have identified that abrogation of MC1R signalling, using MC1Re/e mice, exacerbates CWs with enhanced exudate and NETosis. In contrast, topical administration of an MC1R agonist following ulcer debridement rescues the healing response, highlighting MC1R agonism as a candidate therapeutic approach for human CWs. We anticipate that our unique model will become a valuable tool to elucidate mechanisms of ulcer development and persistence.