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The peripheral nerves (PNs) innervate the dermis and epidermis, and are suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and the noise/background associated with the immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, Denoising Convolutional Neural Network (DnCNN), to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8 mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3, 7, 10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly, we found a positive correlation (R2 = 0.926) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.

Many cell types migrate in response to naturally generated electric fields. Furthermore, it has been suggested that the external application of an electric field may be used to intervene in and optimize natural processes such as wound healing. Precise cell guidance suitable for such optimization may rely on predictive models of cell migration, which do not generalize. Here, we present a machine learning model that can forecast directedness of cell migration given a timeseries of previous directedness and electric field values. This model is trained using time series galvanotaxis data of mammalian cranial neural crest cells obtained through time-lapse microscopy of cells cultured at 37 °C in a galvanotaxis chamber at ambient pressure. Next, we show that our modeling approach can be used for a variety of cell types and experimental conditions with very limited training data using transfer learning methods. We adapt the model to predict cell behavior for keratocytes (room temperature, ~ 18-20 °C) and keratinocytes (37 °C) under similar experimental conditions with a small dataset (~ 2-5 cells). Finally, this model can be used to perform in silico studies by simulating cell migration lines under time-varying and unseen electric fields. We demonstrate this by simulating feedback control on cell migration using a proportional-integral-derivative (PID) controller. This data-driven approach provides predictive models of cell migration that may be suitable for designing electric field based cellular control mechanisms for applications in precision medicine such as wound healing.

During neurulation, cranial neural crest cells (CNCCs) migrate long distances from the neural tube to their terminal site of differentiation. The pathway traveled by the CNCCs defines the blueprint for craniofacial construction, abnormalities of which contribute to three-quarters of human birth defects. Biophysical cues like naturally occurring electric fields (EFs) have been proposed to be one of the guiding mechanisms for CNCC migration from the neural tube to identified position in the branchial arches. Such endogenous EFs can be mimicked by applied EFs of physiological strength that has been reported to guide the migration of amphibian and avian neural crest cells (NCCs), namely galvanotaxis or electrotaxis. However, the behavior of mammalian NCCs in external EFs has not been reported. We show here that mammalian CNCCs migrate towards the anode in direct current (dc) EFs. Reversal of the field polarity reverses the directedness. The response threshold was below 30 ​mV/mm and the migration directedness and displacement speed increased with increase in field strength. Both CNCC line (O9-1) and primary mouse CNCCs show similar galvanotaxis behavior. Our results demonstrate for the first time that the mammalian CNCCs respond to physiological EFs by robust directional migration towards the anode in a voltage-dependent manner.

This review provides a comprehensive overview on the biomedical applications of electrical stimulation (EStim). EStim has a wide range of direct effects on both biomolecules and cells. These effects have been exploited to facilitate proliferation and functional development of engineered tissue constructs for regenerative medicine applications. They have also been tested or used in clinics for pain mitigation, muscle rehabilitation, the treatment of motor/consciousness disorders, wound healing, and drug delivery. However, the research on fundamental mechanism of cellular response to EStim has fell behind its applications, which has hindered the full exploitation of the clinical potential of EStim. Moreover, despite the positive outcome from the in vitro and animal studies testing the efficacy of EStim, existing clinical trials failed to establish strong, conclusive supports for the therapeutic efficacy of EStim for most of the clinical applications mentioned above. Two potential directions of future research to improve the clinical utility of EStim are presented, including the optimization and standardization of the stimulation protocol and the development of more tissue-matching devices.

In the present study gentamicin was encapsulated within calcium alginate beads and incorporated into porous chitosan, gelatin, double-hybrid silk fibroin, chitosan/gelatin and double-hybrid silk fibroin/chitosan scaffolds. Physiochemical, morphological and biological properties of fabricated amenable model systems were evaluated, revealing hemocompatible nature of double-hybrid silk fibroin/chitosan and double-hybrid silk fibroin scaffolds of hemolysis %<5 and porosity >85%. Fourier transform infrared results confirmed the blend formation and scanning electron microscope images showed good interconnectivity. Double-hybrid silk fibroin/chitosan-blended scaffold shows higher compressive strength and compressive modulus than other fabricated scaffolds. A comparative drug release profile of fabricated scaffolds revealed that double-hybrid silk fibroin/chitosan scaffold is a pertinent model system because of its prolonged drug release, optimal hemocompatability and high compressive modulus. [ABSTRACT FROM AUTHOR]

The peripheral nerves (PNs) innervate the dermis and epidermis, which have been suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and noise/background associated with the Immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, DnCNN, to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3,7,10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly we found a positive correlation (R 2  = 0.933) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.