A bioelectronic device for electric field treatment of wounds reduces inflammation in an in vivo mouse model.

Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

A pro-reparative bioelectronic device for controlled delivery of ions and biomolecules.

Wound healing is a complex physiological process that requires precise control and modulation of many parameters. Therapeutic ion and biomolecule delivery has the capability to regulate the wound healing process beneficially. However, achieving controlled delivery through a compact device with the ability to deliver multiple therapeutic species can be a challenge. Bioelectronic devices have emerged as a promising approach for therapeutic delivery. Here, we present a pro-reparative bioelectronic device designed to deliver ions and biomolecules for wound healing applications. The device incorporates ion pumps for the targeted delivery of H+ and zolmitriptan to the wound site. In vivo studies using a mouse model further validated the device's potential for modulating the wound environment via H+ delivery that decreased M1/M2 macrophage ratios. Overall, this bioelectronic ion pump demonstrates potential for accelerating wound healing via targeted and controlled delivery of therapeutic agents to wounds. Continued optimization and development of this device could not only lead to significant advancements in tissue repair and wound healing strategies but also reveal new physiological information about the dynamic wound environment.

Deep learning classification for macrophage subtypes through cell migratory pattern analysis.

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

Quantifying innervation facilitated by deep learning in wound healing.

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.

A system for bioelectronic delivery of treatment directed toward wound healing.

The development of wearable bioelectronic systems is a promising approach for optimal delivery of therapeutic treatments. These systems can provide continuous delivery of ions, charged biomolecules, and an electric field for various medical applications. However, rapid prototyping of wearable bioelectronic systems for controlled delivery of specific treatments with a scalable fabrication process is challenging. We present a wearable bioelectronic system comprised of a polydimethylsiloxane (PDMS) device cast in customizable 3D printed molds and a printed circuit board (PCB), which employs commercially available engineering components and tools throughout design and fabrication. The system, featuring solution-filled reservoirs, embedded electrodes, and hydrogel-filled capillary tubing, is assembled modularly. The PDMS and PCB both contain matching through-holes designed to hold metallic contact posts coated with silver epoxy, allowing for mechanical and electrical integration. This assembly scheme allows us to interchange subsystem components, such as various PCB designs and reservoir solutions. We present three PCB designs: a wired version and two battery-powered versions with and without onboard memory. The wired design uses an external voltage controller for device actuation. The battery-powered PCB design uses a microcontroller unit to enable pre-programmed applied voltages and deep sleep mode to prolong battery run time. Finally, the battery-powered PCB with onboard memory is developed to record delivered currents, which enables us to verify treatment dose delivered. To demonstrate the functionality of the platform, the devices are used to deliver H[Formula: see text] in vivo using mouse models and fluoxetine ex vivo using a simulated wound environment. Immunohistochemistry staining shows an improvement of 35.86% in the M1/M2 ratio of H[Formula: see text]-treated wounds compared with control wounds, indicating the potential of the platform to improve wound healing.

Quantifying innervation facilitated by deep learning in wound healing.

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.

Quantifying innervation facilitated by deep learning in wound healing.

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.

A machine learning based model accurately predicts cellular response to electric fields in multiple cell types.

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.

Automatic wound detection and size estimation using deep learning algorithms.

Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the wounded area and the healing process in the test animal, often a mouse. These images are then manually observed to determine key metrics -such as wound size progress- relevant to the study. However, this task is a time-consuming and laborious process. In addition, defining the wound edge could be subjective and can vary from one individual to another even among experts. Furthermore, as our understanding of the healing process grows, so does our need to efficiently and accurately track these key factors for high throughput (e.g., over large-scale and long-term experiments). Thus, in this study, we develop a deep learning-based image analysis pipeline that aims to intake non-uniform wound images and extract relevant information such as the location of interest, wound only image crops, and wound periphery size over-time metrics. In particular, our work focuses on images of wounded laboratory mice that are used widely for translationally relevant wound studies and leverages a commonly used ring-shaped splint present in most images to predict wound size. We apply the method to a dataset that was never meant to be quantified and, thus, presents many visual challenges. Additionally, the data set was not meant for training deep learning models and so is relatively small in size with only 256 images. We compare results to that of expert measurements and demonstrate preservation of information relevant to predicting wound closure despite variability from machine-to-expert and even expert-to-expert. The proposed system resulted in high fidelity results on unseen data with minimal human intervention. Furthermore, the pipeline estimates acceptable wound sizes when less than 50% of the images are missing reference objects.

Beta adrenergic receptor antagonist can modify Pseudomonas aeruginosa biofilm formation in vitro: Implications for chronic wounds.

Non-healing wounds are a major medical challenge, affecting over 6.5 million people in the US alone, with associated healthcare costs of about $16 billion annually. They can result in prolonged hospitalizations, work loss, disability, poor quality of life, and in diabetic patients with foot ulcers, amputation of the affected limb in 25% of patients. Though chronic ulcers may arise from different underlying diseases, the unifying feature is chronic infection, driving persistent inflammation that prolongs the healing process. One of the most frequently cultured or genetically identified pathogens in skin wounds is Pseudomonas aeruginosa. This species avidly forms biofilms in the wound that impede bacterial eradication by the host's immune mechanisms and limit efficacy of systemic antibiotics. Thus, non-antibiotic approaches to limit the growth and biofilm formation of this wound pathogen would be an advance in the treatment of chronic wounds. Prior work has demonstrated that the growth of other microbial species can be modulated by catecholamine agonists and antagonists of the adrenergic receptors (ARs). Here, we demonstrate that not only can the growth of this common wound pathogen be modulated by catecholamines, but also that the beta-AR antagonists can significantly decrease their growth, and importantly, limit their ability to form biofilms. These findings suggest that beta adrenergic antagonists may have a therapeutic role in the treatment of chronic skin wounds.

Combination product of dermal matrix, preconditioned human mesenchymal stem cells and timolol promotes wound healing in the porcine wound model.

A combination product of human mesenchymal stem/stromal cells (MSCs) embedded in an extracellular matrix scaffold and preconditioned with hypoxia and the beta-adrenergic receptor antagonist, timolol, combined with sustained timolol application post implantation, has shown promising results for improving wound healing in a diabetic mouse model. In the present study, we extend those findings to the more translatable large animal porcine wound model and show that the combined treatment promotes wound reepithelialization in these excisional wounds by 40.2% and increases the CD31 immunostaining marker of angiogenesis compared with the matrix control, while maintaining an accumulated timolol plasma concentration below the clinically safe level of 0.3 ng/mL after the 15-day course of topical application. Human GAPDH was not elevated in the day 15 wounds treated with MSC-containing device relative to wounds treated with matrix alone, indicating that the xenografted human MSCs in the treatment do not persist in these immune-competent animals after 15 days. The work demonstrates the efficacy and safety of the combined treatment for improving healing in the clinically relevant porcine wound model.

Alpha and beta adrenergic receptors modulate keratinocyte migration.

Keratinocyte migration into skin wounds is the step of the healing process that correlates with the wound closure rate. Keratinocyte migration, and wound epithelialization are decreased when beta 2-adrenergic receptors (B2AR) are activated by 1 µM epinephrine/adrenaline, resulting in delayed wound healing in human and mouse skin. In the present study, we found paradoxically, that in a subset of keratinocyte strains exposure to low concentrations of epinephrine (0.1 nM) increased, rather than decreased, their migratory rate. We find that both the alpha- and the beta-adrenergic receptors are expressed in human keratinocytes, and expression of alpha-2 AR subtypes demonstrated for the first time. Therefore, we tested if the alpha-AR could be modulating the increased migratory response observed in these cell strains. By using specific inhibitors to alpha-AR, we demonstrated that blocking A2B-AR could reverse the rapid cell migration induced by the 0.1 nM epinephrine. Phosphorylation of ERK was elevated after 1-10 minutes of the low epinephrine treatment and the A2B-AR inhibitor blocked the ERK phosphorylation. The results suggest that both the A2B-AR and B2AR mediate keratinocyte migration, in which with a low level of epinephrine treatment, A2B-AR could alter the B2AR signals and regulate the migration rate.

Repurposing Ophthalmologic Timolol for Dermatologic Use: Caveats and Historical Review of Adverse Events.

Ophthalmic timolol solution is increasingly being repurposed as a topical therapeutic for a variety of dermatologic diseases, including pyogenic granulomas, infantile hemangiomas, and chronic wounds. There are no published guidelines or protocols for use in these indications in adults, and the dermatologic community may not be familiar with adverse events that have been extensively documented relating to its ophthalmic use. We review the evidence available relating to adverse events to topical timolol use to evaluate its safety in dermatologic applications and to alert clinicians to screening and monitoring that is needed when repurposing this drug for dermatologic use. The majority of serious adverse events associated with ophthalmic timolol were reported in the first 7 years of use, between 1978 and 1985, of which most common were cardiovascular and respiratory events, but also included 32 deaths. The available evidence suggests that ophthalmic timolol safety profiling may have been incomplete prior to widespread use. Recent clinical trials for dermatologic indications have focused on documenting efficacy and have not had rigorous monitoring for potential adverse events. Topical timolol may be safe and effective for the treatment of various dermatologic conditions in patients whose medical histories have been carefully reviewed for evidence of pre-existing cardiac or pulmonary disease and are monitored for potential adverse events. Despite the wide use of timolol in ophthalmologic practice, safe dermatologic repurposing requires recognition of the potential for facilitated systemic absorption though the skin and appreciation of its history of adverse events.

Sling Training with Positive Reinforcement to Facilitate Porcine Wound Studies.

Domestic swine have become important large animal models for dermatologic and wound studies owing to the similarity of their skin architecture to that of human skin. To improve on current porcine wound protocols and accomplish postoperational daily wound care or treatment in a welfare-centered, low-stress setting, we developed a unique sling-training program using a commercially available Panepinto-like sling in combination with positive reinforcement of desired behaviors. Training using these methods is initiated during the acclimation period of 7-10 days before the initial surgical manipulation and continued throughout project-specific treatments for the duration of the study. Using this protocol, daily treatments can be administered without additional anesthesia while the animals rest in the sling with the administration of simultaneous nutritional enrichment. This low-stress handling program successfully facilitates the postoperational treatments and wound care without the use of potentially confounding anesthesia or sedation. It has a wide range of potential applications in translational medicine and in data acquisition from a resting state where baseline readouts of unstressed animals can be achieved.

Combination product of dermal matrix, human mesenchymal stem cells, and timolol promotes diabetic wound healing in mice.

Diabetic foot ulcers are a major health care concern with limited effective therapies. Mesenchymal stem cell (MSC)-based therapies are promising treatment options due to their beneficial effects of immunomodulation, angiogenesis, and other paracrine effects. We investigated whether a bioengineered scaffold device containing hypoxia-preconditioned, allogeneic human MSCs combined with the beta-adrenergic antagonist timolol could improve impaired wound healing in diabetic mice. Different iterations were tested to optimize the primary wound outcome, which was percent of wound epithelialization. MSC preconditioned in 1 µM timolol at 1% oxygen (hypoxia) seeded at a density of 2.5 × 105 cells/cm2 on Integra Matrix Wound Scaffold (MSC/T/H/S) applied to wounds and combined with daily topical timolol applications at 2.9 mM resulted in optimal wound epithelialization 65.6% (24.9% ± 13.0% with MSC/T/H/S vs 41.2% ± 20.1%, in control). Systemic absorption of timolol was below the HPLC limit of quantification, suggesting that with the 7-day treatment, accumulative steady-state timolol concentration is minimal. In the early inflammation stage of healing, the MSC/T/H/S treatment increased CCL2 expression, lowered the pro-inflammatory cytokines IL-1B and IL6 levels, decreased neutrophils by 44.8%, and shifted the macrophage ratio of M2/M1 to 1.9 in the wound, demonstrating an anti-inflammatory benefit. Importantly, expression of the endothelial marker CD31 was increased by 2.5-fold with this treatment. Overall, the combination device successfully improved wound healing and reduced the wound inflammatory response in the diabetic mouse model, suggesting that it could be translated to a therapy for patients with diabetic chronic wounds.

The Beta 2 Adrenergic Receptor Antagonist Timolol Improves Healing of Combined Burn and Radiation Wounds.

In a scenario involving a nuclear detonation during war or a terrorist attack, acute radiation exposure combined with thermal and blast effects results in severe skin injury. Although the cutaneous injury in such a scenario may not be lethal, it may lead to inflammation, delayed wound healing and loss of the skin barrier, resulting in an increased risk of infection. In this study, we tested the potential use of timolol, a beta-adrenergic receptor antagonist, to improve epidermal wound closure after combined burn and radiation injury using an ex vivo human skin culture model. Daily application of 10 µ M timolol after combined injury (burn and 10 Gy ex vivo irradiation) increased wound epithelialization by 5-20%. In addition, exposure to 10 Gy significantly suppressed epidermal keratinocyte proliferation by 46% at 48 h postirradiation. Similar to what has been observed in a thermal burn injury, the enzyme phenylethanolamine N-methyltransferase (PNMT), which generates epinephrine, was elevated in the combined thermal burn and radiation wounds. This likely resulted in elevated tissue levels of this catecholamine, which has been shown to delay healing. Thus, with the addition of timolol to the wound to block the binding of locally generated epinephrine to the beta-adrenergic receptor, healing is improved. This work suggests that by antagonizing local epinephrine action within the wound, a beta-adrenergic receptor antagonist such as timolol may be a useful adjunctive treatment to improve healing in the combined burn and radiation injury.