In this paper we report a simple and efficient method for the concurrent analysis of tryptophan, 5-HTP, tryptamine, serotonin, and 5-HIAA in mouse serum using UHPLC-ED after protein precipitation and dilution. These compounds are neuroactive and are of interest in studies of mood and behavior; They are also biomarkers for the presence of neuroendocrine tumors and are used in the diagnosis of these cancers. After a brief series of validation experiments, this method was applied to serum from mouse behaviour experiments.•A convenient UHPLC method with electrochemical detection for concomitant analysis of the serotonin pathway in serum, including, for the first time, tryptamine.•The method met all performance criteria established for use in our lab and was applied in rodent experiments.

Significance: Chronic skin ulcers, including venous, diabetic, and pressure ulcers, constitute a major health care burden, affecting 2-6 million people in the United States alone, with projected increases in incidence owing to the aging population and rising epidemic of diabetes. The ulcers are often accompanied by pain. Standard of care fails to heal ∼50% of diabetic foot ulcers and 25% of venous leg ulcers. Even advanced therapies do not heal >60%. Thus there is an unmet need for novel therapies that promote healing and also address the concomitant pain issue. Recent Advances: Prolotherapy involves injection of small amounts of an irritant material to the site of degenerated or painful joints, ligaments, and tendons. Multiple irritants are reported to be efficacious, but the focus here is on dextrose prolotherapy. In vitro and in vivo studies support translation to clinical use. Concentrations as low as 5% dextrose have resulted in production of growth factors that have critical roles in repair. Numerous clinical trials report pro-reparative effects of dextrose prolotherapy in joint diseases, tendon, and ligament damage, and for painful musculoskeletal issues. However, most of the studies have limitations that result in low-quality evidence. Critical Issues: The preclinical data support a role for dextrose prolotherapy in promoting tissue repair that is required for healing chronic wounds and ameliorating the associated pain. Critical issues include provision of evidence of efficacy in human chronic wounds. Another potential obstacle is limitation of reimbursement by third-party payers for a therapy with as yet limited evidence. Future Directions: Preclinical studies in models of chronic wounds would support clinical translation. As dextrose prolotherapy has some mechanistic similarities to already approved honey therapies, it may have a shortened pathway for clinical translation. The gold standard for widespread adoption would be a well-designed clinical trial.

Catecholamines play an important regulatory role in cutaneous wound healing. The exact role of dopamine in human epidermis has yet to be fully elucidated. Current published evidence describes its differential effects on two separate families of G protein coupled receptors: D1-like and D2-like dopamine receptors. Dopamine may enhance angiogenesis and wound healing through its action on dopamine D1 receptors, while impairing wound healing when activating D2 receptors. This review summarizes the evidence for the role of dopamine in wound healing and describes potential mechanisms behind its action on D1 versus D2-like receptors in the skin.

Abstract Background Diabetic foot ulcers (DFUs) represent a significant source of morbidity and an enormous financial burden. Standard care for DFUs involves systemic glucose control, ensuring adequate perfusion, debridement of nonviable tissue, off-loading, control of infection, local wound care and patient education, all administered by a multidisciplinary team. Unfortunately, even with the best standard of care (SOC) available, only 24% or 30% of DFUs will heal at weeks 12 or 20, respectively.The extracellular matrix (ECM) in DFUs is abnormal and its impairment has been proposed as a key target for new therapeutic devices. These devices intend to replace the aberrant ECM by implanting a matrix, either devoid of cells or enhanced with fibroblasts, keratinocytes or both as well as various growth factors. These new bioengineered skin substitutes are proposed to encourage angiogenesis and in-growth of new tissue, and to utilize living cells to generate cytokines needed for wound repair.To date, the efficacy of bioengineered ECM containing live cellular elements for improving healing above that of a SOC control group has not been compared with the efficacy of an ECM devoid of cells relative to the same SOC. Our hypothesis is that there is no difference in the improved healing effected by either of these two product types relative to SOC. Methods/Design To test this hypothesis we propose a randomized, single-blind, clinical trial with three arms: SOC, SOC plus Dermagraft® (bioengineered ECM containing living fibroblasts) and SOC plus Oasis® (ECM devoid of living cells) in patients with nonhealing DFUs. The primary outcome is the percentage of subjects that achieved complete wound closure by week 12. Discussion If our hypothesis is correct, then immense cost savings could be realized by using the orders-of-magnitude less expensive acellular ECM device without compromising patient health outcomes. The article describes the protocol proposed to test our hypothesis. Trial registration ClinicalTrials.gov: NCT01450943. Registered: 7 October 2011

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.

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.

Wound healing is one of the most complex processes in the human body, supported by many cellular events that are tightly coordinated to repair the wound efficiently. Chronic wounds have potentially life-threatening consequences. Traditional wound dressings come in direct contact with wounds to help them heal and avoid further complications. However, traditional wound dressings have some limitations. These dressings do not provide real-time information on wound conditions, leading clinicians to miss the best time for adjusting treatment. Moreover, the current diagnosis of wounds is relatively subjective. Wearable electronics have become a unique platform to potentially monitor wound conditions in a continuous manner accurately and even to serve as accelerated healing vehicles. In this review, we briefly discuss the wound status with some objective parameters/biomarkers influencing wound healing, followed by the presentation of various novel wearable devices used for monitoring wounds and accelerating wound healing. We further summarize the associated device working principles. This review concludes by highlighting some major challenges in wearable devices toward wound healing that need to be addressed by the research community.

Fibrosis occurs when collagen deposition and fibroblast proliferation replace healthy tissue. Red light (RL) may improve skin fibrosis via photobiomodulation, the process by which photosensitive chromophores in cells absorb visible or near-infrared light and undergo photophysical reactions. Our previous research demonstrated that high fluence RL reduces fibroblast proliferation, collagen deposition, and migration. Despite the identification of several cellular mechanisms underpinning RL phototherapy, little is known about the transcriptional changes that lead to anti-fibrotic cellular responses. Herein, RNA sequencing was performed on human dermal fibroblasts treated with RL phototherapy. Pathway enrichment and transcription factor analysis revealed regulation of extracellular matrices, proliferation, and cellular responses to oxygen-containing compounds following RL phototherapy. Specifically, RL phototherapy increased the expression of MMP1, which codes for matrix metalloproteinase-1 (MMP-1) and is responsible for remodeling extracellular collagen. Differential regulation of MMP1 was confirmed with RT-qPCR and ELISA. Additionally, RL upregulated PRSS35, which has not been previously associated with skin activity, but has known anti-fibrotic functions. Our results suggest that RL may benefit patients by altering fibrotic gene expression.

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.

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.