Integrated image and location analysis for wound classification: a deep learning approach.

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79-100% for Region of Interest (ROI) without location classifications, 73.98-100% for ROI with location classifications, and 78.10-100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Impact of Wound Dressing on Mechanotransduction within Tissues of Chronic Wounds.

Chronic wounds are significant public health problems impacting the health-related quality of individuals' lives (due to disability, decreased productivity, and loss of independence) and an immense economic burden to healthcare systems around the world. In this study, our main objective is to investigate how mechanotransduction can impact the healing process in chronic wounds. We have developed new three-dimensional models of wound tissue to study the distribution of forces within these tissues exerted by wound dressings with different characteristics. The roles of mechanical forces on wound healing have gained significant clinical attention; the application of mechanical forces is expected to influence the physiology of tissue surrounding a wound. We aim to investigate whether the force transmission within wound tissue is impacted by the dressing characteristics and whether this impact may differ with wound tissue's properties. Our results show that wound dressings with lower stiffnesses promote force transmission within a wound tissue. This impact is even more significant on stiffer wound tissues. Furthermore, we show that size of wound dressing alters forces that transmit within the wound tissue where dressings with 9 cm length show higher stresses. The wound tissue stiffening has been associated with healing of a wound. Our results demonstrate that wounds with stiffer tissue experience higher stresses. Taken all together, our findings suggest that low stiffness of wound dressing and its size may be introduced as a criterion to explain parameters predisposing a chronic wound to heal. This study's findings on the role of dressings and tissue characteristics demonstrate that precision dressings are required for wound management and understanding how a dressing impacts mechanotransduction in wound tissue will lead to design of novel dressings promoting healing in chronic wounds.

Multi-modal wound classification using wound image and location by deep neural network.

Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide on an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize them into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map was also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information were designed with the help of wound specialists. The multi-modal network was developed by concatenating the image-based and location-based classifier outputs with other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 82.48 to 100% in different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95 to 97.12% in various experiments. The proposed multi-modal network also showed a significant improvement in results from the previous works of literature.

Effects of Lower Limb Revascularization on the Microcirculation of the Foot: A Retrospective Cohort Study.

Background: Current assessment standards in chronic limb-threatening ischemia (CLTI) focus on macrovascular function while neglecting the microcirculation. Multispectral near-infrared spectroscopy (NIRS) provides hemodynamic characteristics of the microcirculation (i.e., capillaries) and may be a powerful tool for monitoring CLTI and preventing extremity loss. The aims of this study were to (1) investigate the effects of lower limb revascularization on the microcirculation and (2) determine if macrovascular and microvascular assessments correlate. Methods: An observational, retrospective cohort study of 38 endovascular interventions in 30 CLTI subjects was analyzed pre- and post-intervention for arterial Doppler acceleration times (AcT; macrovascular) and NIRS metrics (microvascular). Pre-intervention ankle-brachial index (ABI) was also analyzed. Results: AcT significantly decreased (p = 0.009) while oxyhemoglobin (HbO) significantly increased (p < 0.04) after endovascular intervention, indicating treatment efficacy. However, macrovascular measurements (ABI, AcT) and NIRS metrics of oxygenation and perfusion did not correlate (p > 0.06, r2 < 0.15, n = 23) indicating that macro- and microvascular assessment are not congruent. Conclusion: These findings suggest that macrovascular and microvascular assessments can determine interventional efficacy in their corresponding vasculature. Their lack of correlation, however, suggests the need for simultaneous assessment as independent use may cause diagnostic information to be missed.

Detection of invisible dental biofilm using light-induced autofluorescence in adult patients-A systematic review.

BACKGROUND: Autofluorescence (AF) spectroscopy is a potent yet non-invasive technique with diagnostic and therapeutic applications. It identifies and characterizes changes occurring in the human body based on the changes in the fluorescence signatures. AIM: The primary purpose of this systematic review is to address the question "Is it feasible to detect dental plaque using light-induced auto-fluorescence in adult patients with invisible grade 1 plaque?" MATERIALS AND METHODS: The literature search was conducted independently MEDLINE, Embase, Cochrane Library, Web of Science, Google Scholar, and Scopus databases for relevant studies from January 2000 and June 2021, using the following terms in various combinations: detection, dental biofilm, plaque, light, auto-fluorescence, caries, gingivitis, sensitivity and specificity. Studies describing various fluorescence techniques for the detection of plaque, including sensitivity and specificity, were included. RESULTS: The majority of the studies indicated that AF spectroscopy allows fluorescence-based detection of various diseases, including early-stage dental plaque. While results of conventional plaque detection techniques are inconsistent, the data from the AF technique is reliable and reproducible, which can be used for patient documentation. However, a wide range of non-uniformity existed in these studies. AF spectroscopy, as a non-invasive technique, represents a viable and patient-friendly clinical tool for the early detection of dental biofilm plaque, and its meticulous removal has been directly responsible for the prevention of this disease. CONCLUSION: Despite the heterogeneity and limitations of studies included in this review, the future for light-induced autofluorescence spectroscopy technologies in diagnostic dentistry certainly presents an accurate and potentially applicable option.

Evidence-based Potential Therapeutic Applications of Cannabinoids in Wound Management.

BACKGROUND: Although wound management is a major component of all domains of healthcare, conventional therapeutics have numerous limitations. The endocannabinoid system of the skin, one of the major endogenous systems, has recently been connected to wound healing. Cannabinoids and their interactions with the endogenous chemical signaling system may be a promising therapeutic option because they address some of the fundamental pathways for physiologic derangement that underpin chronic integumentary wounds. RECENT ADVANCES: The therapeutic applications of cannabinoids are increasing because of their legalization and resulting market expansion. Recently, their immunosuppressive and anti-inflammatory properties have been explored for the treatment of wounds that are not effectively managed by conventional medicines. CRITICAL ISSUES: Failure to manage wounds effectively is associated with reduced quality of life, disability, mortality, and increased healthcare expenditures. Therapeutic options that can manage wounds effectively and efficiently are needed. In this review, the authors summarize recent advances on the use of cannabinoids to treat skin disorders with an emphasis on wound management. FUTURE DIRECTIONS: Effective wound management requires medicines with good therapeutic outcomes and minimal adverse effects. Despite the promising results of cannabinoids in wound management, further controlled clinical studies are required to establish the definitive role of these compounds in the pathophysiology of wounds and their usefulness in the clinical setting.

Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review.

Significance: Accurately predicting wound healing trajectories is difficult for wound care clinicians due to the complex and dynamic processes involved in wound healing. Wound care teams capture images of wounds during clinical visits generating big datasets over time. Developing novel artificial intelligence (AI) systems can help clinicians diagnose, assess the effectiveness of therapy, and predict healing outcomes. Recent Advances: Rapid developments in computer processing have enabled the development of AI-based systems that can improve the diagnosis and effectiveness of therapy in various clinical specializations. In the past decade, we have witnessed AI revolutionizing all types of medical imaging like X-ray, ultrasound, computed tomography, magnetic resonance imaging, etc., but AI-based systems remain to be developed clinically and computationally for high-quality wound care that can result in better patient outcomes. Critical Issues: In the current standard of care, collecting wound images on every clinical visit, interpreting and archiving the data are cumbersome and time consuming. Commercial platforms are developed to capture images, perform wound measurements, and provide clinicians with a workflow for diagnosis, but AI-based systems are still in their infancy. This systematic review summarizes the breadth and depth of the most recent and relevant work in intelligent image-based data analysis and system developments for wound assessment. Future Directions: With increasing availabilities of massive data (wound images, wound-specific electronic health records, etc.) as well as powerful computing resources, AI-based digital platforms will play a significant role in delivering data-driven care to people suffering from debilitating chronic wounds.

Advances in Hemostatic Wound Dressings: Clinical Implications and Insight.

ABSTRACT: Hemorrhagic shock is one of the leading causes of prehospital death in the armed forces. In this state, the body begins to shut down because of blood volume depletion. In both civilian and military trauma, a significant number of hemorrhage deaths occur in the first several hours after injury. Researchers all over the globe are working to develop relatively inexpensive and easy-to-transport products to prevent soldiers from going into hemorrhagic shock. For example, many advances have been made during the last several years toward the development of ideal hemostatic dressings. No current hemostatic agents meet all of the requirements, but the ideal dressing would fulfill many important measures: minimizes or stops blood flow within minutes, contains hemostatic agents to enhance blood clotting, is easy to apply, does not need preapplication preparation, has a reasonably long shelf life, is safe to use, prevents bacterial or viral transmission, is stable at extreme temperatures, and is inexpensive. For this literature review, the authors conducted an extensive search of academic scientific databases for relevant keywords and assessed and summarized the results. This review aimed to identify recent advances in hemostatic wound dressings; summarize the currently available dressings and their supporting literature; and discuss the compositions, mechanisms of action, and clinical relevance of each category of dressing. In addition, case studies and suggestions for future research into hemorrhage control with new hemostatic agents are provided.

Self-management in Heart Failure: The Importance of Self-regulation but not Complexity of Condition.

Heart failure (HF) is a growing public health problem. Self-management (SM) of HF is an important component of chronic disease management. Guided by the Individual and Family Self-Management Theory (IFSMT), we examined the associations among complexity of condition, self-regulation, and self-efficacy mediation of SM behaviors in a population of HF outpatients. A cross-sectional design was used. Seventy-three outpatients with HF were enrolled. Simple and multiple linear regressions were run for each outcome variable. Only self-regulation was significantly associated with SM behavior. Complexity of condition was not significantly associated with SM behavior. There was no mediation by self-efficacy. Future nursing interventions should explore self-regulation in HF to provide a clearer understanding of the processes used to change health behavior. SM may be particularly useful in HF with preserved ejection fraction (EF), where there is no proven pharmacological treatment.

Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks.

Wound prognostic models not only provide an estimate of wound healing time to motivate patients to follow up their treatments but also can help clinicians to decide whether to use a standard care or adjuvant therapies and to assist them with designing clinical trials. However, collecting prognosis factors from Electronic Medical Records (EMR) of patients is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed time series medical generative adversarial networks (GANs) to generate synthetic wound prognosis factors using very limited information collected during routine care in a specialized wound care facility. The generated prognosis variables are used in developing a predictive model for chronic wound healing trajectory. Our novel medical GAN can produce both continuous and categorical features from EMR. Moreover, we applied temporal information to our model by considering data collected from the weekly follow-ups of patients. Conditional training strategies were utilized to enhance training and generate classified data in terms of healing or non-healing. The ability of the proposed model to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We utilized samples generated by our proposed GAN in training a prognosis model to demonstrate its real-life application. Using the generated samples in training predictive models improved the classification accuracy by 6.66-10.01% compared to the previous EMR-GAN. Additionally, the suggested prognosis classifier has achieved the area under the curve (AUC) of 0.875, 0.810, and 0.647 when training the network using data from the first three visits, first two visits, and first visit, respectively. These results indicate a significant improvement in wound healing prediction compared to the previous prognosis models.

Multiclass wound image classification using an ensemble deep CNN-based classifier.

Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually. Wound classification is a key step in wound diagnosis that would help clinicians to identify an optimal treatment procedure. Hence, having a high-performance classifier assists wound specialists to classify wound types with less financial and time costs. Different wound classification methods based on machine learning and deep learning have been proposed in the literature. In this study, we have developed an ensemble Deep Convolutional Neural Network-based classifier to categorize wound images into multiple classes including surgical, diabetic, and venous ulcers. The output classification scores of two classifiers (namely, patch-wise and image-wise) are fed into a Multilayer Perceptron to provide a superior classification performance. A 5-fold cross-validation approach is used to evaluate the proposed method. We obtained maximum and average classification accuracy values of 96.4% and 94.28% for binary and 91.9% and 87.7% for 3-class classification problems. The proposed classifier was compared with some common deep classifiers and showed significantly higher accuracy metrics. We also tested the proposed method on the Medetec wound image dataset, and the accuracy values of 91.2% and 82.9% were obtained for binary and 3-class classifications. The results show that our proposed method can be used effectively as a decision support system in classification of wound images or other related clinical applications.

670 nm photobiomodulation improves the mitochondrial redox state of diabetic wounds.

BACKGROUND: Photobiomodulation (PBM) by far-red (FR) to near-infrared (NIR) light has been demonstrated to accelerate diabetic wound healing in preclinical and clinical studies. Mitochondrial dysfunction and oxidative stress play key roles in impaired diabetic wound healing, and the effect of PBM on the metabolic state of diabetic wounds remains to be elucidated. METHODS: In this study, a custom-designed in vivo fluorescence imaging technique was used to quantitatively assess the effect of FR-PBM on the mitochondrial bioenergetics of diabetic wounds. The intrinsic fluorescence of two mitochondrial co-enzymes, nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD), was monitored to quantify the redox ratio (RR) (NADH/FAD) of wounds over time. RESULTS: Using an excisional model of wound healing, we demonstrated that 670 nm (FR) PBM improved mitochondrial bioenergetics and stimulated the rate of wound healing in diabetic db/db mice. Wound closure and the RR of diabetic wounds in response to 670 nm PBM (4.5 J/cm2, 60 mW/cm2 for 90 s per day, 5 days/week) were compared to the sham-treated group. At day 9 of post-wounding, we observed a 43% decrease in the wound area and a 75% increase in RR in FR-treated diabetic mice compared to sham-treated diabetic mice. CONCLUSIONS: We conclude that the increase in mitochondrial RR and the related decrease in oxidative stress may be an important factor in FR-PBM mediated acceleration of wound healing in diabetic mice.

In Vitro Characterization of Pressure Redistribution Among Commercially Available Wound Dressings.

OBJECTIVE: Recent clinical evidence has suggested that certain wound dressings may play a significant role in protocols to prevent or reduce pressure injury (PI) in patients at risk by modifying the pressure, friction, and shear forces that can contribute to PI. The aim of this study was to investigate the pressure reduction properties of commercially available wound dressings in vitro. METHODS: Using a standardized protocol (1.7 kg, 7.5-cm sphere), testing was performed in a controlled environment by the same clinician using a pressure mapping device (XSENSOR LX205; XSENSOR Technology Corporation, Calgary, Alberta, Canada) to measure and compare the pressure mitigation properties in a variety of wound dressings. RESULTS: A total of 13 different commercially available dressings were tested in triplicate for changes in pressure redistribution as compared with the control. One dressing demonstrated the greatest reduction of pressure forces (OxyBand PR; 50.33 ± 1.45 mm Hg) compared with the control (302.7 ± 0.33 mm Hg) and the greatest surface area of all the study dressings tested. There was a negative correlation (R2 = 0.73) between the average pressure distribution of a wound dressing and its contact area. Further, the peak pressure for OxyBand PR (P ≤ .05) was significantly different from all other tested dressings. CONCLUSIONS: One dressing (OxyBand PR) provided superior pressure redistribution and significantly reduced peak pressure in this study when compared with currently available standard foam and silicone dressings that are marketed for the purpose of PI prevention.

Fully automatic wound segmentation with deep convolutional neural networks.

Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. The advantage of this model is its lightweight and less compute-intensive architecture. The performance is not compromised and is comparable to deeper neural networks. We build an annotated wound image dataset consisting of 1109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks. The full implementation is available at https://github.com/uwm-bigdata/wound-segmentation .

Photobiomodulation preserves mitochondrial redox state and is retinoprotective in a rodent model of retinitis pigmentosa.

Photobiomodulation (PBM) by far-red (FR) to near-infrared (NIR) light has been demonstrated to restore the function of damaged mitochondria, increase the production of cytoprotective factors and prevent cell death. Our laboratory has shown that FR PBM improves functional and structural outcomes in animal models of retinal injury and retinal degenerative disease. The current study tested the hypothesis that a brief course of NIR (830 nm) PBM would preserve mitochondrial metabolic state and attenuate photoreceptor loss in a model of retinitis pigmentosa, the P23H transgenic rat. P23H rat pups were treated with 830 nm light (180 s; 25 mW/cm2; 4.5 J/cm2) using a light-emitting diode array (Quantum Devices, Barneveld, WI) from postnatal day (p) 10 to p25. Sham-treated rats were restrained, but not treated with 830 nm light. Retinal metabolic state, function and morphology were assessed at p30 by measurement of mitochondrial redox (NADH/FAD) state by 3D optical cryo-imaging, electroretinography (ERG), spectral-domain optical coherence tomography (SD-OCT), and histomorphometry. PBM preserved retinal metabolic state, retinal function, and retinal morphology in PBM-treated animals compared to the sham-treated group. PBM protected against the disruption of the oxidation state of the mitochondrial respiratory chain observed in sham-treated animals. Scotopic ERG responses over a range of flash intensities were significantly greater in PBM-treated rats compared to sham controls. SD-OCT studies and histological assessment showed that PBM preserved the structural integrity of the retina. These findings demonstrate for the first time a direct effect of NIR PBM on retinal mitochondrial redox status in a well-established model of retinal disease. They show that chronic proteotoxic stress disrupts retinal bioenergetics resulting in mitochondrial dysfunction, and retinal degeneration and that therapies normalizing mitochondrial metabolism have considerable potential for the treatment of retinal degenerative disease.

Fluorescence Imaging of Mitochondrial Redox State to Assess Diabetic Wounds.

Background: Diabetes is known to cause delayed wound healing, and chronic non-healing lower extremity ulcers may end with lower limb amputations and mortalities. Given the increasing prevalence of diabetes mellitus worldwide, it is critical to focus on underlying mechanisms of these debilitating wounds to find novel therapeutic strategies and thereby improve patient outcome. Methods: This study aims to design a label-free optical fluorescence imager that captures metabolic indices (NADH and FAD autofluorescence) and monitors the in vivo wound healing progress noninvasively. Furthermore, 3D optical cryo-imaging of the mitochondrial redox state was utilized to assess the volumetric redox state of the wound tissue. Results: The results from our in vivo fluorescence imager and the 3D cryo-imager quantify the differences between the redox state of wounds on diabetic mice in comparison with the control mice. These metabolic changes are associated with mitochondrial dysfunction and higher oxidative stress in diabetic wounds. A significant correlation was observed between the redox state and the area of the wounds. Conclusion: The results suggest that our developed novel optical imaging system can successfully be used as an optical indicator of the complex wound healing process noninvasively.

Feasibility and Pilot Testing of a Mindfulness Intervention for Frail Older Adults and Individuals With Dementia.

Mindfulness interventions have been beneficial for healthy adults and individuals experiencing a stressful medical or mental health diagnosis. The purposes of the current study were to: (a) determine feasibility of mindfulness for older adults in long-term residential settings, and (b) examine differences in outcomes between a mindfulness and cognitive activity. The current study is the first mindfulness study to include individuals in moderate and severe stages of dementia, and included 36 individuals with a range of cognitive abilities. A crossover design was used, and the intervention was feasible for continued practice by individuals with cognitive impairment. Statistically significant short-term changes in agitation, discomfort, anger, and anxiety were found. Nighttime sleep did not improve, but participants slept less during the day. Long-term changes in outcomes were not found. Mindfulness may be useful in decreasing emotional reactivity and improving well-being of older adults in long-term care. TARGETS: Individuals with multiple chronic conditions, including cognitive impairment. INTERVENTION DESCRIPTION: The Present in the Now (PIN) intervention is a mindfulness intervention with three components: attentional skill exercises, body awareness activities, and compassion meditation. MECHANISMS OF ACTION: Mindfulness acts to decrease emotional reactivity through cognitive and affective mechanisms of action and neural activation of the cingulate cortex, amygdala, and hippocampus. OUTCOMES: Agitation, affect, stress, sleep, discomfort, and communication of need. [Res Gerontol Nurs. 2018; 11(3):137-150.].

Near-Infrared Photobiomodulation in Retinal Injury and Disease.

Evidence is growing that exposure of tissue to low energy photon irradiation in the far-red (FR) to near-infrared (NIR) range of the spectrum, collectively termed "photobiomodulation" (PBM) can restore the function of damaged mitochondria, upregulate the production of cytoprotective factors and prevent apoptotic cell death. PBM has been applied clinically in the treatment of soft tissue injuries and acceleration of wound healing for more than 40 years. Recent studies have demonstrated that FR/NIR photons penetrate diseased tissues including the retina. The therapeutic effects of PBM have been hypothesized to result from intracellular signaling pathways triggered when FR/NIR photons are absorbed by the mitochondrial photoacceptor molecule, cytochrome c oxidase, culminating in improved mitochondrial energy metabolism, increased cytoprotective factor production and cell survival. Investigations in rodent models of methanol-induced ocular toxicity, light damage, retinitis pigmentosa and age-related macular degeneration have demonstrated the PBM attenuates photoreceptor cell death, protects retinal function and exerts anti-inflammatory actions.

Endogenous events modulating myogenic regulation of cerebrovascular function.

The existence of arterial myogenic tone was first described by Bayliss in 1902, however, its association with pressure-dependent membrane depolarization was not observed until 1984. The factors that mediate myogenic arterial constriction remain unknown. One possible clue was a finding by our laboratory that cerebral arterial muscle cells express CYP 4A ω-hydroxylase enzyme that catalyzes the formation of the potent vasoconstrictor 20-hydroxyeicosatetraenoic acid (20-HETE) from arachidonic acid (AA), the production of which increased by elevations of intravascular pressure. 20-HETE activates protein kinase C (PKC), inhibits Ca(2+)-activated K(+) (KCa) channels, depolarizes arterial muscle cell membrane, activates L-type Ca(2+) channels, increases intracellular Ca(2+) ([Ca(2+)]i) and mediates autoregulation of cerebral blood flow. Emerging evidence indicates that 20-HETE level increases in ischemia/reperfusion injury and stimulates production of reactive oxygen species (ROS) resulting in oxidative stress induced ischemic stroke injury, which can be prevented by inhibition of 20-HETE synthesis or action. The brain also expresses CYP epoxygenases that convert AA to the vasodilator epoxyeicosatrienoic acids (EETs), the production of which increases in ischemia and provide protection against ischemia-induced tissue damage. Basal or stimulus released ROS act to modify cerebral myogenic tone. Similar to other enzymes CYP epoxygenase and ω-hydroxylase also generate ROS that modify myogenic cerebral reactivity. Hypoxia per se or adenosine released during hypoxia induces increased production of ROS that alter cerebrovascular function. The capacity of the brain to express CYP enzymes that produce bioactive EETs and 20-HETE and generate ROS has a significant bearing in regulating the dynamics of cerebral blood flow and serve as potential therapeutic targets for the management of pathologic disorders of the cerebral circulation.

Photobiomodulation induced by 670 nm light ameliorates MOG35-55 induced EAE in female C57BL/6 mice: a role for remediation of nitrosative stress.

BACKGROUND: Experimental autoimmune encephalomyelitis (EAE) is the most commonly studied animal model of multiple sclerosis (MS), a chronic autoimmune demyelinating disorder of the central nervous system. Immunomodulatory and immunosuppressive therapies currently approved for the treatment of MS slow disease progression, but do not prevent it. A growing body of evidence suggests additional mechanisms contribute to disease progression. We previously demonstrated the amelioration of myelin oligodendrocyte glycoprotein (MOG)-induced EAE in C57BL/6 mice by 670 nm light-induced photobiomodulation, mediated in part by immune modulation. Numerous other studies demonstrate that near-infrared/far red light is therapeutically active through modulation of nitrosoxidative stress. As nitric oxide has been reported to play diverse roles in EAE/MS, and recent studies suggest that axonal loss and progression of disability in MS is mediated by nitrosoxidative stress, we investigated the effect of 670 nm light treatment on nitrosative stress in MOG-induced EAE. METHODOLOGY: Cell culture experiments demonstrated that 670 nm light-mediated photobiomodulation attenuated antigen-specific nitric oxide production by heterogenous lymphocyte populations isolated from MOG immunized mice. Experiments in the EAE model demonstrated down-regulation of inducible nitric oxide synthase (iNOS) gene expression in the spinal cords of mice with EAE over the course of disease, compared to sham treated animals. Animals receiving 670 nm light treatment also exhibited up-regulation of the Bcl-2 anti-apoptosis gene, an increased Bcl-2:Bax ratio, and reduced apoptosis within the spinal cord of animals over the course of disease. 670 nm light therapy failed to ameliorate MOG-induced EAE in mice deficient in iNOS, confirming a role for remediation of nitrosative stress in the amelioration of MOG-induced EAE by 670 nm mediated photobiomodulation. CONCLUSIONS: These data indicate that 670 nm light therapy protects against nitrosative stress and apoptosis within the central nervous system, contributing to the clinical effect of 670 nm light therapy previously noted in the EAE model.