Search results for: releasing%20predators

Authors: Pauline Nyssen, Ange Mouithys-Mickalad, Maryse Hoebeke

Abstract:

Inflammation is a complex physiological phenomenon involving chemical and enzymatic mechanisms. Polymorphonuclear neutrophil leukocytes (PMNs) play an important role by producing reactive oxygen species (ROS) and releasing myeloperoxidase (MPO), a pro-oxidant enzyme. Released both in the phagolysosome and the extracellular medium, MPO produces during its peroxidase and halogenation cycles oxidant species, including hypochlorous acid, involved in the destruction of pathogen agents, like bacteria or viruses. Inflammatory pathologies, like rheumatoid arthritis, atherosclerosis induce an excessive stimulation of the PMNs and, therefore, an uncontrolled release of ROS and MPO in the extracellular medium, causing severe damages to the surrounding tissues and biomolecules such as proteins, lipids, and DNA. The treatment of chronic inflammatory pathologies remains a challenge. For many years, MPO has been used as a target for the development of effective treatments. Numerous studies have been focused on the design of new drugs presenting more efficient MPO inhibitory properties. However, some designed inhibitors can be toxic. An alternative consists of assessing the potential inhibitory action of clinically-known molecules, having antioxidant activity. Propofol, 2,6-diisopropyl phenol, which is used as an intravenous anesthetic agent, meets these requirements. Besides its anesthetic action employed to induce a sedative state during surgery or in intensive care units, propofol and its injectable form Diprivan indeed present antioxidant properties and act as ROS and free radical scavengers. A study has also evidenced the ability of propofol to inhibit the formation of the neutrophil extracellular traps fibers, which are important to trap pathogen microorganisms during the inflammation process. The aim of this study was to investigate the potential inhibitory action mechanism of propofol and Diprivan on MPO activity. To go into the anti-inflammatory action of propofol in-depth, two of its oxidative derivatives, 2,6-diisopropyl-1,4-p-benzoquinone (PPFQ) and 3,5,3’,5’-tetra isopropyl-(4,4’)-diphenoquinone (PPFDQ), were studied regarding their inhibitory action. Specific immunological extraction followed by enzyme detection (SIEFED) and molecular modeling have evidenced the low anti-catalytic action of propofol. Stopped-flow absorption spectroscopy and direct MPO activity analysis have proved that propofol acts as a reversible MPO inhibitor by interacting as a reductive substrate in the peroxidase cycle and promoting the accumulation of redox compound II. Overall, Diprivan exhibited a weaker inhibitory action than the active molecule propofol. In contrast, PPFQ seemed to bind and obstruct the enzyme active site, preventing the trigger of the MPO oxidant cycles. PPFQ induced a better chlorination cycle inhibition at basic and neutral pH in comparison to propofol. PPFDQ did not show any MPO inhibition activity. The three interest molecules have also demonstrated their inhibition ability on an important step of the inflammation pathway, the PMNs superoxide anions production, thanks to EPR spectroscopy and chemiluminescence. In conclusion, propofol presents an interesting immunomodulatory activity by acting as a reductive substrate in the peroxidase cycle of MPO, slowing down its activity, whereas PPFQ acts more as an anti-catalytic substrate. Although PPFDQ has no impact on MPO, it can act on the inflammation process by inhibiting the superoxide anions production by PMNs.

Keywords: Diprivan, inhibitor, myeloperoxidase, propofol, spectroscopy

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Authors: Nino Kupatadze, Tamar Memanishvili, Natia Ochkhikidze, David Tugushi, Zaal Kokaia, Ramaz Katsarava

Abstract:

Amino acid-based Biodegradable poly(ester-amide)s (PEAs) have gained considerable interest as a promising materials for numerous biomedical applications. These polymers reveal a high biocompatibility and easily form small particles suitable for delivery various biological, as well as elastic bio-erodible films serving as matrices for constructing antibacterial coatings. In the present work we have demonstrated a potential of the PEAs for two applications: 1. cell therapy for stroke as vehicles for delivery and sustained release of growth factors, 2. bactericidal coating as prevention biofilm and applicable in infected wound management. Stroke remains the main cause of adult disability with limited treatment options. Although stem cell therapy is a promising strategy, it still requires improvement of cell survival, differentiation and tissue modulation. .Recently, microspheres (MPs) made of biodegradable polymers have gained significant attention for providing necessary support of transplanted cells. To investigate this strategy in the cell therapy of stroke, MPs loaded with transcription factors Wnt3A/BMP4 were prepared. These proteins have been shown to mediate the maturation of the cortical neurons. We have suggested that implantation of these materials could create a suitable microenvironment for implanted cells. Particles with spherical shape, porous surface, and 5-40 m in size (monitored by scanning electron microscopy) were made on the basis of the original PEA composed of adipic acid, L-phenylalanine and 1,4-butanediol. After 4 months transplantation of MPs in rodent brain, no inflammation was observed. Additionally, factors were successfully released from MPs and affected neuronal cell differentiation in in vitro. The in vivo study using loaded MPs is in progress. Another severe problem in biomedicine is prevention of surgical devices from biofilm formation. Antimicrobial polymeric coatings are most effective “shields” to protect surfaces/devices from biofilm formation. Among matrices for constructing the coatings preference should be given to bio-erodible polymers. Such types of coatings will play a role of “unstable seating” that will not allow bacteria to occupy the surface. In other words, bio-erodible coatings would be discomfort shelter for bacteria that along with releasing “killers of bacteria” should prevent the formation of biofilm. For this purpose, we selected an original biodegradable PEA composed of L-leucine, 1,6-hexanediol and sebacic acid as a bio-erodible matrix, and nanosilver (AgNPs) as a bactericidal agent (“killer of bacteria”). Such nanocomposite material is also promising in treatment of superficial wound and ulcer. The solubility of the PEA in ethanol allows to reduce AgNO3 to NPs directly in the solution, where the solvent served as a reductive agent, and the PEA served as NPs stabilizer. The photochemical reduction was selected as a basic method to form NPs. The obtained AgNPs were characterized by UV-spectroscopy, transmission electron microscope (TEM), and dynamic light scattering (DLS). According to the UV-data and TEM data the photochemical reduction resulted in spherical AgNPs with wide particle size distribution with a high contribution of the particles below 10 nm that are known as responsible for bactericidal activity of AgNPs. DLS study showed that average size of nanoparticles formed after photo-reduction in ethanol solution ranged within ca. 50 nm.

Keywords: biodegradable polymers, microparticles, nanocomposites, stem cell therapy, stroke

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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