216 research outputs found

    Volume Regulated Anion Channel Currents of Rat Hippocampal Neurons and Their Contribution to Oxygen-and-Glucose Deprivation Induced Neuronal Death

    Get PDF
    Volume-regulated anion channels (VRAC) are widely expressed chloride channels that are critical for the cell volume regulation. In the mammalian central nervous system, the physiological expression of neuronal VRAC and its role in cerebral ischemia are issues largely unknown. We show that hypoosmotic medium induce an outwardly rectifying chloride conductance in CA1 pyramidal neurons in rat hippocampal slices. The induced chloride conductance was sensitive to some of the VRAC inhibitors, namely, IAA-94 (300 µM) and NPPB (100 µM), but not to tamoxifen (10 µM). Using oxygen-and-glucose deprivation (OGD) to simulate ischemic conditions in slices, VRAC activation appeared after OGD induced anoxic depolarization (AD) that showed a progressive increase in current amplitude over the period of post-OGD reperfusion. The OGD induced VRAC currents were significantly inhibited by inhibitors for glutamate AMPA (30 µM NBQX) and NMDA (40 µM AP-5) receptors in the OGD solution, supporting the view that induction of AD requires an excessive Na+-loading via these receptors that in turn to activate neuronal VRAC. In the presence of NPPB and DCPIB in the post-OGD reperfusion solution, the OGD induced CA1 pyramidal neuron death, as measured by TO-PRO-3-I staining, was significantly reduced, although DCPIB did not appear to be an effective neuronal VRAC blocker. Altogether, we show that rat hippocampal pyramidal neurons express functional VRAC, and ischemic conditions can initial neuronal VRAC activation that may contribute to ischemic neuronal damage

    Air quality and urban sustainable development: the application of machine learning tools

    Full text link
    [EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 18(4):1-18. https://doi.org/10.1007/s13762-020-02896-6S118184Al-Dabbous A, Kumar P, Khan A (2017) Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network. Atmos Pollut Res 8:446–454. https://doi.org/10.1016/j.apr.2016.11.004Antanasijević D, Pocajt V, Povrenović D, Ristić M, Perić-Grujić A (2013) PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519. https://doi.org/10.1016/j.scitotenv.2012.10.110Brink H, Richards JW, Fetherolf M (2016) Real-world machine learning. Richards JW, Fetherolf M (eds) Manning Publications Co. Berkeley, CA. https://www.manning.com/books/real-world-machine-learning. Accessed 26 Apr 2020Cervone G, Franzese P, Ezber Y, Boybeyi Z (2008) Risk assessment of atmospheric emissions using machine learning. Nat Hazard Earth Syst 8:991–1000. https://doi.org/10.5194/nhess-8-991-2008Chen S, Kan G, Li J, Liang K, Hong Y (2018) Investigating China’s urban air quality using big data, information theory, and machine learning. Pol J Environ Stud 27:565–578. https://doi.org/10.15244/pjoes/75159Corani (2005) Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model 185:513–529. https://doi.org/10.1016/j.ecolmodel.2005.01.008Cruz C, Gómez A, Ramírez L, Villalva A, Monge O, Varela J, Quiroz J, Duarte H (2017) Calidad del aire respecto de metales (Pb, Cd, Ni, Cu, Cr) y relación con salud respiratoria: caso Sonora, México. Rev Int Contam Ambient 33:23–34. https://doi.org/10.20937/RICA.2017.33.esp02.02de Hoogh K, Héritier H, Stafoggia M, Künzli N, Kloog I (2018) Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environ Pollut 233:1147–1154. https://doi.org/10.1016/j.envpol.2017.10.025Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering. Atmos Pollut Res 9:912–922. https://doi.org/10.1016/j.apr.2018.02.006García N, Combarro E, del Coz J, Montañes E (2013) A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Appl Math Comput 219:8923–8937. https://doi.org/10.1016/j.amc.2013.03.018Gibert K, Sànchez-Màrre M, Sevilla B (2012) Tools for environmental data mining and intelligent decision support. In iEMSs. Leipzig, Germany. http://www.iemss.org/society/index.php/iemss-2012-proceedings. Accessed 26 Nov 2018Gibert K, Sànchez-Marrè M, Izquierdo J (2016) A survey on pre-processing techniques: relevant issues in the context of environmental data mining. Ai Commun 29:627–663. https://doi.org/10.3233/AIC-160710Gounaridis D, Chorianopoulos I, Koukoulas S (2018) Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr 90:134–144. https://doi.org/10.1016/j.apgeog.2017.12.001Holloway J, Mengersen K (2018) Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sens 10:1–21. https://doi.org/10.3390/rs10091365Ifaei P, Karbassi A, Lee S, Yoo Ch (2017) A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data. Energy Convers Manag 153:257–277. https://doi.org/10.1016/j.enconman.2017.10.014Kadiyala A, Kumar A (2017a) Applications of R to evaluate environmental data science problems. Environ Prog Sustain 36:1358–1364. https://doi.org/10.1002/ep.12676Kadiyala A, Kumar A (2017b) Vector time series-based radial basis function neural network modeling of air quality inside a public transportation bus using available software. Environ Prog Sustain 36:4–10. https://doi.org/10.1002/ep.12523Karimian H, Li Q, Wu Ch, Qi Y, Mo Y, Chen G, Zhang X, Sachdeva S (2019) Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations. Aerosol Air Qual Res 19:1400–1410. https://doi.org/10.4209/aaqr.2018.12.0450Krzyzanowski M, Apte J, Bonjour S, Brauer M, Cohen A, Prüss-Ustun A (2014) Air pollution in the mega-cities. Curr Environ Health Rep 1:185–191. https://doi.org/10.1007/s40572-014-0019-7Lässig K, Morik (2016) Computat sustainability. Springer, Berlin. https://doi.org/10.1007/978-3-319-31858-5Li Y, Wu Y-X, Zeng Z-X, Guo L (2006) Research on forecast model for sustainable development of economy-environment system based on PCA and SVM. In: Proceedings of the 2006 international conference on machine learning and cybernetics, vol 2006. IEEE, Dalian, China, pp 3590–3593. https://doi.org/10.1109/ICMLC.2006.258576Liu B-Ch, Binaykia A, Chang P-Ch, Tiwari M, Tsao Ch-Ch (2017) Urban air quality forecasting based on multi- dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0179763Lubell M, Feiock R, Handy S (2009) City adoption of environmentally sustainable policies in California’s Central Valley. J Am Plan Assoc 75:293–308. https://doi.org/10.1080/01944360902952295Ma D, Zhang Z (2016) Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. J Hazard Mater 311:237–245. https://doi.org/10.1016/j.jhazmat.2016.03.022Madu C, Kuei N, Lee P (2017) Urban sustainability management: a deep learning perspective. Sustain Cities Soc 30:1–17. https://doi.org/10.1016/j.scs.2016.12.012Mellos K (1988) Theory of eco-development. In: Perspectives on ecology. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-19598-5_4Ni XY, Huang H, Du WP (2017) Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data. Atmos Environ 150:146–161. https://doi.org/10.1016/j.atmosenv.2016.11.054Oprea M, Dragomir E, Popescu M, Mihalache S (2016) Particulate matter air pollutants forecasting using inductive learning approach. Rev Chim 67:2075–2081Paas B, Stienen J, Vorländer M, Schneider Ch (2017) Modelling of urban near-road atmospheric PM concentrations using an artificial neural network approach with acoustic data input. Environments 4:1–25. https://doi.org/10.3390/environments4020026Pandey G, Zhang B, Jian L (2013) Predicting submicron air pollution indicators: a machine learning approach. Environ Sci Proc Impacts 15:996–1005. https://doi.org/10.1039/c3em30890aPeng H, Lima A, Teakles A, Jin J, Cannon A, Hsieh W (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual Atmos Health 10:195–211. https://doi.org/10.1007/s11869-016-0414-3Pérez-Ortíz M, de La Paz-Marín M, Gutiérrez PA, Hervás-Martínez C (2014) Classification of EU countries’ progress towards sustainable development based on ordinal regression techniques. Knowl Based Syst 66:178–189. https://doi.org/10.1016/j.knosys.2014.04.041Phillis Y, Kouikoglou V, Verdugo C (2017) Urban sustainability assessment and ranking of cities. Comput Environ Urban 64:254–265. https://doi.org/10.1016/j.compenvurbsys.2017.03.002Saeed S, Hussain L, Awan I, Idris A (2017) Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours. Int J Comput Sci Netw Secur 17:45–52Sayegh A, Munir S, Habeebullah T (2014) Comparing the performance of statistical models for predicting PM10 concentrations. Aerosol Air Qual Res 14:653–665. https://doi.org/10.4209/aaqr.2013.07.0259Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16:2598–2606. https://doi.org/10.1109/JSEN.2016.2514378Sierra B (2006) Aprendizaje automático conceptos básicos y avanzados Aspectos prácticos utilizando el software Weka. Madrid Pearson Prentice Hall, MadridSingh K, Gupta S, Rai P (2013) Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos Environ 80:426–437. https://doi.org/10.1016/j.atmosenv.2013.08.023Song L, Pang S, Longley I, Olivares G, Sarrafzadeh A (2014) Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression. In: International joint conference on neural networks. IEEE, Beijing, pp 623–630. https://doi.org/10.1109/IJCNN.2014.6889521Souza R, Coelho G, da Silva A, Pozza S (2015) Using ensembles of artificial neural networks to improve PM10 forecasts. Chem Eng Trans 43:2161–2166. https://doi.org/10.3303/CET1543361Suárez A, García PJ, Riesgo P, del Coz JJ, Iglesias-Rodríguez FJ (2011) Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math Comput Model 54:453–1466. https://doi.org/10.1016/j.mcm.2011.04.017Tamas W, Notton G, Paoli C, Nivet M, Voyant C (2016) Hybridization of air quality forecasting models using machine learning and clustering: an original approach to detect pollutant peaks. Aerosol Air Qual Res 16:405–416. https://doi.org/10.4209/aaqr.2015.03.0193Toumi O, Le Gallo J, Ben Rejeb J (2017) Assessment of Latin American sustainability. Renew Sustain Energy Rev 78:878–885. https://doi.org/10.1016/j.rser.2017.05.013Tzima F, Mitkas P, Voukantsis D, Karatzas K (2011) Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst Appl 38:5019–5027. https://doi.org/10.1016/j.eswa.2010.09.148United Nations, Department of Economic and Social Affairs (2019) World urbanization prospects The 2018 Revision. New York. https://doi.org/10.18356/b9e995fe-enWang B (2019) Applying machine-learning methods based on causality analysis to determine air quality in China. Pol J Environ Stud 28:3877–3885. https://doi.org/10.15244/pjoes/99639Wang X, Xiao Z (2017) Regional eco-efficiency prediction with support vector spatial dynamic MIDAS. J Clean Prod 161:165–177. https://doi.org/10.1016/j.jclepro.2017.05.077Wang W, Men C, Lu W (2008) Online prediction model based on support vector machine. Neurocomputing 71:550–558. https://doi.org/10.1016/j.neucom.2007.07.020WCED (1987) Report of the world commission on environment and development: our common future: report of the world commission on environment and development. WCED, Oslo. https://doi.org/10.1080/07488008808408783Weizhen H, Zhengqiang L, Yuhuan Z, Hua X, Ying Z, Kaitao L, Donghui L, Peng W, Yan M (2014) Using support vector regression to predict PM10 and PM2.5. In: IOP conference series: earth and environmental science, vol 17. IOP. https://doi.org/10.1088/1755-1315/17/1/012268WHO (2016) OMS | La OMS publica estimaciones nacionales sobre la exposición a la contaminación del aire y sus repercusiones para la salud. WHO. http://www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/es/. Accesed 26 Nov 2018Yeganeh N, Shafie MP, Rashidi Y, Kamalan H (2012) Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos Environ 55:357–365. https://doi.org/10.1016/j.atmosenv.2012.02.092Zalakeviciute R, Bastidas M, Buenaño A, Rybarczyk Y (2020) A traffic-based method to predict and map urban air quality. Appl Sci. https://doi.org/10.3390/app10062035Zeng L, Guo J, Wang B, Lv J, Wang Q (2019) Analyzing sustainability of Chinese coal cities using a decision tree modeling approach. Resour Policy 64:101501. https://doi.org/10.1016/j.resourpol.2019.101501Zhan Y, Luo Y, Deng X, Grieneisen M, Zhang M, Di B (2018) Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ Pollut 233:464–473. https://doi.org/10.1016/j.envpol.2017.10.029Zhang Y, Huan Q (2006) Research on the evaluation of sustainable development in Cangzhou city based on neural-network-AHP. In: Proceedings of the fifth international conference on machine learning and cybernetics, vol 2006. pp 3144–3147. https://doi.org/10.1109/ICMLC.2006.258407Zhang Y, Shang W, Wu Y (2009) Research on sustainable development based on neural network. In: 2009 Chinese control and decision conference. IEEE, pp 3273–3276. https://doi.org/10.1109/CCDC.2009.5192476Zhou Y, Chang F-J, Chang L-Ch, Kao I-F, Wang YS (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134–145. https://doi.org/10.1016/j.jclepro.2018.10.24

    Between mediatisation and politicisation: The changing role and position of Whitehall press officers in the age of political spin

    Get PDF
    Despite widespread critiques of ‘political spin’, the way governments engage with the mass media has attracted relatively little empirical attention. There is a small but growing body of research into bureaucracies’ responses to mediatisation from within which have identified tensions between bureaucratic and party political values, but this has not included the United Kingdom. There are concerns that the traditional dividing line between government information and political propaganda has come under increasing pressure as a higher premium is placed on persuasion by both journalists and politicians battling for public attention in an increasingly competitive market. Within Whitehall, the arrival of Labour in 1997 after 18 years in opposition was a watershed for UK government communications, allowing the government to reconfigure its official information service in line with the party political imperative to deploy strategic communications as a defence against increasingly invasive media scrutiny. Public relations, in government as elsewhere, has grown in scale, scope and status, becoming institutionalised and normalised within state bureaucracies, but how has this affected the role, status and influence of the civil servants who conduct media management? Within the system of executive self-regulation of government publicity that is characteristic of Whitehall, government press officers must negotiate a difficult path between the need to inform citizens about the government’s programme, and demands by ministers to deploy privileged information to secure and maintain personal and party advantage in the struggle for power. Taking 1997 as a turning point, and through the voices of the actors who negotiate government news – mainly press officers, but also journalists and special advisers – this article examines the changing role and position of Whitehall press officers in what has become known as the age of political spin, finding that profound and lasting change in the rules of engagement has taken place and is continuing

    Effect of Adenosine A2A Receptor Antagonists and l-DOPA on Hydroxyl Radical, Glutamate and Dopamine in the Striatum of 6-OHDA-Treated Rats

    Get PDF
    A2A adenosine receptor antagonists have been proposed as a new therapy of PD. Since oxidative stress plays an important role in the pathogenesis of PD, we studied the effect of the selective A2A adenosine receptor antagonists 8-(-3-chlorostyryl)caffeine (CSC) and 4-(2-[7-amino-2-(2-furyl)[1,2,4]triazolo[2,3-a][1,3,5]triazin-5-ylamino]ethyl)phenol (ZM 241385) on hydroxyl radical generation, and glutamate (GLU) and dopamine (DA) extracellular level using a microdialysis in the striatum of 6-OHDA-treated rats. CSC (1 mg/kg) and ZM 241385 (3 mg/kg) given repeatedly for 14 days decreased the production of hydroxyl radical and extracellular GLU level, both enhanced by prior 6-OHDA treatment in dialysates from the rat striatum. CSC and ZM 241385 did not affect DA and its metabolites, 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanilic acid (HVA) extracellular levels in the striatum of 6-OHDA-treated rats. l-DOPA (6 mg/kg) given twice daily for two weeks in the presence of benserazide (3 mg/kg) decreased striatal hydroxyl radical and glutamate extracellular level in 6-OHDA-treated rats. At the same time, l-DOPA slightly but significantly increased the extracellular levels of DOPAC and HVA. A combined repeated administration of l-DOPA and CSC or ZM 241385 did not change the effect of l-DOPA on hydroxyl radical production and glutamate extracellular level in spite of an enhancement of extracellular DA level by CSC and elevation of extracellular level of DOPAC and HVA by ZM 241385. The data suggest that the 6-OHDA-induced damage of nigrostriatal DA-terminals is related to oxidative stress and excessive release of glutamate. Administration of l-DOPA in combination with CSC or ZM 241385, by restoring striatal DA-glutamate balance, suppressed 6-OHDA-induced overproduction of hydroxyl radical

    Two Distinct Modes of Hypoosmotic Medium-Induced Release of Excitatory Amino Acids and Taurine in the Rat Brain In Vivo

    Get PDF
    A variety of physiological and pathological factors induce cellular swelling in the brain. Changes in cell volume activate several types of ion channels, which mediate the release of inorganic and organic osmolytes and allow for compensatory cell volume decrease. Volume-regulated anion channels (VRAC) are thought to be responsible for the release of some of organic osmolytes, including the excitatory neurotransmitters glutamate and aspartate. In the present study, we compared the in vivo properties of the swelling-activated release of glutamate, aspartate, and another major brain osmolyte taurine. Cell swelling was induced by perfusion of hypoosmotic (low [NaCl]) medium via a microdialysis probe placed in the rat cortex. The hypoosmotic medium produced several-fold increases in the extracellular levels of glutamate, aspartate and taurine. However, the release of the excitatory amino acids differed from the release of taurine in several respects including: (i) kinetic properties, (ii) sensitivity to isoosmotic changes in [NaCl], and (iii) sensitivity to hydrogen peroxide, which is known to modulate VRAC. Consistent with the involvement of VRAC, hypoosmotic medium-induced release of the excitatory amino acids was inhibited by the anion channel blocker DNDS, but not by the glutamate transporter inhibitor TBOA or Cd2+, which inhibits exocytosis. In order to elucidate the mechanisms contributing to taurine release, we studied its release properties in cultured astrocytes and cortical synaptosomes. Similarities between the results obtained in vivo and in synaptosomes suggest that the swelling-activated release of taurine in vivo may be of neuronal origin. Taken together, our findings indicate that different transport mechanisms and/or distinct cellular sources mediate hypoosmotic medium-induced release of the excitatory amino acids and taurine in vivo

    Identification of Genes Required for Neural-Specific Glycosylation Using Functional Genomics

    Get PDF
    Glycosylation plays crucial regulatory roles in various biological processes such as development, immunity, and neural functions. For example, α1,3-fucosylation, the addition of a fucose moiety abundant in Drosophila neural cells, is essential for neural development, function, and behavior. However, it remains largely unknown how neural-specific α1,3-fucosylation is regulated. In the present study, we searched for genes involved in the glycosylation of a neural-specific protein using a Drosophila RNAi library. We obtained 109 genes affecting glycosylation that clustered into nine functional groups. Among them, members of the RNA regulation group were enriched by a secondary screen that identified genes specifically regulating α1,3-fucosylation. Further analyses revealed that an RNA–binding protein, second mitotic wave missing (Swm), upregulates expression of the neural-specific glycosyltransferase FucTA and facilitates its mRNA export from the nucleus. This first large-scale genetic screen for glycosylation-related genes has revealed novel regulation of fucTA mRNA in neural cells

    High-Density Expression of Ca2+-Permeable ASIC1a Channels in NG2 Glia of Rat Hippocampus

    Get PDF
    NG2 cells, a fourth type of glial cell in the mammalian CNS, undergo reactive changes in response to a wide variety of brain insults. Recent studies have demonstrated that neuronally expressed acid-sensing ion channels (ASICs) are implicated in various neurological disorders including brain ischemia and seizures. Acidosis is a common feature of acute neurological conditions. It is postulated that a drop in pH may be the link between the pathological process and activation of NG2 cells. Such postulate immediately prompts the following questions: Do NG2 cells express ASICs? If so, what are their functional properties and subunit composition? Here, using a combination of electrophysiology, Ca2+ imaging and immunocytochemistry, we present evidence to demonstrate that NG2 cells of the rat hippocampus express high density of Ca2+-permeable ASIC1a channels compared with several types of hippocampal neurons. First, nucleated patch recordings from NG2 cells revealed high density of proton-activated currents. The magnitude of proton-activated current was pH dependent, with a pH for half-maximal activation of 6.3. Second, the current-voltage relationship showed a reversal close to the equilibrium potential for Na+. Third, psalmotoxin 1, a blocker specific for the ASIC1a channel, largely inhibited proton-activated currents. Fourth, Ca2+ imaging showed that activation of proton-activated channels led to an increase of [Ca2+]i. Finally, immunocytochemistry showed co-localization of ASIC1a and NG2 proteins in the hippocampus. Thus the acid chemosensor, the ASIC1a channel, may serve for inducing membrane depolarization and Ca2+ influx, thereby playing a crucial role in the NG2 cell response to injury following ischemia

    Neuroprotection by adenosine in the brain: From A1 receptor activation to A2A receptor blockade

    Get PDF
    Adenosine is a neuromodulator that operates via the most abundant inhibitory adenosine A1 receptors (A1Rs) and the less abundant, but widespread, facilitatory A2ARs. It is commonly assumed that A1Rs play a key role in neuroprotection since they decrease glutamate release and hyperpolarize neurons. In fact, A1R activation at the onset of neuronal injury attenuates brain damage, whereas its blockade exacerbates damage in adult animals. However, there is a down-regulation of central A1Rs in chronic noxious situations. In contrast, A2ARs are up-regulated in noxious brain conditions and their blockade confers robust brain neuroprotection in adult animals. The brain neuroprotective effect of A2AR antagonists is maintained in chronic noxious brain conditions without observable peripheral effects, thus justifying the interest of A2AR antagonists as novel protective agents in neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease, ischemic brain damage and epilepsy. The greater interest of A2AR blockade compared to A1R activation does not mean that A1R activation is irrelevant for a neuroprotective strategy. In fact, it is proposed that coupling A2AR antagonists with strategies aimed at bursting the levels of extracellular adenosine (by inhibiting adenosine kinase) to activate A1Rs might constitute the more robust brain neuroprotective strategy based on the adenosine neuromodulatory system. This strategy should be useful in adult animals and especially in the elderly (where brain pathologies are prevalent) but is not valid for fetus or newborns where the impact of adenosine receptors on brain damage is different

    Interplay between n-3 and n-6 long-chain polyunsaturated fatty acids and the endocannabinoid system in brain protection and repair.

    Get PDF
    The brain is enriched in arachidonic acid (ARA) and docosahexaenoic acid (DHA), long-chain polyunsaturated fatty acids (LCPUFA) of the n-6 and n-3 series, respectively. Both are essential for optimal brain development and function. Dietary enrichment with DHA and other long-chain n-3 PUFA, such as eicosapentaenoic acid (EPA) have shown beneficial effects on learning and memory, neuroinflammatory processes and synaptic plasticity and neurogenesis. ARA, DHA and EPA are precursors to a diverse repertoire of bioactive lipid mediators, including endocannabinoids. The endocannabinoid system comprises cannabinoid receptors, their endogenous ligands, the endocannabinoids, and their biosynthetic and degradation enzymes. Anandamide (AEA) and 2-archidonoylglycerol (2-AG) are the most widely studied endocannabinoids, and are both derived from phospholipid-bound ARA. The endocannabinoid system also has well established roles in neuroinflammation, synaptic plasticity and neurogenesis, suggesting an overlap in the neuroprotective effects observed with these different classes of lipids. Indeed, growing evidence suggests a complex interplay between n-3 and n-6 LCPUFA and the endocannabinoid system. For example, long-term DHA and EPA supplementation reduces AEA and 2-AG levels, with reciprocal increases in levels of the analogous endocannabinoid-like DHA and EPA-derived molecules. This review summarises current evidence of this interplay and discusses the therapeutic potential for brain protection and repair
    corecore