Palmitoylethanolamide exerts neuroprotective effects in mixed neuroglial cultures and organotypic hippocampal slices via peroxisome proliferator-activated receptor-α

<p>Abstract</p> <p>Background</p> <p>In addition to cytotoxic mechanisms directly impacting neurons, β-amyloid (Aβ)-induced glial activation also promotes release of proinflammatory molecules that may self-perpetuate reactive gliosis and damage neighbouring neurons, thus amplifying neuropathological lesions occurring in Alzheimer's disease (AD). Palmitoylethanolamide (PEA) has been studied extensively for its anti-inflammatory, analgesic, antiepileptic and neuroprotective effects. PEA is a lipid messenger isolated from mammalian and vegetable tissues that mimics several endocannabinoid-driven actions, even though it does not bind to cannabinoid receptors. Some of its pharmacological properties are considered to be dependent on the expression of peroxisome proliferator-activated receptors-α (PPARα).</p> <p>Findings</p> <p>In the present study, we evaluated the effect of PEA on astrocyte activation and neuronal loss in models of Aβ neurotoxicity. To this purpose, primary rat mixed neuroglial co-cultures and organotypic hippocampal slices were challenged with Aβ_1-42and treated with PEA in the presence or absence of MK886 or GW9662, which are selective PPARα and PPARγ antagonists, respectively. The results indicate that PEA is able to blunt Aβ-induced astrocyte activation and, subsequently, to improve neuronal survival through selective PPARα activation. The data from organotypic cultures confirm that PEA anti-inflammatory properties implicate PPARα mediation and reveal that the reduction of reactive gliosis subsequently induces a marked rebound neuroprotective effect on neurons.</p> <p>Conclusions</p> <p>In line with our previous observations, the results of this study show that PEA treatment results in decreased numbers of infiltrating astrocytes during Aβ challenge, resulting in significant neuroprotection. PEA could thus represent a promising pharmacological tool because it is able to reduce Aβ-evoked neuroinflammation and attenuate its neurodegenerative consequences.</p

The Evolution of Social Orienting: Evidence from Chicks (Gallus gallus) and Human Newborns

Converging evidence from different species indicates that some newborn vertebrates, including humans, have visual predispositions to attend to the head region of animate creatures. It has been claimed that newborn preferences for faces are domain-relevant and similar in different species. One of the most common criticisms of the work supporting domain-relevant face biases in human newborns is that in most studies they already have several hours of visual experience when tested. This issue can be addressed by testing newly hatched face-na\uefve chicks (Gallus gallus) whose preferences can be assessed prior to any other visual experience with faces

Cannabidiol Reduces Aβ-Induced Neuroinflammation and Promotes Hippocampal Neurogenesis through PPARγ Involvement

Peroxisome proliferator-activated receptor-γ (PPARγ) has been reported to be involved in the etiology of pathological features of Alzheimer's disease (AD). Cannabidiol (CBD), a Cannabis derivative devoid of psychomimetic effects, has attracted much attention because of its promising neuroprotective properties in rat AD models, even though the mechanism responsible for such actions remains unknown. This study was aimed at exploring whether CBD effects could be subordinate to its activity at PPARγ, which has been recently indicated as its putative binding site. CBD actions on β-amyloid-induced neurotoxicity in rat AD models, either in presence or absence of PPAR antagonists were investigated. Results showed that the blockade of PPARγ was able to significantly blunt CBD effects on reactive gliosis and subsequently on neuronal damage. Moreover, due to its interaction at PPARγ, CBD was observed to stimulate hippocampal neurogenesis. All these findings report the inescapable role of this receptor in mediating CBD actions, here reported

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

[EN] Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with nonimmersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject's arousal and valence perception. The model's accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.This work was supported by the Ministerio de Economia y Competitividad. Spain (Project TIN2013-45736-R).Marín-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres Provinciale, J.; Llinares Millán, MDC.; Scilingo, EP.; Alcañiz Raya, ML.... (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports. 8:1-15. https://doi.org/10.1038/s41598-018-32063-4S1158Picard, R. W. Affective computing. (MIT press, 1997).Picard, R. W. Affective Computing: Challenges. Int. J. Hum. Comput. Stud. 59, 55–64 (2003).Jerritta, S., Murugappan, M., Nagarajan, R. & Wan, K. Physiological signals based human emotion Recognition: a review. Signal Process. its Appl. (CSPA), 2011 IEEE 7th Int. Colloq. 410–415, https://doi.org/10.1109/CSPA.2011.5759912 (2011).Harms, M. B., Martin, A. & Wallace, G. L. Facial emotion recognition in autism spectrum disorders: A review of behavioral and neuroimaging studies. Neuropsychol. Rev. 20, 290–322 (2010).Koolagudi, S. G. & Rao, K. S. Emotion recognition from speech: A review. Int. J. Speech Technol. 15, 99–117 (2012).Gross, J. J. & Levenson, R. W. Emotion elicitation using films. Cogn. Emot. 9, 87–108 (1995).Lindal, P. J. & Hartig, T. Architectural variation, building height, and the restorative quality of urban residential streetscapes. J. Environ. Psychol. 33, 26–36 (2013).Ulrich, R. View through a window may influence recovery from surgery. Science (80-.). 224, 420–421 (1984).Fernández-Caballero, A. et al. Smart environment architecture for emotion detection and regulation. J. Biomed. Inform. 64, 55–73 (2016).Ekman, P. Basic Emotions. Handbook of cognition and emotion 45–60, https://doi.org/10.1017/S0140525X0800349X (1999).Posner, J., Russell, J. A. & Peterson, B. S. The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17, 715–34 (2005).Russell, J. A. & Mehrabian, A. Evidence for a three-factor theory of emotions. J. Res. Pers. 11, 273–294 (1977).Calvo, R. A. & D’Mello, S. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1, 18–37 (2010).Valenza, G. et al. Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374, 20150176 (2016).Valenza, G., Lanata, A. & Scilingo, E. P. The role of nonlinear dynamics in affective valence and arousal recognition. IEEE Trans. Affect. Comput. 3, 237–249 (2012).Valenza, G., Citi, L., Lanatá, A., Scilingo, E. P. & Barbieri, R. Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics. Sci. Rep. 4, 4998 (2014).Valenza, G. et al. Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J. Biomed. Heal. Informatics 18, 1625–1635 (2014).Piwek, L., Ellis, D. A., Andrews, S. & Joinson, A. The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Med. 13, 1–9 (2016).Xu, J., Mitra, S., Van Hoof, C., Yazicioglu, R. & Makinwa, K. A. A. Active Electrodes for Wearable EEG Acquisition: Review and Electronics Design Methodology. IEEE Rev. Biomed. Eng. 3333, 1–1 (2017).Kumari, P., Mathew, L. & Syal, P. Increasing trend of wearables and multimodal interface for human activity monitoring: A review. Biosens. Bioelectron. 90, 298–307 (2017).He, C., Yao, Y. & Ye, X. An Emotion Recognition System Based on Physiological Signals Obtained by Wearable Sensors. In Wearable Sensors and Robots: Proceedings of International Conference on Wearable Sensors and Robots 2015 (eds Yang, C., Virk, G. S. & Yang, H.) 15–25. https://doi.org/10.1007/978-981-10-2404-7_2 (Springer Singapore, 2017).Nakisa, B., Rastgoo, M. N., Tjondronegoro, D. & Chandran, V. Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Syst. Appl. 93, 143–155 (2018).Kory Jacqueline, D. & Sidney, K. Affect Elicitation for Affective Computing. In The Oxford Handbook of Affective Computing 371–383 (2014).Ekman, P. The directed facial action task. In Handbook of emotion elicitation and assessment 47–53 (2007).Harmon-Jones, E., Amodio, D. M. & Zinner, L. R. Social psychological methods of emotion elicitation. Handb. Emot. elicitation Assess. 91–105, https://doi.org/10.2224/sbp.2007.35.7.863 (2007)Roberts, N. A., Tsai, J. L. & Coan, J. A. Emotion elicitation using dyadic interaction task. Handbook of Emotion Elicitation and Assessment 106–123 (2007).Nardelli, M., Valenza, G., Greco, A., Lanata, A. & Scilingo, E. P. Recognizing emotions induced by affective sounds through heart rate variability. IEEE Trans. Affect. Comput. 6, 385–394 (2015).Kim, J. Emotion Recognition Using Speech and Physiological Changes. Robust Speech Recognit. Underst. 265–280 (2007).Soleymani, M., Pantic, M. & Pun, T. Multimodal emotion recognition in response to videos (Extended abstract). 2015 Int. Conf. Affect. Comput. Intell. Interact. ACII 2015 3, 491–497 (2015).Baños, R. M. et al. Immersion and Emotion: Their Impact on the Sense of Presence. CyberPsychology Behav. 7, 734–741 (2004).Giglioli, I. A. C., Pravettoni, G., Martín, D. L. S., Parra, E. & Raya, M. A. A novel integrating virtual reality approach for the assessment of the attachment behavioral system. Front. Psychol. 8, 1–7 (2017).Marín-Morales, J., Torrecilla, C., Guixeres, J. & Llinares, C. Methodological bases for a new platform for the measurement of human behaviour in virtual environments. DYNA 92, 34–38 (2017).Vince, J. Introduction to virtual reality. (Media, Springer Science & Business, 2004).Alcañiz, M., Baños, R., Botella, C. & Rey, B. The EMMA Project: Emotions as a Determinant of Presence. PsychNology J. 1, 141–150 (2003).Vecchiato, G. et al. Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural environments. Cogn. Process. 16, 425–429 (2015).Slater, M. & Wilbur, S. A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence Teleoperators Virtual Environ. 6, 603–616 (1997).Riva, G. et al. Affective Interactions Using Virtual Reality: The Link between Presence and Emotions. CyberPsychology Behav. 10, 45–56 (2007).Baños, R. M. et al Changing induced moods via virtual reality. In International Conference on Persuasive Technology (ed. Springer, Berlin, H.) 7–15, https://doi.org/10.1007/11755494_3 (2006).Baños, R. M. et al. Positive mood induction procedures for virtual environments designed for elderly people. Interact. Comput. 24, 131–138 (2012).Gorini, A. et al. Emotional Response to Virtual Reality Exposure across Different Cultures: The Role of the AttributionProcess. CyberPsychology Behav. 12, 699–705 (2009).Gorini, A., Capideville, C. S., De Leo, G., Mantovani, F. & Riva, G. The Role of Immersion and Narrative in Mediated Presence: The Virtual Hospital Experience. Cyberpsychology, Behav. Soc. Netw. 14, 99–105 (2011).Chirico, A. et al. Effectiveness of Immersive Videos in Inducing Awe: An Experimental Study. Sci. Rep. 7, 1–11 (2017).Blascovich, J. et al. Immersive Virtual Environment Technology as a Methodological Tool for Social Psychology. Psychol. Inq. 7965, 103–124 (2012).Peperkorn, H. M., Alpers, G. W. & Mühlberger, A. Triggers of fear: Perceptual cues versus conceptual information in spider phobia. J. Clin. Psychol. 70, 704–714 (2014).McCall, C., Hildebrandt, L. K., Bornemann, B. & Singer, T. Physiophenomenology in retrospect: Memory reliably reflects physiological arousal during a prior threatening experience. Conscious. Cogn. 38, 60–70 (2015).Hildebrandt, L. K., Mccall, C., Engen, H. G. & Singer, T. Cognitive flexibility, heart rate variability, and resilience predict fine-grained regulation of arousal during prolonged threat. Psychophysiology 53, 880–890 (2016).Notzon, S. et al. Psychophysiological effects of an iTBS modulated virtual reality challenge including participants with spider phobia. Biol. Psychol. 112, 66–76 (2015).Amaral, C. P., Simões, M. A., Mouga, S., Andrade, J. & Castelo-Branco, M. A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study. J. Neurosci. Methods 290, 105–115 (2017).Eudave, L. & Valencia, M. Physiological response while driving in an immersive virtual environment. 2017 IEEE 14th Int. Conf. Wearable Implant. Body Sens. Networks 145–148, https://doi.org/10.1109/BSN.2017.7936028 (2017).Sharma, G. et al. Influence of landmarks on wayfinding and brain connectivity in immersive virtual reality environment. Front. Psychol. 8, 1–12 (2017).Bian, Y. et al. A framework for physiological indicators of flow in VR games: construction and preliminary evaluation. Pers. Ubiquitous Comput. 20, 821–832 (2016).Egan, D. et al. An evaluation of Heart Rate and Electrodermal Activity as an Objective QoE Evaluation method for Immersive Virtual Reality Environments. 3–8, https://doi.org/10.1109/QoMEX.2016.7498964 (2016).Meehan, M., Razzaque, S., Insko, B., Whitton, M. & Brooks, F. P. Review of four studies on the use of physiological reaction as a measure of presence in stressful virtual environments. Appl. Psychophysiol. Biofeedback 30, 239–258 (2005).Higuera-Trujillo, J. L., López-Tarruella Maldonado, J. & Llinares Millán, C. Psychological and physiological human responses to simulated and real environments: A comparison between Photographs, 360° Panoramas, and Virtual Reality. Appl. Ergon. 65, 398–409 (2016).Felnhofer, A. et al. Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios. Int. J. Hum. Comput. Stud. 82, 48–56 (2015).Anderson, A. P. et al. Relaxation with Immersive Natural Scenes Presented Using Virtual Reality. Aerosp. Med. Hum. Perform. 88, 520–526 (2017).Higuera, J. L. et al. Emotional cartography in design: A novel technique to represent emotional states altered by spaces. In D and E 2016: 10th International Conference on Design and Emotion 561–566 (2016).Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001).Bradley, M. M. & Lang, P. J. Measuring emotion: The self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25, 49–59 (1994).Lang, P. J., Bradley, M. M. & Cuthbert, B. N. International Affective Picture System (IAPS): Technical Manual and Affective Ratings. NIMH Cent. Study Emot. Atten. 39–58, https://doi.org/10.1027/0269-8803/a000147 (1997).Nanda, U., Pati, D., Ghamari, H. & Bajema, R. Lessons from neuroscience: form follows function, emotions follow form. Intell. Build. Int. 5, 61–78 (2013).Russell, J. A. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980).Sejima, K. Kazuyo Sejima. 1988–1996. El Croquis 15 (1996).Ochiai, H. et al. Physiological and Psychological Effects of Forest Therapy on Middle-Aged Males with High-NormalBlood Pressure. Int. J. Environ. Res. Public Health 12, 2532–2542 (2015).Noguchi, H. & Sakaguchi, T. Effect of illuminance and color temperature on lowering of physiological activity. Appl. Hum. Sci. 18, 117–123 (1999).Küller, R., Mikellides, B. & Janssens, J. Color, arousal, and performance—A comparison of three experiments. Color Res. Appl. 34, 141–152 (2009).Yildirim, K., Hidayetoglu, M. L. & Capanoglu, A. Effects of interior colors on mood and preference: comparisons of two living rooms. Percept. Mot. Skills 112, 509–524 (2011).Hogg, J., Goodman, S., Porter, T., Mikellides, B. & Preddy, D. E. Dimensions and determinants of judgements of colour samples and a simulated interior space by architects and non‐architects. Br. J. Psychol. 70, 231–242 (1979).Jalil, N. A., Yunus, R. M. & Said, N. S. Environmental Colour Impact upon Human Behaviour: A Review. Procedia - Soc. Behav. Sci. 35, 54–62 (2012).Jacobs, K. W. & Hustmyer, F. E. Effects of four psychological primary colors on GSR, heart rate and respiration rate. Percept. Mot. Skills 38, 763–766 (1974).Jin, H. R., Yu, M., Kim, D. W., Kim, N. G. & Chung, A. S. W. Study on Physiological Responses to Color Stimulation. In International Association of Societies of Design Research (ed. Poggenpohl, S.) 1969–1979 (Korean Society of Design Science, 2009).Vartanian, O. et al. Impact of contour on aesthetic judgments and approach-avoidance decisions in architecture. Proc. Natl. Acad. Sci. 110, 1–8 (2013).Tsunetsugu, Y., Miyazaki, Y. & Sato, H. Visual effects of interior design in actual-size living rooms on physiological responses. Build. Environ. 40, 1341–1346 (2005).Stamps, A. E. Physical Determinants of Preferences for Residential Facades. Environ. Behav. 31, 723–751 (1999).Berlyne, D. E. Novelty, Complexity, and Hedonic Value. Percept. Psychophys. 8, 279–286 (1970).Krueger, R. A. & Casey, M. Focus groups: a practical guide for applied research. (Sage Publications, 2000).Acharya, U. R., Joseph, K. P., Kannathal, N., Lim, C. M. & Suri, J. S. Heart rate variability: A review. Med. Biol. Eng. Comput. 44, 1031–1051 (2006).Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta-aho, P. O. & Karjalainen, P. A. Kubios HRV - Heart rate variability analysis software. Comput. Methods Programs Biomed. 113, 210–220 (2014).Pan, J. & Tompkins, W. J. A real-time QRS detection algorithm. Biomed. Eng. IEEE Trans. 1, 230–236 (1985).Tarvainen, M. P., Ranta-aho, P. O. & Karjalainen, P. A. An advanced detrending method with application to HRV analysis. IEEE Trans. Biomed. Eng. 49, 172–175 (2002).Valenza, G. et al. Predicting Mood Changes in Bipolar Disorder Through HeartbeatNonlinear Dynamics. IEEE J. Biomed. Heal. Informatics 20, 1034–1043 (2016).Pincus, S. & Viscarello, R. Approximate Entropy A regularity measure for fetal heart rate analysis. Obstet. Gynecol. 79, 249–255 (1992).Richman, J. & Moorman, J. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Hear. Circ Physiol 278, H2039–H2049 (2000).Peng, C.-K., Havlin, S., Stanley, H. E. & Goldberger, A. L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82–87 (1995).Grassberger, P. & Procaccia, I. Characterization of strange attractors. Phys. Rev. Lett. 50, 346–349 (1983).Delorme, A. & Makeig, S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).Colomer Granero, A. et al. A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Front. Comput. Neurosci. 10, 1–14 (2016).Kober, S. E., Kurzmann, J. & Neuper, C. Cortical correlate of spatial presence in 2D and 3D interactive virtual reality: An EEG study. Int. J. Psychophysiol. 83, 365–374 (2012).Hyvärinen, A. & Oja, E. Independent component analysis: Algorithms and applications. Neural Networks 13, 411–430 (2000).Welch, P. D. The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Aver. aging Over Short, Modified Periodograms. IEEE Trans. AUDIO Electroacoust. 15, 70–73 (1967).Mormann, F., Lehnertz, K., David, P. & Elger, E. C. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Phys. D Nonlinear Phenom. 144, 358–369 (2000).Jolliffe, I. T. Principal Component Analysis, Second Edition. Encycl. Stat. Behav. Sci. 30, 487 (2002).Schöllkopf, B., Smola, A. J., Williamson, R. C. & Bartlett, P. L. New support vector algorithms. Neural Comput 12, 1207–1245 (2000).Yan, K. & Zhang, D. Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors Actuators, B Chem. 212, 353–363 (2015).Chang, C.-C. & Lin, C.-J. Libsvm: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).Lewis, P. A., Critchley, H. D., Rotshtein, P. & Dolan, R. J. Neural correlates of processing valence and arousal in affective words. Cereb. Cortex 17, 742–748 (2007).McCall, C., Hildebrandt, L. K., Hartmann, R., Baczkowski, B. M. & Singer, T. Introducing the Wunderkammer as a tool for emotion research: Unconstrained gaze and movement patterns in three emotionally evocative virtual worlds. Comput. Human Behav. 59, 93–107 (2016).Blake, J. & Gurocak, H. B. Haptic glove with MR brakes for virtual reality. IEEE/ASME Trans. Mechatronics 14, 606–615 (2009).Heydarian, A. et al. Immersive virtual environments versus physical built environments: A benchmarking study for building design and user-built environment explorations. Autom. Constr. 54, 116–126 (2015).Kuliga, S. F., Thrash, T., Dalton, R. C. & Hölscher, C. Virtual reality as an empirical research tool - Exploring user experience in a real building and a corresponding virtual model. Comput. Environ. Urban Syst. 54, 363–375 (2015).Yeom, D., Choi, J.-H. & Zhu, Y. Investigation of the Physiological Differences between Immersive Virtual Environment and Indoor Enviorment in a Building. Indoor adn Built Enviornment 0, Accept (2017).Combrisson, E. & Jerbi, K. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods 250, 126–136 (2015).He, C., Yao, Y. & Ye, X. An Emotion Recognition System Based on Physiological Signals Obtained by Wearable Sensors. In Wearable Sensors and Robots: Proceedings of International Conference on Wearable Sensors and Robots 2015 (eds. Yang, C., Virk, G. S. & Yang, H.) 15–25, https://doi.org/10.1007/978-981-10-2404-7_2 (Springer Singapore, 2017)

Increasing abdominal pressure with and without PEEP: effects on intra-peritoneal, intra-organ and intra-vascular pressures

Intra-organ and intra-vascular pressures can be used to estimate intra-abdominal pressure. The aim of this prospective, interventional study was to assess the effect of PEEP on the accuracy of pressure estimation at different measurement sites in a model of increased abdominal pressure

Increasing abdominal pressure with and without PEEP: effects on intra-peritoneal, intra-organ and intra-vascular pressures

Intra-organ and intra-vascular pressures can be used to estimate intra-abdominal pressure. The aim of this prospective, interventional study was to assess the effect of PEEP on the accuracy of pressure estimation at different measurement sites in a model of increased abdominal pressure

Acute effects of intracranial hypertension and ARDS on pulmonary and neuronal damage: a randomized experimental study in pigs

Abstract PURPOSE: To determine reciprocal and synergistic effects of acute intracranial hypertension and ARDS on neuronal and pulmonary damage and to define possible mechanisms. METHODS: Twenty-eight mechanically ventilated pigs were randomized to four groups of seven each: control; acute intracranial hypertension (AICH); acute respiratory distress syndrome (ARDS); acute respiratory distress syndrome in combination with acute intracranial hypertension (ARDS + AICH). AICH was induced with an intracranial balloon catheter and the inflation volume was adjusted to keep intracranial pressure (ICP) at 30-40 cmH2O. ARDS was induced by oleic acid infusion. Respiratory function, hemodynamics, extravascular lung water index (ELWI), lung and brain computed tomography (CT) scans, as well as inflammatory mediators, S100B, and neuronal serum enolase (NSE) were measured over a 4-h period. Lung and brain tissue were collected and examined at the end of the experiment. RESULTS: In both healthy and injured lungs, AICH caused increases in NSE and TNF-alpha plasma concentrations, extravascular lung water, and lung density in CT, the extent of poorly aerated (dystelectatic) and atelectatic lung regions, and an increase in the brain tissue water content. ARDS and AICH in combination induced damage in the hippocampus and decreased density in brain CT. CONCLUSIONS: AICH induces lung injury and also exacerbates pre-existing damage. Increased extravascular lung water is an early marker. ARDS has a detrimental effect on the brain and acts synergistically with intracranial hypertension to cause histological hippocampal damage

Defective Sphingosine-1-phosphate metabolism is a druggable target in Huntington's disease

Huntington's disease is characterized by a complex and heterogeneous pathogenic profile. Studies have shown that disturbance in lipid homeostasis may represent a critical determinant in the progression of several neurodegenerative disorders. The recognition of perturbed lipid metabolism is only recently becoming evident in HD. In order to provide more insight into the nature of such a perturbation and into the effect its modulation may have in HD pathology, we investigated the metabolism of Sphingosine-1-phosphate (S1P), one of the most important bioactive lipids, in both animal models and patient samples. Here, we demonstrated that S1P metabolism is significantly disrupted in HD even at early stage of the disease and importantly, we revealed that such a dysfunction represents a common denominator among multiple disease models ranging from cells to humans through mouse models. Interestingly, the in vitro anti-apoptotic and the pro-survival actions seen after modulation of S1P-metabolizing enzymes allows this axis to emerge as a new druggable target and unfolds its promising therapeutic potential for the development of more effective and targeted interventions against this incurable condition