Hyperglycaemia and apoptosis of microglial cells in human septic shock

International audienceIntroductionThe effect of hyperglycaemia on the brain cells of septic shock patients is unknown. The objective of this study was to evaluate the relationship between hyperglycaemia and apoptosis in the brains of septic shock patients.MethodsIn a prospective study of 17 patients who died from septic shock, hippocampal tissue was assessed for neuronal ischaemia, neuronal and microglial apoptosis, neuronal Glucose Transporter (GLUT) 4, endothelial inducible Nitric Oxide Synthase (iNOS), microglial GLUT5 expression, microglial and astrocyte activation. Blood glucose (BG) was recorded five times a day from ICU admission to death. Hyperglycaemia was defined as a BG 200 mg/dL g/l and the area under the BG curve (AUBGC) > 2 g/l was assessed.ResultsMedian BG over ICU stay was 2.2 g/l. Neuronal apoptosis was correlated with endothelial iNOS expression (rho = 0.68, P = 0.04), while microglial apoptosis was associated with AUBGC > 2 g/l (rho = 0.70; P = 0.002). Neuronal and microglial apoptosis correlated with each other (rho = 0.69, P = 0.006), but neither correlated with the duration of septic shock, nor with GLUT4 and 5 expression. Neuronal apoptosis and ischaemia tended to correlate with duration of hypotension.ConclusionsIn patients with septic shock, neuronal apoptosis is rather associated with iNOS expression and microglial apoptosis with hyperglycaemia, possibly because GLUT5 is not downregulated. These data provide a mechanistic basis for understanding the neuroprotective effects of glycemic control

Changes in CRH and ACTH Synthesis during Experimental and Human Septic Shock

Context The mechanisms of septic shock-associated adrenal insufficiency remain unclear. This study aimed at investigating the synthesis of corticotropin-releasing hormone (CRH) and vasopressin (AVP) by parvocellular neurons and the antehypophyseal expression of ACTH in human septic shock and in an experimental model of sepsis. Objective To test the hypothesis that ACTH secretion is decreased secondarily to alteration of CRH or AVP synthesis, we undertook a neuropathological study of the antehypophyseal system in patients who had died from septic shock and rats with experimental faecal peritonitis. Methods Brains obtained in 9 septic shock patients were compared to 10 nonseptic patients (controls). Parvocellular expression of AVP and CRH mRNA were evaluated by in situ hybridization. Antehypophyseal expression of ACTH, vasopressin V1b and CRH R1 receptors and parvocellular expression of iNOS in the PVN were evaluated by immunohistochemistry. The same experiments were carried out in a fecal peritonitis-induced model of sepsis. Data from septic rats with (n = 6) or without (n = 10) early death were compared to sham-operated (n = 8) animals. Results In patients and rats, septic shock was associated with a decreased expression of ACTH, unchanged expression of V1B receptor, CRHR1 and AVP mRNA, and increased expression of parvocellular iNOS compared to controls. Septic shock was also characterized by an increased expression of CRH mRNA in rats but not in patients, who notably had a greater duration of septic shock. Conclusion The present study suggests that in humans and in rats, septic shock is associated with decreased ACTH synthesis that is not compensated by its two natural secretagogues, AVP and CRH. One underlying mechanism might be increased expression of iNOS in hypothalamic parvocellular neurons

How valuable are your customers in the brand value co-creation process? The development of a Customer Co-Creation Value (CCCV) scale.

Despite an increasing amount of research on co-creation of value, in general, research on brand value co-creation remains limited. Particularly, how much value customers contribute to the brand value co-creation process remains unclear. This research develops in a series of eight studies the Customer Co-Creation Value (CCCV) measurement scale that helps firms assess the value of customers in the brand value co-creation process. The findings reveal that CCCV is a multidimensional construct consisting of two higher-order factors and seven dimensions: customer-owned resources (including brand knowledge, brand skills, brand creativity, and brand connectedness) and customer motivation (comprising brand passion, brand trust, and brand commitment). Further, the CCCV scale reliably and validly gauges the value customers contribute to a firm's brand. The CCCV framework helps marketing managers understand how customers can contribute to a firm's brand value cocreation efforts and how much value customers contribute to a brand in the co-creation process

"Etude algorithmique préliminaire" à la conception du module eMouve au sein d’ActivCollector : détection des activités physiques en conditions habituelles de vie à l’aide de smartphones

Obesity and sedentary lifestyles are constantly increasing for many years. INRA currently uses expensive and/or complex methods in order to estimate energy expenditure in controlled conditions in laboratory or in free-living conditions. The aim of the project is to recognize low to moderate physical activities and to estimate volunteers’energy expenditure in free-living conditions. This is possible thanks to smartphones, well-spread into the population, which are equipped of a triaxial acceleromete. The activities studied in this project are: walking, running, walking up stairs, walking down stairs, sitting, standing and driving a car. To reach this goal innovative mathematical tools have been introduced and new algorithms are proposed. The obtained recognition percentages are satisfactory compared to the results of other publications which often used several research-accelerometers unavailable to the general public. Car driving was the activity the most badly recognized (36%) and running the best recognized (97%). On average, the model recognized 79.7% of the activities made by the volunteer. Moreover, the estimation of the energy expenditure seemed to be quite close to the estimations of the reference sensors Actiheart (less than 2% of difference) and farther from SenseWear Armband estimation (about 17% of difference).The next step consists in testing the model on the data of ten volunteers and improving it.L’obésité et la sédentarité ne cessent de croître depuis plusieurs années. L’INRA dispose actuellement de méthodes coûteuses et/ou lourdes à mettre en oeuvre afin d’estimer la dépense énergétique en conditions contrôlée de laboratoire ou en conditions habituelles de vie. L’objectif de ce projet est de pouvoir reconnaître les activités physiques de faible et moyenne intensité et d’estimer la dépense énergétique des volontaires en conditions habituelles de vie. Ceci est possible grâce aux smartphones, largement répandus dans la population, qui sont équipés d'accéléromètres. Les activités étudiées sont la marche, la course, la montée et la descente d’escaliers, les postures « assis » et « debout statique » ainsi que la conduite automobile. Pour atteindre cet objectif il a été proposé des méthodes d'analyse des flux de données des accéléromètres ainsi que de nouveaux algorithmes. Les pourcentages de reconnaissances des activités obtenus sont satisfaisants au regard des résultats des autres publications, lesquelles utilisent souvent plusieurs accéléromètres non-disponibles auprès du grand public. La conduite automobile est l’activité que le modèle a le plus de mal à reconnaître (36%), tandis que la course est reconnue à 97%. En moyenne, le modèle reconnaît 79,7% des activités réalisées. D’autre part, notre estimation de la dépense énergétique semble assez proche des estimations des capteurs de référence Actiheart (moins de 2% d’écart) et plus éloigné de celle du SenseWear Armband (environ 17%).Le modèle devra prochainement être affiné et testé sur dix volontaires en conditions contrôlées

Evaluation of physical activity intensities and energy expenditure in overweight and obese adults

Original Article: Open AccessEvaluation of physical activity intensities and energy expenditure in overweight and obese adult

Comparison of Active and Sedentary Bout Lengths in Normal and Overweight Adults using eMouverecherche

International audienceBackgroundPhysical inactivity and long sedentary time are common in obese people. The eMouveRecherche application was developed to provide accurate minute-by-minute classification of physical activity in light, moderate, vigorous intensity and sedentary bouts. The aim was to compare the frequency and length of bouts in Normal-Weight (NW) and Overweight (OW) adults.MethodsFifty-seven adult participants either normal weight or overweight wore a smartphone with the eMouve application for the entire waking period of the day. The continuous 1-5, 5-15, 15-30, 30-60 and higher than 60-minute bouts for each behavior were counted.ResultsThe total number of bouts was higher in NW than in OW (12.4 vs. 9.8 bouts.h-1, p < 0.001). The breakdown of immobile and active bouts according to their length was different in the two groups. The NW had a significantly higher percentage of brief immobile bouts (1-5 min) (65.2% vs. 49.7%), while OW had a significantly higher percentage of 5-15 min (26.8% vs. 19.1%) and 15-30 min sedentary bouts (11.8% vs. 8.0%). The 1-5 min bouts of light-intensity activity were statistically more frequent in OW (93.6% vs. 83.5%), whereas bouts of 5-15 min (15.1% vs. 6.4%) and 15-30 min bouts (1.3% vs. 0%) were more common in NW.ConclusionThe frequency of both immobile and light-intensity activity bouts was lower in OW, whereas the duration of bouts was respectively longer for immobile behavior and shorter for light-intensity activity, resulting in a continuous sedentary pattern with few active breaks. The overweight appears to be a barrier to the spontaneous practice of light-intensity physical activity

A smartphone application to evaluate energy expenditure and duration of moderate-intensity activities

National audienceObesity and sedentary lifestyles are constantly increasing for many years. Most of the studies use expensive and / or complex methods in order to estimate energy expenditure in controlled conditions or in free-living conditions. The aim of the project is to recognize low to moderate physical activities and to estimate volunteers’ energy expenditure in free-living conditions by using smartphones, equipped with tri-axial accelerometers. The project consists of 2 main steps: 1) the creation of energy expenditure estimation functions; 2) their validation over 18 volunteers. Twelve volunteers have first been equipped with an Android smartphone (a Samsung Galaxy Xcover) and with two sensors used in research to estimate energy expenditure (Armband and Actiheart) during several controlled activities such as sitting, standing, walking and transportation. The accelerometry data collected by the smartphone allowed creating functions of energy expenditure prediction. Then, 6 other participants had the smartphone and sensors during one day in free-living conditions. Their data allowed testing the functions. Two functions have been created during the research study: * f(AEDES), which recognizes physical activities in order to estimate energy expenditure. * f(NRJSI), which uses accelerometry data for estimating energy expenditure directly. The results of our study showed that the difference between the energy expenditure estimation provided by f(AEDES) and f(NRJSI) were 14.03% and 9.60% from Armband, respectively. Moreover, physical activity categorization was good for categories "immobile activities" (less than 2 METs) and "light and moderate activities" (from 2 to 6 METs). However, it was not possible to conclude over the "vigorous activities" category (6 METs or more), since there were not enough data. The next step will consist in recruiting 24 new volunteers for validating this study’s results. Indeed, the functions have been optimized over the data of all the 18 volunteers, so it is normal to obtain low error rates for this population. New volunteers will allow us to estimate performances of the functions with a population that has not been used to parameter them. Keywords: obesity, sedentary lifestyles, low to moderate physical activities, energy expenditure, smartphones, accelerometers, free-living condition

"Etude des algorithmes" du module eMouve en conditions contrôlées : étape préalable à la détection des activités physiques en conditions habituelles de vie à l'aide de smartphones

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The eMouveRecherche application competes with research devices to evaluate energy expenditure, physical activity and still time in free-living conditions

Special thanks to the volunteers for their participation in this study and to Gail Wagman for correcting the English. This study was carried out in the context of research collaboration n°101923-581 supported by INRA and AlmerysThe proliferation of smartphones is creating new opportunities to monitor and interact with human subjects in free-living conditions since smartphones are familiar to large segments of the population and facilitate data collection, transmission and analysis. From accelerometry data collected by smartphones, the present work aims to estimate time spent in different activity categories and the energy expenditure in free-living conditions. Our research encompasses the definition of an energy-saving function (PredEE) considering four physical categories of activities (still, light, moderate and vigorous), their duration and metabolic cost (MET). To create an efficient discrimination function, the method consists of classifying accelerometry-transformed signals into categories and of associating each category with corresponding Metabolic Equivalent Tasks. The performance of the PredEE function was compared with two previously published functions (f(eta,d)aedes,f(eta,d)nrjsi), and with two dedicated sensors (Armband(R) and Actiheart(R)) in free-living conditions over a 12-hours monitoring period using 30 volunteers. Compared to the two previous functions, PredEE was the only one able to provide estimations of time spent in each activity category. In relative value, all the activity categories were evaluated similarly to those given by Armband(R). Compared to Actiheart(R), the function underestimated still activities by 10.1% and overestimated light- and moderate-intensity activities by 7.9% and 4.2%, respectively. The total energy expenditure error produced by PredEE compared to Armband(R) was lower than those given by the two previous functions (5.7% vs 14.1% and 17.0%). PredEE provides the user with an accurate physical activity feedback which should help self-monitoring in free-living condition

eMouve : la 1ère application scientifique sur smartphone pour promouvoir les activités de faible intensité et prévenir les maladies chroniques non transmissibles

L’inactivité physique et les comportements sédentaires sont impliqués dans le développement des maladies chroniques. Le but de cette étude est de comparer la quantité et la répartition des activités physiques et de la sédentarité dans 2 populations d’adultes normo-pondérés (NP) et en surpoids (SP). Les comportements ont été évalués en utilisant l’application eMouve qui collecte des données d’accélérométrie. Les algorithmes implémentés sur la plateforme ActivCollector discriminent précisément les comportements sédentaires et actifs en 4 catégories : immobilité, activité d’intensité légère, modérée et vigoureuse. Chez les personnes en surpoids, les comportements sédentaires durent plus longtemps (81.4% vs. 65 % de la période éveillée), à l’inverse le temps passé en activité d’intensité légère est plus bref (15.4% vs 29.5 %). Les périodes de sédentarité des normo-pondéraux sont brèves (1-5 min) et fréquentes, alors que celle des personnes en surpoids sont plus longues (15-30 min). Les périodes d’activité légère de 1-5 min sont plus fréquentes chez les SP alors que celles de 5-15 et 15-30 min sont plus représentées chez les NP. Ainsi l’évaluation des comportements à l’aide d’une application mobile permet de différencier les 2 populations au regard de leurs profils de sédentarité et d’activité physique d’intensité légère