Molecular mechanisms of ischemia and glutamate excitotoxicity

Excitotoxicity is classically defined as the neuronal damage caused by the excessive release of glutamate, and subsequent activation of excitatory plasma membrane receptors. In the mammalian brain, this phenomenon is mainly driven by excessive activation of glutamate receptors (GRs). Excitotoxicity is common to several chronic disorders of the Central Nervous System (CNS) and is considered the primary mechanism of neuronal loss of function and cell death in acute CNS diseases (e.g. ischemic stroke). Multiple mechanisms and pathways lead to excitotoxic cell damage including pro-death signaling cascade events downstream of glutamate receptors, calcium (Ca2+) overload, oxidative stress, mitochondrial impairment, excessive glutamate in the synaptic cleft as well as altered energy metabolism. Here, we review the current knowledge on the molecular mechanisms that underlie excitotoxicity, emphasizing the role of Nicotinamide Adenine Dinucleotide (NAD) metabolism. We also discuss novel and promising therapeutic strategies to treat excitotoxicity, highlighting recent clinical trials. Finally, we will shed light on the ongoing search for stroke biomarkers, an exciting and promising field of research, which may improve stroke diagnosis, prognosis and allow better treatment options.info:eu-repo/semantics/publishedVersio

Accuracy of Fitbit Activity Trackers During Walking in a Controlled Setting

Activity trackers are widely used to measure daily physical activity. Many devices have been shown to measure steps more accurately at higher intensities, however, it is also important to determine the accuracy of these new devices at measuring steps while walking at a pace similar to that used during most daily activities. PURPOSE: To assess the accuracy of 6 popular activity trackers at measuring steps while walking on a treadmill. METHODS: Twenty-six college students (Mean±SD; 22.1±3.7yrs; 25.1±4.0kg/m2; 13 male) walked 500 steps at 3mph on a treadmill while wearing 6 different activity trackers (Pedometer, Fitbit Blaze, Charge HR, Alta, Flex, Zip, One). The Charge HR was placed two fingers above the right wrist while the Flex was next to the wrist bone. The Blaze was placed two fingers above the left wrist while the Alta was next to the wrist bone. The Fitbit Zip and the One were aligned with the hipbone on the left and right waistband respectively. Steps were counted by a trained researcher using a hand tally counter. Missing values were replaced with the mean value for that device. Step counts were correlated between Fitbit devices and the pedometer and tally counter using Pearson correlations. Significance was set at p\u3c0.05. Mean bias scores were calculated between the step counts for each device and the tally counter. Mean Absolute Percent Error (MAPE) values were also calculated for each device relative to the tally counter. RESULTS: Fitbit Zip and One were significantly correlated with the tally counter (r=0.50, p\u3c0.05; r=0.68, p\u3c0.01, respectively) while the other devices were not significantly correlated. Mean bias and MAPE values were as follows: Device (Mean Bias/MAPE) Pedometer (-0.2±39.2/3.8±6.8), Blaze (34.5±67.1/9.9±11.3), Charge HR (-12.6±61.5/7.0±10.3), Alta (-85.0±70.8/17.1±14.1), Flex (49.5±242.4/19.7±45.3), Zip (1.8±3.4/0.4±0.6), One (0.2±2.1/0.3±0.3). Fitbit Zip and One were within one half percent of actual steps while wrist-worn Fitbits ranged from 7.0-19.7% from actual step counts. CONCLUSION: Consistent with previous research, activity trackers worn at the waist provide the most accurate step counts compared to wrist-worn models. Differences found in wrist-worn models may result in significant over- or underestimation of activity levels when worn for long periods of time

Comparison of Smartphone Pedometer Apps on a Treadmill versus Outdoors

Previous research has focused on the accuracy of smartphone pedometer apps in laboratory settings, however less information is available in outdoor (free living) environments. PURPOSE: Determine the accuracy of 5 smartphone apps at recording steps at a walking speed in a laboratory versus an outdoor setting. METHODS: Twenty-three healthy college students consented (Mean±SD; 22±3.8yrs; BMI 24.9±4.13kg/m2) to participate in 2 separate visits. During the first visit participants walked 500 steps at 3mph on a treadmill while wearing a pedometer and a smartphone placed in the pocket using 5 pedometer apps concurrently (Moves, Google Fit (G-Fit), Runtastic, Accupedo, S-Health). During the second visit, participants walked 400 meters at 3mph on a sidewalk outside. Actual steps for each visit were recorded using a hand tally counter device. Zero and negative values were replaced with the mean value for that trial. Statistical analyses were performed using IBM SPSS 23.0. Mean bias scores were calculated between the step count for each app and the respective tally count for each trial. Mean bias scores were correlated between trials for each app using Pearson correlations and significance was set at p\u3c0.05. Mean Absolute Percent Error (MAPE) values were also calculated for each app for both trials. RESULTS: G-Fit recorded 2 zero values and 2 negative values and Moves recorded 1 zero value. Mean bias scores were significantly correlated between the indoor and outdoor protocols for the pedometer (r=0.67, p\u3c0.01) and S-Health (r=0.46, p\u3c0.5). The remaining apps were not correlated between protocols. The outdoor protocol producing a greater mean bias for the outdoor protocol for G-Fit, Runtastic, and Accupedo (mean bias ± SD indoor, outdoor; -4.3±53.1, -19.3±120.0; -10.7±63.3, -33.4±118.7; 16.0±143.6, 79.0±75.0; respectively) and a greater mean bias for the indoor protocol for the pedometer, Moves, and S-Health (mean bias indoor, outdoor; -1.4±41.5, 0.0±34.1; -117.4±196.7, -42.2±209.6; 11.3±28.4, 0.0±58.7; respectively). MAPE was below 5% for the pedometer and S-Health for both trials. CONCLUSION: Apps with the lowest error in a controlled setting may be less affected when used in other settings, while apps with greater variation in a controlled setting may be affected when used in a different environment

Validity of Daily Physical Activity Measurements of Fitbit Charge 2

Physical activity monitors collect continuous data to provide a summary of daily activity. The Fitbit Charge 2 monitors heart rate as well as steps, calories, and active minutes throughout the day. There is currently no research validating the Fitbit Charge 2 at measuring daily physical activity levels in a real life setting. PURPOSE: To compare measures of daily steps and active minutes of Fitbit Charge 2 with a research-grade accelerometer. METHODS: Sixteen active college students (Mean±SD; 23±4.9yrs; 16.43±10.19%fat; 9 male) consented to be part of the study. Participants wore an ActiGraph GT3X accelerometer and Fitbit Charge 2 concurrently for seven consecutive days. Both devices were programed with each participant’s information and the participants were instructed to perform their daily activities wearing both devices and only remove them to shower and to sleep. Data were considered valid when participants wore both devices for at least 10 hours on 4 or more days of the week. Steps and active minutes (moderate-vigorous physical activity) were recorded by each device. Mean bias was calculated by subtracting ActiGraph steps and active minutes from those obtained from the Fitbit Charge 2 for each day and an average daily mean bias was calculated using values from all seven days. Absolute percentage error was also calculated [100(|Fitbit Charge 2 - ActiGraph|)/ActiGraph] to indicate the overall 7-day difference between the Fitbit Charge 2 and ActiGraph. Pearson correlations and paired sample t-test were performed to compare Fitbit Charge 2 measurements with the corresponding ActiGraph measurements with significance considered at p\u3c0.05. RESULTS: The Fitbit Charge 2 overestimated steps by 2,451.3±2085.4 compared to the ActiGraph using the daily average steps over the seven days. This was 32.2±40.7% above the ActiGraph measurement. Average mean bias for daily active minutes was -52.1±58.9 with the Fitbit Charge 2 underestimating compared to the ActiGraph. Active minutes for the Fitbit Charge 2 were an average of 69±26.1% away from the ActiGraph. Steps for the Fitbit Charge 2 were significantly correlated to ActiGraph steps (r=0.575, p=0.02) while active minutes were not significantly correlated (r= -0.255, p=0.34). Paired sample t-test results showed a significant difference between the Fitbit Charge 2 steps and active minutes compared with the ActiGraph (p\u3c0.01 for both). CONCLUSION: The Fitbit Charge 2 may be useful for measuring steps in a free-living environment, however active minutes are significantly underestimated

Accuracy of Fitbit Charge 2 at Estimating VO2max, Calories, and Steps on a Treadmill

Current fitness activity trackers can account for steps, calories burned, heart rate, and distance traveled. A new feature has recently been introduced on the Fitbit Charge 2, “Cardio Fitness Level,” which is comparable to a VO2max score in that it allows consumers to be aware of their aerobic fitness level. PURPOSE: To assess the accuracy of the Fitbit Charge 2 at estimating VO2 score (“Cardio Fitness Level”), calories, and steps when compared to indirect calorimetry and video analyzed steps, respectively. METHODS: Twenty-two healthy adults (Mean±SD; 24.1±4.2yrs; 16.9±9.0%fat; 15 male) completed two separate visits. On the first visit, anthropometric measurements were taken followed by a 10-minute outdoor run. Participants ran for 10 minutes at their own pace on flat terrain as recommended by Fitbit to generate a Cardio Fitness score. On the second visit, participants came fasted, at least 8 hours, and completed a standardized VO2max protocol (Arizona State protocol) using a PARVO TrueOne2400 metabolic cart. The treadmill was set at 3mph for the first 3 minutes with 0% grade. Following the first stage, the speed was raised to the participant’s pre-selected speed (between 5-8mph) with 0% grade. After stage 2 the grade increased every minute by 1.5% and speed was kept constant until fatigue was reached. Calories and step counts from the Fitbits were correlated with the metabolic cart and tally counter respectively, using 2-tailed Pearson correlations. Significance was set at pRESULTS: Participants completed the VO2max test in an average of 11:05. Eight of the 22 estimated VO2max ranges given by Fitbit included the value given by the metabolic cart. Fitbit ranges for seven participants were below the metabolic cart values and the Fitbit ranges for the remaining seven participants were above the metabolic cart values. Calories were correlated between the Fitbit and metabolic cart (r = 0.874, pCONCLUSION: VO2 scores given by the Fitbit Charge 2 did not always match values given by the metabolic cart but may serve as a rough estimate of fitness level. Fitbit Charge 2 may also be useful in tracking calories and steps in a controlled setting, but results may differ in real world conditions

BDNF-Live-Exon-Visualization (BLEV) Allows Differential Detection of BDNF Transcripts in vitro and in vivo

Bdnf exon-IV and exon-VI transcripts are driven by neuronal activity and are involved in pathologies related to sleep, fear or memory disorders. However, how their differential transcription translates activity changes into long-lasting network changes is elusive. Aiming to trace specifically the network controlled by exon-IV and -VI derived BDNF during activity-dependent plasticity changes, we generated a transgenic reporter mouse for BDNF-live-exon-visualization (BLEV), in which expression of Bdnf exon-IV and -VI can be visualized by co-expression of CFP and YFP. CFP and YFP expression was differentially activated and targeted in cell lines, primary cultures and BLEV reporter mice without interfering with BDNF protein synthesis. CFP and YFP expression, moreover, overlapped with BDNF protein expression in defined hippocampal neuronal, glial and vascular locations in vivo. So far, activity-dependent BDNF cannot be explicitly monitored independent of basal BDNF levels. The BLEV reporter mouse therefore provides a new model, which can be used to test whether stimulus-induced activity-dependent changes in BDNF expression are instrumental for long-lasting plasticity modifications

Charge separation relative to the reaction plane in Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm NN}}= 2.76 TeV

Measurements of charge dependent azimuthal correlations with the ALICE detector at the LHC are reported for Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV. Two- and three-particle charge-dependent azimuthal correlations in the pseudo-rapidity range $|\eta| < 0.8$ are presented as a function of the collision centrality, particle separation in pseudo-rapidity, and transverse momentum. A clear signal compatible with a charge-dependent separation relative to the reaction plane is observed, which shows little or no collision energy dependence when compared to measurements at RHIC energies. This provides a new insight for understanding the nature of the charge dependent azimuthal correlations observed at RHIC and LHC energies.Comment: 12 pages, 3 captioned figures, authors from page 2 to 6, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/286

Anisotropy and chemical composition of ultra-high energy cosmic rays using arrival directions measured by the Pierre Auger Observatory

The Pierre Auger Collaboration has reported evidence for anisotropy in the distribution of arrival directions of the cosmic rays with energies $E>E_{th}=5.5\times 10^{19}$ eV. These show a correlation with the distribution of nearby extragalactic objects, including an apparent excess around the direction of Centaurus A. If the particles responsible for these excesses at $E>E_{th}$ are heavy nuclei with charge $Z$, the proton component of the sources should lead to excesses in the same regions at energies $E/Z$. We here report the lack of anisotropies in these directions at energies above $E_{th}/Z$ (for illustrative values of $Z=6,\ 13,\ 26$). If the anisotropies above $E_{th}$ are due to nuclei with charge $Z$, and under reasonable assumptions about the acceleration process, these observations imply stringent constraints on the allowed proton fraction at the lower energies

A note on comonotonicity and positivity of the control components of decoupled quadratic FBSDE

In this small note we are concerned with the solution of Forward-Backward Stochastic Differential Equations (FBSDE) with drivers that grow quadratically in the control component (quadratic growth FBSDE or qgFBSDE). The main theorem is a comparison result that allows comparing componentwise the signs of the control processes of two different qgFBSDE. As a byproduct one obtains conditions that allow establishing the positivity of the control process.Comment: accepted for publicatio