27 research outputs found

    Regional differences in the expression of K<sup>+</sup>–Cl<sup>−</sup> 2 cotransporter in the developing rat cortex

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    The type 2 potassium–chloride cotransporter (KCC2) is the main regulator of intracellular chloride concentration in CNS neurons, and plays a crucial role in spine development that is independent of its ion cotransport function. The expression pattern of KCC2 is upregulated during postnatal development showing area and layer-specific differences in distinct brain areas. We examined the regional and ultrastructural localisation of KCC2 in various areas of developing neocortex and paleocortex during the first two postnatal weeks. Light-microscopy examination revealed diffuse neuropil and discrete funnel-shaped dendritic labelling in the piriform and entorhinal cortices at birth. Subsequently, during the beginning of the first postnatal week, diffuse KCC2 labelling gradually started to appear in the superficial layers of the neocortex while the punctate-like labelling of dendrites in the piriform, entorhinal and perirhinal cortices become more pronounced. By the end of the first postnatal week, discrete dendritic expression of KCC2 was visible in all neocortical and paleocortical areas. The expression level did not change during the second postnatal week suggesting that, in contrast to hippocampus, adult pattern of KCC2 in the cortical cells is already established by the end of the first postnatal week. Quantitative electron microscopy examination revealed that in superficial layers of both neo- and paleocortex, the majority of KCC2 signal was plasma membrane associated but the number of transport vesicle-associated immunosignal increased with development. In deep layers, KCC2 immunolabeling was evenly distributed in plasma membrane and transport vesicles showing no obvious change with maturation. The number of KCC2 immunogold particles increased in dendritic spines with no association with synapses. This observation points to the dual role of KCC2 in spine genesis and ion cotransport

    Synchronization of interhippocampal ripple events (80-200 Hz) by long-projection inhibitory neurons

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    Network oscillations between the two hippocampi are highly synchronized. Synchronized theta is believed to be the result of the common input from the septal region, whereas the mechanism of the ripple synchronization is not well understood. It was previously demonstrated using partial coherence analysis that the "coupling" between the two CA1 regions of hippocampi during theta oscillations is stronger than that between the individual layers of the same hippocampus

    Optimisation of Embryonic and Larval ECG Measurement in Zebrafish for Quantifying the Effect of QT Prolonging Drugs

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    Effective chemical compound toxicity screening is of paramount importance for safe cardiac drug development. Using mammals in preliminary screening for detection of cardiac dysfunction by electrocardiography (ECG) is costly and requires a large number of animals. Alternatively, zebrafish embryos can be used as the ECG waveform is similar to mammals, a minimal amount of chemical is necessary for drug testing, while embryos are abundant, inexpensive and represent replacement in animal research with reduced bioethical concerns. We demonstrate here the utility of pre-feeding stage zebrafish larvae in detection of cardiac dysfunction by electrocardiography. We have optimised an ECG recording system by addressing key parameters such as the form of immobilization, recording temperature, electrode positioning and developmental age. Furthermore, analysis of 3 days post fertilization (dpf) zebrafish embryos treated with known QT prolonging drugs such as terfenadine, verapamil and haloperidol led to reproducible detection of QT prolongation as previously shown for adult zebrafish. In addition, calculation of Z-factor scores revealed that the assay was sensitive and specific enough to detect large drug-induced changes in QTc intervals. Thus, the ECG recording system is a useful drug-screening tool to detect alteration to cardiac cycle components and secondary effects such as heart block and arrhythmias in zebrafish larvae before free feeding stage, and thus provides a suitable replacement for mammalian experimentation

    3D finite element electrical model of larval zebrafish ECG signals

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    Assessment of heart function in zebrafish larvae using electrocardiography (ECG) is a potentially useful tool in developing cardiac treatments and the assessment of drug therapies. In order to better understand how a measured ECG waveform is related to the structure of the heart, its position within the larva and the position of the electrodes, a 3D model of a 3 days post fertilisation (dpf) larval zebrafish was developed to simulate cardiac electrical activity and investigate the voltage distribution throughout the body. The geometry consisted of two main components; the zebrafish body was modelled as a homogeneous volume, while the heart was split into five distinct regions (sinoatrial region, atrial wall, atrioventricular band, ventricular wall and heart chambers). Similarly, the electrical model consisted of two parts with the body described by Laplace’s equation and the heart using a bidomain ionic model based upon the Fitzhugh-Nagumo equations. Each region of the heart was differentiated by action potential (AP) parameters and activation wave conduction velocities, which were fitted and scaled based on previously published experimental results. ECG measurements in vivo at different electrode recording positions were then compared to the model results. The model was able to simulate action potentials, wave propagation and all the major features (P wave, R wave, T wave) of the ECG, as well as polarity of the peaks observed at each position. This model was based upon our current understanding of the structure of the normal zebrafish larval heart. Further development would enable us to incorporate features associated with the diseased heart and hence assist in the interpretation of larval zebrafish ECGs in these conditions

    In vivo detection of hyperacute neuronal compaction and recovery by MRI following electric trauma in rats

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    PURPOSE To verify the following phenomenon in vivo using quantitative magnetic resonance imaging (MRI). Neuronal compression may occur following brain injuries in the cortex and hippocampus. As well being characterized by previous histological studies in rats, the majority of these neurons undergo hyperacute recovery rather than apoptotic death. MATERIALS AND METHODS Twenty male Wistar rats were assigned into injured or sham-injured groups (n = 10). The injured group underwent an electric trauma model to provoke compacted neuron formation. A T1 map was acquired prior to the injury and 10 T1 maps were acquired consecutively over a period of 2.5 hours after the injury, using a 3.0T scanner. Voxelwise statistical analyses were performed between timepoints. To enable comparison with the histological appearance of the compacted neurons, silver staining was performed on a sham-injured rat and five injured rats, 10, 40, 90, 150, and 300 minutes after the injury. RESULTS A significant (corrected P < 0.05) increase in average T1 from the preinjury (895.24 msec) to the first postinjury timepoint (T1  = 951.37 msec) was followed by a significant (corrected P < 0.05) decrease (return) up to the last postinjury timepoint (T1  = 913.16 msec) in the voxels of the cortex and hippocampus. No significant (corrected P < 0.05) change in T1 was found in the sham-injured group. CONCLUSION The spatial and temporal linkages between the MRI T1 changes and the histological findings suggest that neuronal compaction and recovery is associated with T1 alterations. MRI therefore offers the possibility of in vivo investigations of neuronal compaction and recovery. J. MAGN. RESON. IMAGING 2016;44:814-822

    Optimisation of ECG recording system.

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    <p>A) Mean ECG interval measurements obtained from 3 dpf larvae following exposure to different ambient temperatures (n = 10), and B) over different developmental periods (2, 3, 4 and 5 days; n = 8).</p
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