The effects of chronic aluminum exposure on learning and memory of rats by observing the changes of Ras/Raf/ERK signal transduction pathway

Objective

To investigate the effects of chronic aluminum (Al) exposure on learning and memory function of rats by observing the changes of Ras/Raf/ERK (Ras/ERK) signaling pathway.

Methods

Eighty weaned Wistar rats were divided into four groups ad libitum, 20 rats in each group. The four groups were fed with drinking water containing 0% (control), 0.2%, 0.4% and 0.6% (Al exposure) AlCl₃ for 3 months individually to set up aluminum exposure models. The laboratory was maintained at 18–23 °C and 45–55% relative humidity. Graphite furnace atomic absorption spectrometry was used to detect the content of Al in brain and blood. Western blot and real-time PCR (RT-PCR) were used to determine the protein and mRNA expression levels for Ras, Raf1, ERK2 and CREB.

Results

Chronic Al exposure increased the content of Al in rats’ blood and brain. It increased expression of Ras in the hippocampi compared with the control but the expression decreased along the Al exposure groups (p < 0.05). Similarly, Raf1, ERK2 and CREB expressions decreased compared to the control in a dose-dependent manner (p < 0.05).

Conclusion

Chronic Al exposure may affect learning and memory through impact on Ras/ERK signal pathway.     

Assessment of PCBs exposure in human hair using double focusing high resolution mass spectrometry and single quadrupole mass spectrometry

Polychlorinated biphenyls (PCBs) levels were assessed in human hair samples, originating from two main agricultural regions of Greece. The analysis was performed by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-double focusing high resolution mass spectrometry (GC-DFHRMS). The main analytical procedure involved hair decontamination, solid-liquid extraction and cleanup steps. The recoveries of PCBs ranged from 71.2% to 101.6%, with accuracies greater than 87.5% and the between-run precisions (%RSD) lower than 25% for all analytes. Differences in the frequencies of detection and the median values of PCBs were detected between the examined regions and between the applied analytical techniques. All Peloponnesus' hair examined samples were found positive for each examined PCB, while the percentage of the total positive samples ranged from 86.1% (for PCB 138) to 94.4% (for PCB 28 and 153 congeners) using GC-DFHRMS. The Cretan hair samples were less contaminated (SUM PCBs=0.61 and 1.47pg/mg) unlike the Peloponnesus' samples (SUM PCBs=24.68 and 38.74pg/mg) measured by GC-DFHRMS and GC-MS, respectively. PCBs with high chlorination gave lower concentration values compared to low chlorination PCBs in both populations. No significant differences were observed between women and men. The GC-DFHRMS technique provided higher percentage of positive samples and low PCBs median values, due to higher sensitivity and interferences from isobaric ions in the GC-MS technique and is therefore considered as a powerful tool for such assessments in hair specimens.     

Taurine attenuates methamphetamine-induced autophagy and apoptosis in PC12 cells through mTOR signaling pathway

Methamphetamine (METH), a commonly abused psychostimulant, has been shown to induce neuronal damage by causing reactive oxygen species (ROS) formation, apoptosis and autophagy. Taurine (2-aminoethanesulfonic acid) is involved in several physiological actions in the brain, including neuroprotection, osmoregulation and neurotransmission. In this study, we investigate the protective effect of taurine against METH-induced neurotoxicity in PC12 cells and the underlying mechanism. The results showed that taurine significantly increased the cell viability inhibited by METH. LC3-II expression was elevated by METH treatment, whereas such increase was obviously attenuated by taurine. Co-treatment of taurine strongly reversed the decline of antioxidase activities induced by METH. Moreover, phosphorylated mammalian target of rapamycin (p-mTOR) was significantly inhibited by METH, whereas complementation of taurine markedly increased the expression of p-mTOR in PC12 cells, rather than phosphorylated Erk. Interestingly, taurine-induced decreasing expression of LC3-II was partially blocked by pretreatment of RAD001, an mTOR inhibitor. These results indicated that taurine inhibits METH-induced autophagic process through activating mTOR rather than Erk signaling. Collectively, our study shows that taurine protects METH-induced PC12 cells damage by attenuating ROS production, apoptosis and autophagy, at least in part, via mTOR signaling pathway.     

信号检测在药品不良反应监测系统中的应用

信号检测是一种能筛选大量数据的、系统的、自动化的、操作性较强的方法,可充分挖掘和利用各类药物安全性数据库,比传统评价方法更快捷、更精确地鉴别潜在的安全性问题。国家药品不良反应监测系统中采用了信号检测方法来挖掘药品不良反应数据库,辅助发现药品安全性信号。通过解释信号检测原理、方法,介绍信号检测方法在不良反应监测中的实施及应用,以期为有关人员理解和运用信号检测方法及工具提供参考。     

Characterization of epithelial V-like antigen in human choroid plexus epithelial cells: potential role in CNS immune surveillance

Near-infrared spectroscopy (NIRS) can detect two different kinds of signals from the human brain: the hemodynamic response (slow) and the neuronal response (fast). This paper explores a nonlinear aspect in the tactile-stimulus-evoked neuronal optical response over a NIRS time series (light intensity variation). The existence of the fast optical responses (FORs) over the time series recorded in stimulus sessions is confirmed by event-related averaging. The chaos levels of the NIRS time series recorded both in stimulus and in rest sessions are then identified according to the estimated largest Lyapunov exponent. The obtained results ascertain that stimulus-evoked neuronal optical responses can be detected in the somatosensory cortex using continuous-wave NIRS equipment. Further, the results strongly suggest that the chaos level can be used to recognize the FORs in NIRS time series and, thereby, the state of the pertinent brain activity.     

The effect of musical experience on hemispheric lateralization in musical feature processing

In the monkey's visual cortex, there are two well-documented information processing streams: the dorsal motion and ventral form/color pathways. Similarly, two corresponding information streams were also found in the cat's visual cortices, and PMLS and area 21a are the gateways for distinct motion and form information processing. It has been shown that the feedback from PMLS solely modulates motion direction, but not orientation response, while the feedback from area 21a modulates form related features, such as spatial frequency dependency and neuronal oblique effect. Here, we postulate that feedback signals from higher cortical areas in the form or the motion information pathway may solely modulate the corresponding properties in neurons in the lower areas of the visual system. To examine the above hypothesis, the impact of feedback from higher area 21a on both orientation and direction maps was investigated in area 17 of the cat using intrinsic signal optical imaging. The results showed that the feedback from area 21a did not affect the amplitude and preference of direction, but did modulate orientation response in area 17, supporting the above hypothesis.     

Functional magnetic resonance imaging as a dynamic candidate biomarker for Alzheimer's disease

During the last two decades, imaging of neural activation has become an invaluable tool for assessing the functional organization of the human brain in vivo. Due to its widespread application in neuroscience, functional neuroimaging has raised the interest of clinical researchers in its possible use as a diagnostic biomarker. A hallmark feature of many neurodegenerative diseases is their chronic non-linear dynamic and highly complex preclinical course. Neurodegenerative diseases unfold over years to decades through clinically silent and asymptomatic stages of early adaptive, compensatory to pathophysiological (i.e. actively neurodegenerative) and decompensatory mechanisms in the brain - phases that are increasingly being considered as critical for primary and secondary preventive and therapeutic measures. Emerging evidence supports the concept of a potentially fully reversible functional phase that may precede the onset of micro- and macrostructural and cognitive decline, a potentially late-stage "neurodegenerative" phase of a primary neurodegenerative disorder. Alzheimer's disease serves as an ideal model to test this hypothesis supported by the neural network model of the healthy and diseased brain. Being highly dynamic in nature, brain activation and neuronal network functional connectivity represent not only candidate diagnostic but also candidate surrogate markers for interventional trials. Potential caveats of functional imaging are critically reviewed with focus on confound variables such as altered neurovascular coupling as well as parameters related to task- and study design.     

Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study

Objective

We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification.

Methods and materials

The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely, Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof.

Results

We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value.

Conclusions

Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality).