Applied Sciences and Technologies for Detection and Therapies of Pathologies in the Neuronal Environment

When we experience psychological challenges in the environment, our heart rate usually rises to make us more able to solve the task, but there is an individual difference in cardiovascular reactivity (CVR). Extreme CVR to environmental demands has been associated with worse health outcomes, with blunted CVR (little or no rise in heart rate) related to maladaptive behavior, including depression. The blunted CVR has been explained by motivational disengagement, which involves giving up on a task when facing obstacles. Disengagement is thought to be a habitual response that people might not be aware of, and, therefore, objective measures such as test performance might serve as a good measure of engagement. In this study, 66 participants solved different cognitive tasks while their CVR was measured. The aim was to test the association between test performance and reactivity, measured with the difference in heart rate at baseline and the mean heart rate while solving the tasks. Our results show a significant association between reactivity scores and performance in all tests, of various difficulty, indicating that blunted cardiovascular reactivity predicts poorer cognitive performance. Furthermore, we find an association between reactivity in one test and the performance in the other tests, suggesting that disengagement from environmental demands can be more general and not depend on the task at hand. The results, therefore, support earlier research suggesting that blunted CVR is associated with worse cognitive performance, and extends the literature by indicating that disengagement could be a more general maladaptive response to the environment. Full article

Nitric oxide (NO) is a crucial factor in regulating neuronal development. However, certain effects of NO are complex under different physiological conditions. In this study, we used differentiated neural stem cells (NSCs), which contained neural progenitor cells, neurons, astrocytes, and oligodendrocytes, to observe the physiological effects of sodium nitroprusside (SNP) on the early developmental stage of the nervous system. After SNP treatment for 24 h, the results showed that SNP at 100 μM, 200 μM, 300 μM, and 400 μM concentrations resulted in reduced cell viability and increased cleaved caspase 3 levels, while no significant changes were found at 50 μM. There were no effects on neuronal differentiation in the SNP-treated groups. The phosphorylation of p38 was also significantly upregulated with SNP concentrations of 100 μM, 200 μM, 300 μM, and 400 μM, with no changes for 50 μM concentration in comparison with the control. We also observed that the levels of phosphorylation increased with the increasing concentration of SNP. To further explore the possible role of p38 in SNP-regulated survival of differentiated NSCs, SB202190, the antagonist of p38 mitogen-activated protein kinase, at a concentration of 10 mM, was pretreated for 30 min, and the ratio of phosphorylated p38 was found to be decreased after treatment with SNP. Survival and cell viability increased in the SB202190 and SNP co-treated group. Taken together, our results suggested that p38 is involved in the cell survival of NSCs, regulated by NO. Full article

Purpose: Brain tumors are diagnosed and classified manually and noninvasively by radiologists using Magnetic Resonance Imaging (MRI) data. The risk of misdiagnosis may exist due to human factors such as lack of time, fatigue, and relatively low experience. Deep learning methods have become increasingly important in MRI classification. To improve diagnostic accuracy, researchers emphasize the need to develop Computer-Aided Diagnosis (CAD) computational diagnostics based on artificial intelligence (AI) systems by using deep learning methods such as convolutional neural networks (CNN) and improving the performance of CNN by combining it with other data analysis tools such as wavelet transform. In this study, a novel diagnostic framework based on CNN and DWT data analysis is developed for the diagnosis of glioma tumors in the brain, among other tumors and other diseases, with T2-SWI MRI scans. It is a binary CNN classifier that treats the disease “glioma tumor” as positive and the other pathologies as negative, resulting in a very unbalanced binary problem. The study includes a comparative analysis of a CNN trained with wavelet transform data of MRIs instead of their pixel intensity values in order to demonstrate the increased performance of the CNN and DWT analysis in diagnosing brain gliomas. The results of the proposed CNN architecture are also compared with a deep CNN pre-trained on VGG16 transfer learning network and with the SVM machine learning method using DWT knowledge. Methods: To improve the accuracy of the CNN classifier, the proposed CNN model uses as knowledge the spatial and temporal features extracted by converting the original MRI images to the frequency domain by performing Discrete Wavelet Transformation (DWT), instead of the traditionally used original scans in the form of pixel intensities. Moreover, no pre-processing was applied to the original images. The images used are MRIs of type T2-SWI sequences parallel to the axial plane. Firstly, a compression step is applied for each MRI scan applying DWT up to three levels of decomposition. These data are used to train a 2D CNN in order to classify the scans as showing glioma or not. The proposed CNN model is trained on MRI slices originated from 382 various male and female adult patients, showing healthy and pathological images from a selection of diseases (showing glioma, meningioma, pituitary, necrosis, edema, non-enchasing tumor, hemorrhagic foci, edema, ischemic changes, cystic areas, etc.). The images are provided by the database of the Medical Image Computing and Computer-Assisted Intervention (MICCAI) and the Ischemic Stroke Lesion Segmentation (ISLES) challenges on Brain Tumor Segmentation (BraTS) challenges 2016 and 2017, as well as by the numerous records kept in the public general hospital of Chania, Crete, “Saint George”. Results: The proposed frameworks are experimentally evaluated by examining MRI slices originating from 190 different patients (not included in the training set), of which 56% are showing gliomas by the longest two axes less than 2 cm and 44% are showing other pathological effects or healthy cases. Results show convincing performance when using as information the spatial and temporal features extracted by the original scans. With the proposed CNN model and with data in DWT format, we achieved the following statistic percentages: accuracy 0.97, sensitivity (recall) 1, specificity 0.93, precision 0.95, FNR 0, and FPR 0.07. These numbers are higher for this data format (respectively: accuracy by 6% higher, recall by 11%, specificity by 7%, precision by 5%, FNR by 0.1%, and FPR is the same) than it would be, had we used as input data the intensity values of the MRIs (instead of the DWT analysis of the MRIs). Additionally, our study showed that when our CNN takes into account the TL of the existing network VGG, the performance values are lower, as follows: accuracy 0.87, sensitivity (recall) 0.91, specificity 0.84, precision 0.86, FNR of 0.08, and FPR 0.14. Conclusions: The experimental results show the outperformance of the CNN, which is not based on transfer learning, but is using as information the MRI brain scans decomposed into DWT information instead of the pixel intensity of the original scans. The results are promising for the proposed CNN based on DWT knowledge to serve for binary diagnosis of glioma tumors among other tumors and diseases. Moreover, the SVM learning model using DWT data analysis performs with higher accuracy and sensitivity than using pixel values. Full article