The Brain Oxylipin Profile Is Resistant to Modulation by Dietary n-6 and n-3 Polyunsaturated Fatty Acids in Male and Female Rats

Oxylipins are bioactive lipids formed by the monooxygenation of polyunsaturated fatty acids (PUFA). Eicosanoids derived from arachidonic acid (ARA) are the most well-studied class of oxylipins that influence brain functions in normal health and in disease. However, comprehensive profiling of brain oxylipins from other PUFA with differing functions, and the examination of the effects of dietary PUFA and sex differences in oxylipins are warranted. Therefore, female and male Sprague–Dawley rats were provided standard rodent diets that provided additional levels of the individual n-3 PUFA α-linolenic acid (ALA), eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA), or the n-6 PUFA linoleic acid (LNA) alone or with ALA (LNA + ALA) compared to essential fatty acid-sufficient control diets. Oxylipins and PUFA were quantified in whole brains using HPLC-MS/MS and GC, respectively. Eighty-seven oxylipins were present at quantifiable levels: 51% and 17% of these were derived from ARA and DHA, respectively. At the mass level, ARA and DHA oxylipins comprised 81–90% and 6–12% of total oxylipins, while phospholipid ARA and DHA represented 25–35% and 49–62% of PUFA mass, respectively. Increasing dietary n-3 PUFA resulted in higher levels of oxylipins derived from their precursor PUFA; otherwise, the brain oxylipin profile was largely resistant to modulation by diet. Approximately 25% of oxylipins were higher in males, and this was largely unaffected by diet, further revealing a tight regulation of brain oxylipin levels. These fundamental data on brain oxylipin composition, diet effects, and sex differences will help guide future studies examining the functions of oxylipins in the brain.     

Pharmacokinetics and efficiency of brain targeting of ginsenosides Rg1 and Rb1 given as Nao-Qing microemulsion

Nao-Qing solution has been shown to be clinically effective in the treatment of acute ischemic stroke (AIS). The purpose of this study was to improve the pharmacokinetics and brain uptake of Nao-Qing, administered as an oil-in-water microemulsion. Sprague–Dawley (SD) rats were given Nao-Qing microemulsion by intranasal or intragastric routes. Samples of blood, brain, heart, liver, lung and kidney were collected at pre-determined time intervals, and the contents of ginsenosides Rg1 and Rb1 (active ingredients of the Nao-Qing microemulsion) were analyzed by high-performance liquid chromatography (HPLC). The results showed that contents of ginsenosides Rg1 and Rb1 in Nao-Qing microemulsion was 8475.13?±?54.61?μg/ml and 6633.42?±?527.27?μg/ml, respectively, and that the particle size, pH and viscosity of the microemulsion were 19.9?±?5.07?nm, 6.1 and 3.056?×?10^?3?Pas, respectively. Absorption of ginsenoside Rg1 was higher than that of ginsenoside Rb1, which was barely detectable after intragastric administration; furthermore, the concentration of ginsenoside Rg1 in blood and other tissues at each time point was lower for intragastric than for intranasal administration. Compared with intragastric administration, intranasal administration resulted in a shorter t_max (0.08 versus 1?h), a higher C_max (16.65 versus 11.29?μg/ml), and a higher area under the concentration–time curve (AUC) (592.91 versus 101.70?μg?h/ml) in the brain. The relative rates of uptake (R_e) and the ratio of peak concentration (C_e) in the brain were 126.31% and 147.48% for ginsenoside Rg1, respectively. These data illustrate that intranasal administration can promote the absorption of drugs in Nao-Qing microemulsion and achieve fast effect.     

Prenatal exposure to the fluoride containing psychiatric drug fluoxetine and anti-oxidative alterations in the neonatal rat brain

Fluoride is a key ingredient of many psychiatric drugs like fluoxetine (Prozac^®, Fluoxetine^®). Pregnant women frequently use this drug as they suffer from depression and anxiety disorders during this period. Fluoxetine is able to reach the fetus through the placenta and passes to the newborn through milk. In the present study, female Wistar rats were treated with 5, 10, and 20 mg/L fluoxetine (containing 94% fluorides) from pregnancy day 10 to day 20. After delivery, the levels of the enzymatic antioxidants in the brain of their offspring at postnatal day 2 were measured. The results showed that, in all fluoxetine exposed groups compared with the control group, there was a significant decrease (P < 0.01) in the glutathione, catalase, glutathione S-transferases and potassium and a non- significant increase (P > 0.05) in the activity of malondialdehyde and creatine kinase. The results suggest that fluoxetine may be a developmental neurotoxicant due to presence of fluoride hence must be used carefully during pregnancy.     

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.     

Impact of SARS-CoV-2 infection during pregnancy on postnatal brain development: The potential role of glial cells

Glial cells are crucial for maintaining central nervous system (CNS) homeostasis. They actively participate in immune responses, as well as form functional barriers, such as blood-brain barrier (BBB), which restrict the entry of pathogens and inflammatory mediators into the CNS. In general, viral infections during the gestational period can alter the embryonic and fetal environment, and the related inflammatory response may affect neurodevelopment and lead to behavioral dysfunction during later stage of life, as highlighted by our group for Zika virus infection. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) induces a cytokine storm and, during pregnancy, may be related to a more severe form of the coronavirus disease-19 (COVID-19) and also to higher preterm birth rates. SARS-CoV-2 can also affect the CNS by inducing neurochemical remodeling in neural cells, which can compromise neuronal plasticity and synaptic function. However, the impact of SARS-CoV-2 infection during pregnancy on postnatal CNS, including brain development during childhood and adulthood, remains undetermined. Our group has recently highlighted the impact of COVID-19 on the expression of molecular markers associated with neuropsychiatric disorders, which are strongly related to the inflammatory response. Thus, based on these relationships, we discussed the impact of SARS-CoV-2 infection either during pregnancy or in critical periods of neurodevelopment as a risk factor for neurological consequences in the offspring later in life, focusing on the potential role of glial cells. Thus, it is important to consider future and long-term public health concerns associated with SARS-CoV-2 infection during pregnancy.     

A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network

The principle restorative step in the treatment of ischemic stroke depends on how fast the lesion is delineated from the Magnetic Resonance Imaging (MRI) images. This will serve as a vital aid to estimate the extent of damage caused to the brain cells. However, manual delineation of the lesion is time-consuming and it is subjected to intra-observer and inter-observer variability. Most of the existing methods for ischemic lesion segmentation rely on extracting handcrafted features followed by application of a machine learning algorithm. Identifying such features demand multi-domain expertise in Neuro-radiology as well as Image processing. This can be accomplished by learning the features automatically using Convolutional Neural Network (CNN). To perform segmentation, the spatial arrangement of pixel needs to be preserved in addition to learning local features of an image. Hence, a deep supervised Fully Convolutional Network (FCN) is presented in this work to segment the ischemic lesion. The highlight of this research is the application of Leaky Rectified Linear Unit activation in the last two layers of the network for a precise reconstruction of the ischemic lesion. By doing so, the network was able to learn additional features which are not considered in the existing U-Net architecture. Also, an extensive analysis was conducted in this research to select optimal hyper-parameters for training the FCN. A mean segmentation accuracy of 0.70 has been achieved from the experiments conducted on ISLES 2015 dataset. Experimental observations show that our proposed FCN method is 10% better than the existing works in terms of Dice Coefficient.     

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method. This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features. Using k-mean clustering algorithm the tumor regions in glioma are classified. The proposed algorithm provides high sensitivity, specificity and tumor segmentation accuracy.     

Ultra‐Performance Liquid Chromatography‐Mass Spectrometry Analysis of Free and Esterified Oxygenated Derivatives from Docosahexaenoic Acid in Rat Brain

Docosahexaenoic acid (DHA), a prominent long‐chain fatty acid of the omega‐3 family, is present at high amount in brain tissues, especially in membrane phospholipids. This polyunsaturated fatty acid is the precursor of various oxygenated lipid mediators involved in diverse physiological and pathophysiological processes. Characterization of DHA‐oxygenated metabolites is therefore crucial for better understanding the biological roles of DHA. In this study, we identified and measured, by ultrahigh‐performance liquid chromatography coupled with tandem mass spectrometry, a number of oxygenated products derived from DHA in exsanguinated and nonexsanguinated brains. These metabolites were found both in free form and esterified in phospholipids. Interestingly, both (R)‐ and (S)‐monohydroxylated fatty acid stereoisomers were observed free and esterified in phospholipids. Monohydroxylated metabolites were the main derivatives; however, measurable amounts of dihydroxylated products such as protectin DX were detected. Moreover, exsanguination allowed discriminating brain oxygenated metabolites from those generated in blood. These results obtained in healthy rats allowed an overview on the brain oxygenated metabolism of DHA, which deserves further research in pathophysiological conditions, especially in neurodegenerative diseases.