Maternal Western diet programs cardiometabolic dysfunction and hypothalamic inflammation via epigenetic mechanisms predominantly in the male offspring.

OBJECTIVE: Maternal exposure during pregnancy is a strong determinant of offspring health outcomes. Such exposure induces changes in the offspring epigenome resulting in gene expression and functional changes. In this study, we investigated the effect of maternal Western hypercaloric diet (HCD) programming during the perinatal period on neuronal plasticity and cardiometabolic health in adult offspring. METHODS: C57BL/6J dams were fed HCD for 1 month prior to mating with regular diet (RD) sires and kept on the same diet throughout pregnancy and lactation. At weaning, offspring were maintained on either HCD or RD for 3 months resulting in 4 treatment groups that underwent cardiometabolic assessments. DNA and RNA were extracted from the hypothalamus to perform whole genome methylation, mRNA, and miRNA sequencing followed by bioinformatic analyses. RESULTS: Maternal programming resulted in male-specific hypertension and hyperglycemia, with both males and females showing increased sympathetic tone to the vasculature. Surprisingly, programmed male offspring fed HCD in adulthood exhibited lower glucose levels, less insulin resistance, and leptin levels compared to non-programmed HCD-fed male mice. Hypothalamic genes involved in inflammation and type 2 diabetes were targeted by differentially expressed miRNA, while genes involved in glial and astrocytic differentiation were differentially methylated in programmed male offspring. These data were supported by our findings of astrogliosis, microgliosis and increased microglial activation in programmed males in the paraventricular nucleus (PVN). Programming induced a protective effect in male mice fed HCD in adulthood, resulting in lower protein levels of hypothalamic TGFß2, NF-κB2, NF-κBp65, Ser-pIRS1, and GLP1R compared to non-programmed HCD-fed males. Although TGFß2 was upregulated in male mice exposed to HCD pre- or post-natally, only blockade of the brain TGFß receptor in RD-HCD mice improved glucose tolerance and a trend to weight loss. CONCLUSIONS: Our study shows that maternal HCD programs neuronal plasticity in the offspring and results in male-specific hypertension and hyperglycemia associated with hypothalamic inflammation in mechanisms and pathways distinct from post-natal HCD exposure. Together, our data unmask a compensatory role of HCD programming, likely via priming of metabolic pathways to handle excess nutrients in a more efficient way.

Deep Learning Approach for Epileptic Focus Localization.

The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on the intracranial EEG data to make a surgical decision related to epileptic subjects suffering from uncontrollable seizures. This surgery usually aims to remove the epileptogenic region which requires precise characterization of that area using the EEG recordings. In this paper, we propose two methods based on deep learning targeting accurate automatic epileptic focus localization using the non-stationary EEG recordings. Our first proposed method is based on semi-supervised learning, in which a deep convolutional autoencoder is trained and then the pre-trained encoder is used with multi-layer perceptron as a classifier. The goal is to determine the location of the EEG signal that is responsible for the epileptic activity. In the second proposed method, unsupervised learning scheme is implemented by merging deep convolutional variational autoencoder and K-means algorithm for clustering the iEEG signals into two distinct clusters based on the seizure source. The proposed methods automate and integrate the features extraction and classification processes instead of manually extracting the features as done in the previous studies. Dimensionality reduction is achieved using the autoencoder, while the important spatio-temporal features are extracted from the EEG recordings using the convolutional layers. Moreover, we implemented the inference network of the semi-supervised model on FPGA. The results of our experiments demonstrate high classification accuracy and clustering performance in localizing the epileptic focus compared with the state of the art.

Efficient Epileptic Seizure Prediction Based on Deep Learning.

Epilepsy is one of the world's most common neurological diseases. Early prediction of the incoming seizures has a great influence on epileptic patients' life. In this paper, a novel patient-specific seizure prediction technique based on deep learning and applied to long-term scalp electroencephalogram (EEG) recordings is proposed. The goal is to accurately detect the preictal brain state and differentiate it from the prevailing interictal state as early as possible and make it suitable for real time. The features extraction and classification processes are combined into a single automated system. Raw EEG signal without any preprocessing is considered as the input to the system which further reduces the computations. Four deep learning models are proposed to extract the most discriminative features which enhance the classification accuracy and prediction time. The proposed approach takes advantage of the convolutional neural network in extracting the significant spatial features from different scalp positions and the recurrent neural network in expecting the incidence of seizures earlier than the current methods. A semi-supervised approach based on transfer learning technique is introduced to improve the optimization problem. A channel selection algorithm is proposed to select the most relevant EEG channels which makes the proposed system good candidate for real-time usage. An effective test method is utilized to ensure robustness. The achieved highest accuracy of 99.6% and lowest false alarm rate of 0.004 h - 1 along with very early seizure prediction time of 1 h make the proposed method the most efficient among the state of the art.

Combined Genotype Analyses of Precursor miRNA196a2 and 499a Variants with Hepatic and Renal Cancer Susceptibility a Preliminary Study.

MicroRNAs, a novel class of small noncoding RNAs, are key players in many cellular processes, including cell proliferation, differentiation, invasion and regeneration. Tissue and circulatory microRNAs could serve as useful clinical biomarkers and deregulated expression levels have been observed in various cancers. Gene variants may alter microRNA processing and maturation. Thus, we aimed to investigate the association of MIR196a2 rs11614913 (C/T), MIR499a rs3746444 (A/G) polymorphisms and their combination with cancer susceptibility in an Egyptian population. Sixty five renal cell carcinoma (RCC) and 60 hepatocellular carcinoma (HCC) patients and 150 controls were enrolled in the study. They were genotyped using realtime polymerase chain reaction technology. Both miR196a2*T and miR499a*G were associated with RCC risk, but only miR196a*T was associated with HCC development. Carriage of the homozygote combinations (MIR196a2*TT + MIR499a*AA) and (MIR196a2*CC + MIR499a*GG) was associated with 25 and 48 fold elevation of likelhood to develop RCC, respectively. The miR196a2 SNP was also linked with larger tumor size in RCC and advanced tumor stage in HCC. miR196a2 and miR499a combined genotypes were associated with RCC and HCC. Further functional analysis of SNPs is required to confirm relationships between genotypes and phenotypes.