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 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 one hour make the proposed method the most efficient among the state of the art.

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.

Background: Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them with conventional scores in predicting 10-year CVD risk for patients with breast cancer (BC), colorectal cancer (CRC), or lung cancer (LC). Methods: We analyzed adults diagnosed with BC, CRC, or LC between 2005 and 2012. An machine learning (ML) Extreme Gradient Boosting algorithm ranked 40-50 covariates for predicting CVD for each cancer type using SHapley Additive exPlanations values. The top 10 ML-predictors were used to create predictive equations using logistic regression and compared with American College of Cardiology (ACC)/American Heart Association (AHA) Pooled Cohort Equations (PCE), Predicting Risk of cardiovascular disease EVENTs (PREVENT), and Systematic COronary Risk Evaluation-2 (SCORE2) using the area under the curve (AUC). Results: We included 10 339 patients: 55.5% had BC, 15.6% had CRC, and 29.7% had LC. The actual 10-year CVD rates were: BC 21%, CRC 10%, and LC 28%. The predictors derived from the ML algorithm included cancer-specific and socioeconomic factors. The cancer-specific predictive scores achieved AUCs of 0.84, 0.76, and 0.83 for BC, CRC, and LC, respectively, and outperformed PCE, PREVENT, and SCORE2, increasing the absolute AUC values by up to 0.31 points (with AUC ranging from 0 to 1). Similar results were found when excluding patients with cardiac history or advanced cancer from the analysis. Conclusions: Cancer-specific CVD predictive scores outperform conventional scores and emphasize the importance of integrating cancer-related covariates for precise prediction.

BACKGROUND: Ang-II (angiotensin II) impairs the function of the antihypertensive enzyme ACE2 (angiotensin-converting enzyme 2) by promoting its internalization, ubiquitination, and degradation, thus contributing to hypertension. However, few ACE2 ubiquitination partners have been identified, and their role in hypertension remains unknown. METHODS: Proteomics and bioinformatic analyses were used to identify ACE2 ubiquitination partners in the brain, heart, and kidney of hypertensive C57BL6/J mice of both sexes. The interaction between UBR1 (ubiquitin protein ligase E3 component N-recognin) and ACE2 was validated in cells. Central and peripheral UBR1 knockdown was then performed in male mice to investigate its role in the maintenance of hypertension. RESULTS: Proteomics analysis of the hypothalamus identified UBR1 as a potential E3 (ubiquitin protein ligase) ligase promoting ACE2 ubiquitination. Enhanced UBR1 expression, associated with ACE2 reduction, was confirmed in various tissues from hypertensive male mice and human samples. Treatment of endothelial and smooth muscle cells with testosterone, but not 17β-estradiol, confirmed a sex-specific regulation of UBR1. In vivo silencing of UBR1 using chronic administration of small interference RNA resulted in the restoration of ACE2 levels in hypertensive male mice. A transient decrease in blood pressure after intracerebroventricular, but not systemic, infusion was also observed. Interestingly, UBR1 knockdown increased brain activation of Nedd4-2 (neural precursor cell expressed developmentally downregulated protein 4), an E3 ligase promoting ACE2 ubiquitination, and reduced expression of serum and glucocorticoid-regulated kinase 1, the kinase that inactivates Nedd4-2. CONCLUSIONS: These data demonstrate that UBR1 is a novel E3 ubiquitin ligase targeting ACE2 in hypertension. UBR1 and Nedd4-2 appear to work synergistically to ubiquitinate ACE2. Targeting these ubiquitin ligases may represent a novel strategy to restore ACE2 compensatory activity in hypertension.