Analysis of Endothelial Progenitor Cell Subtypes as Clinical Biomarkers for Elderly Patients with Ischaemic Stroke

Endothelial progenitor cells (EPCs), expressing markers for stemness (CD34), immaturity (CD133) and endothelial maturity (KDR), may determine the extent of post-stroke vascular repair. Given the prevalence of stroke in elderly, this study explored whether variations in plasmatic availability of certain EPC subtypes could predict the severity and outcome of disease in older patients. Blood samples were collected from eighty-one consented patients (≥ 65 years) at admission and days 7, 30 and 90 post-stroke. EPCs were counted with flow cytometry. Stroke severity and outcome were assessed using the National Institutes of Health Stroke Scale, Barthel Index and modified Rankin Scale. The levels of key elements known to affect EPC characteristics were measured by ELISA. Diminished total antioxidant capacity and CD34 + KDR + and CD133 + KDR + counts in early phases of stroke were associated with disease severity and worse functional outcome at day 90 post-stroke. Baseline levels of angiogenic agent PDGF-BB, but not VEGF, positively correlated with CD34 + KDR + numbers at day 90. Baseline LDL-cholesterol levels were inversely correlated with CD34 + KDR+, CD133 + KDR + and CD34 + CD133 + KDR + numbers at day 90. Close correlation between baseline CD34 + KDR + and CD133 + KDR + counts and the outcome of stroke proposes these particular EPC subtypes as potential prognostic markers for ischaemic stroke

Enhanced Weight-Optimized Recurrent Neural Networks Based on Sine Cosine Algorithm for Wave Height Prediction

Constructing offshore and coastal structures with the highest level of stability and lowest cost, as well as the prevention of faulty risk, is the desired plan that stakeholders seek to obtain. The successful construction plans of such projects mostly rely on well-analyzed and modeled metocean data that yield high prediction accuracy for the ocean environmental conditions including waves and wind. Over the past decades, planning and designing coastal projects have been accomplished by traditional static analytic, which requires tremendous efforts and high-cost resources to validate the data and determine the transformation of metocean data conditions. Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. Three neural network models named: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (VRNN), and Gated Recurrent Network (GRU) are enhanced, instead of random weight initialization, SCA generates weight values that are adaptable to the nature of the data and model structure. Besides, a Grid Search (GS) is utilized to automatically find the best models’ configurations. To validate the performance of the proposed models, metocean datasets have been used. The original LSTM, VRNN, and GRU are implemented and used as benchmarking models. The results show that the optimized models outperform the original three benchmarking models in terms of mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE)