Search

Cerebral ischemic stroke is a major cause of death worldwide due to brain cell death resulting from ischemia-reperfusion injury. However, effective treatment approaches for patients with ischemic stroke are still lacking in clinical practice. This study investigated the potential neuroprotective effects of sildenafil, a phosphodiesterase-5 inhibitor, in a gerbil model of global brain ischemia. We investigated the effects of sildenafil on the expression of glial fibrillary acidic protein and aquaporin-4, which are markers related to astrocyte activation and water homeostasis, respectively. Immunofluorescence analysis showed that the number of cells co-expressing these markers, which was elevated in the ischemia-induced group, was significantly reduced in the sildenafil-treated groups. This suggests that sildenafil may have a potential mitigating effect on astrocyte activation induced by ischemia. Additionally, we performed various behavioral tests, including the open-field test, novel object recognition, Barnes maze, Y-maze, and passive avoidance tests, to evaluate sildenafil's effect on cognitive function impaired by ischemia. Overall, the results suggest that sildenafil may serve as a neuroprotective agent, potentially alleviating delayed neuronal cell death and improving cognitive function impaired by ischemia. [ABSTRACT FROM AUTHOR]

Recent research has made significant progress in automated unmanned systems utilizing Artificial Intelligence (AI)-based image processing to optimize the rebar manufacturing process and minimize defects such as twisting during production. Despite various studies, including those employing data augmentation through Generative Adversarial Networks (GANs), the performance of rebar twist prediction has been limited due to image quality degradation caused by environmental noise, such as insufficient image quality and inconsistent lighting conditions in rebar processing environments. To address these challenges, we propose a novel approach for real-time rebar twist prediction in manufacturing processes. Our method involves restoring low-quality grayscale images to high resolution and employing an object detection model to identify and track rebar endpoints. We then apply regression analysis to the coordinates obtained from the bounding boxes to estimate the error rate of the rebar endpoint positions, thereby determining the occurrence of twisting. To achieve this, we first developed a Unified-Channel Attention (UCA) module that is robust to changes in intensity and contrast for grayscale images. The UCA can be integrated into image restoration models to more accurately detect rebar endpoint characteristics in object detection models. Furthermore, we introduce a method for predicting the future positions of rebar endpoints using various linear and non-linear regression models. The predicted positions are used to calculate the error rate in rebar endpoint locations, determined by the distance between the actual and predicted positions, which is then used to classify the presence of rebar twisting. Our experimental results demonstrate that integrating the UCA module with our image restoration model significantly improved existing models in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Moreover, employing regression models to predict future rebar endpoint positions enhances the F1 score for twist prediction. As a result, our approach offers a practical solution for rapid defect detection in rebar manufacturing processes. [ABSTRACT FROM AUTHOR]

During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small dataset sizes, the limited availability of clinical variables, or a constrained classification of severity levels by a single classifier. In this paper, we propose an adaptive stacking ensemble technique that identifies various COVID-19 patient severity levels and separates them into three formats: Type 1 (low or high severity), Type 2 (mild, severe, critical), and Type 3 (asymptomatic, mild, moderate, severe, fatal). To enhance the model's generalizability, we utilized a nationwide dataset from the South Korean government, comprising data from 5644 patients across over 100 hospitals. To address the limited availability of clinical variables, our technique employs data-driven strategies and a proposed feature selection method. This ensures the availability of clinical variables across diverse hospital environments. To construct optimal stacking ensemble models, our technique adaptively selects candidate base classifiers by analyzing the correlation between their predicted outcomes and performance. It then automatically determines the optimal multi-layer combination of base and meta-classifiers using a greedy search algorithm. To further improve the performance, we applied various techniques, including imputation of missing values and oversampling. The experimental results demonstrate that our stacking ensemble models significantly outperform existing single classifiers and AutoML approaches, with improvements of 6.42% and 8.86% in F1 and AUC scores for Type 1, 9.59% and 6.68% for Type 2, and 11.94% and 9.24% for Type 3, respectively. Consequently, our approach improves the prediction of COVID-19 severity levels and potentially assists frontline healthcare providers in making informed decisions. [ABSTRACT FROM AUTHOR]