Unsupervised Bidirectional Contrastive Reconstruction and Adaptive Fine-Grained Channel Attention Networks for image dehazing.

Recently, Unsupervised algorithms has achieved remarkable performance in image dehazing. However, the CycleGAN framework can lead to confusion in generator learning due to inconsistent data distributions, and the DisentGAN framework lacks effective constraints on generated images, resulting in the loss of image content details and color distortion. Moreover, Squeeze and Excitation channel attention employs only fully connected layers to capture global information, lacking interaction with local information, resulting in inaccurate feature weight allocation for image dehazing. To solve the above problems, in this paper, we propose an Unsupervised Bidirectional Contrastive Reconstruction and Adaptive Fine-Grained Channel Attention Networks (UBRFC-Net). Specifically, an Unsupervised Bidirectional Contrastive Reconstruction Framework (BCRF) is proposed, aiming to establish bidirectional contrastive reconstruction constraints, not only to avoid the generator learning confusion in CycleGAN but also to enhance the constraint capability for clear images and the reconstruction ability of the unsupervised dehazing network. Furthermore, an Adaptive Fine-Grained Channel Attention (FCA) is developed to utilize the correlation matrix to capture the correlation between global and local information at various granularities promotes interaction between them, achieving more efficient feature weight assignment. Experimental results on challenging benchmark datasets demonstrate the superiority of our UBRFC-Net over state-of-the-art unsupervised image dehazing methods. This study successfully introduces an enhanced unsupervised image dehazing approach, addressing limitations of existing methods and achieving superior dehazing results. The source code is available at https://github.com/Lose-Code/UBRFC-Net.

Application of Prognostic Nutritional Index in the Predicting of Prognosis in Young Adults with Acute Ischemic Stroke.

BACKGROUND: The incidence of ischemic stroke in young adults (18-45 years old) is increasing gradually. However, performing nutritional assessment in stroke patients is often challenging due to the lack of an accepted standard for nutritional assessment. METHODS: Two hundred sixty young stroke patients were recruited in this study and 144 cases in the good prognosis group and 116 cases in the poor prognosis group were scored according to the modified Rankin scale 90 days after treatment. The National Institutes of Health Stroke Scale was performed on admission and discharge of patients. Serum interleukin 6 and high-sensitivity C-reactive protein were detected at patient presentation. The Prognostic Nutritional Index (PNI) was assessed on admission. Calculation formula of PNI score: serum albumin (g/L) + 5× total lymphocyte count (109/L). RESULTS: The PNI at admission of young stroke patients with poor prognosis was higher than that of those with good prognosis. PNI at admission was significantly negatively correlated with National Institutes of Health Stroke Scale at discharge and modified Rankin scale score after 90 days in young stroke patients. PNI at admission was also significantly negatively correlated with serum levels of high-sensitivity C-reactive protein and interleukin -6 at admission. CONCLUSIONS: PNI has a statistically predictive value for the 90-day prognosis of young stroke patients.