Severe retinal hemorrhages at various levels with a serous retinal detachment in a pediatric patient with aplastic anemia–A case report

BackgroundAplastic anemia can cause ophthalmic abnormalities in patients. Vision loss in a child with aplastic anemia due to massive retinal hemorrhages at various levels is rare.Case presentationA pediatric patient with aplastic anemia presented with retinal hemorrhages at multiple levels along with a serous retinal detachment in both eyes and subsequent retinal changes after pars plana vitrectomy.ConclusionAnemia and thrombocytopenia in aplastic anemia could cause severe retinal hemorrhages and result in retinal atrophy and retinal edema. Vitrectomy can be performed to remove vitreous hemorrhage, but risk factors for retinal atrophy and edema need further investigation

Neurometabolic and structural alterations of medial septum and hippocampal CA1 in a model of post-operative sleep fragmentation in aged mice: a study combining 1H-MRS and DTI

Post-operative sleep disturbance is a common feature of elderly surgical patients, and sleep fragmentation (SF) is closely related to post-operative cognitive dysfunction (POCD). SF is characterized by sleep interruption, increased number of awakenings and sleep structure destruction, similar to obstructive sleep apnea (OSA). Research shows that sleep interruption can change neurotransmitter metabolism and structural connectivity in sleep and cognitive brain regions, of which the medial septum and hippocampal CA1 are key brain regions connecting sleep and cognitive processes. Proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive method for the evaluation of neurometabolic abnormalities. Diffusion tensor imaging (DTI) realizes the observation of structural integrity and connectivity of brain regions of interest in vivo. However, it is unclear whether post-operative SF induces harmful changes in neurotransmitters and structures of the key brain regions and their contribution to POCD. In this study, we evaluated the effects of post-operative SF on neurotransmitter metabolism and structural integrity of medial septum and hippocampal CA1 in aged C57BL/6J male mice. The animals received a 24-h SF procedure after isoflurane anesthesia and right carotid artery exposure surgery. 1H-MRS results showed after post-operative SF, the glutamate (Glu)/creatine (Cr) and glutamate + glutamine (Glx)/Cr ratios increased in the medial septum and hippocampal CA1, while the NAA/Cr ratio decreased in the hippocampal CA1. DTI results showed post-operative SF decreased the fractional anisotropy (FA) of white matter fibers in the hippocampal CA1, while the medial septum was not affected. Moreover, post-operative SF aggravated subsequent Y-maze and novel object recognition performances accompanied by abnormal enhancement of glutamatergic metabolism signal. This study suggests that 24-h SF induces hyperglutamate metabolism level and microstructural connectivity damage in sleep and cognitive brain regions in aged mice, which may be involved in the pathophysiological process of POCD

Superresolution Reconstruction of Video Based on Efficient Subpixel Convolutional Neural Network for Urban Computing

Video surveillance is an important data source of urban computing and intelligence. The low resolution of many existing video surveillance devices affects the efficiency of urban computing and intelligence. Therefore, improving the resolution of video surveillance is one of the important tasks of urban computing and intelligence. In this paper, the resolution of video is improved by superresolution reconstruction based on a learning method. Different from the superresolution reconstruction of static images, the superresolution reconstruction of video is characterized by the application of motion information. However, there are few studies in this area so far. Aimed at fully exploring motion information to improve the superresolution of video, this paper proposes a superresolution reconstruction method based on an efficient subpixel convolutional neural network, where the optical flow is introduced in the deep learning network. Fusing the optical flow features between successive frames can compensate for information in frames and generate high-quality superresolution results. In addition, in order to improve the superresolution, a superpixel convolution layer is added after the deep convolution network. Finally, experimental evaluations demonstrate the satisfying performance of our method compared with previous methods and other deep learning networks; our method is more efficient

Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm

It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties and axial compression behavior is highly nonlinear. These challenges have prompted the use of soft computing methods to predict the ultimate bearing capacity (abbreviated as Nu) under axial compression. Taking the square CFST short column as an example, a mass of experimental data is obtained through axial compression tests. Combined with support vector machine (SVM) and particle swarm optimization (PSO), this paper presents a new method termed PSVM (SVM optimized by PSO) for Nu value prediction. The nonlinear relationship in Nu value prediction is efficiently represented by SVM, and PSO is used to select the model parameters of SVM. The experimental dataset is utilized to verify the reliability of the PSVM model, and the prediction performance of PSVM is compared with that of traditional design methods and other benchmark models. The proposed PSVM model provides a better prediction of the ultimate axial capacity of square CFST short columns. As such, PSVM is an efficient alternative method other than empirical and theoretical formulas