RESUMO
OBJECTIVE: This study aims to investigate the methods for rat spinal cord ischemia injury models with a high long-term survival rate. METHODS: The rats were divided into three groups: the treatment group, the control group, and the sham operation group. The treatment group had a blocked thoracic aorta (landing zone 3 by Ishimaru - T11) + aortic bypass circulation for 20 min. In the control group, the thoracic aorta at the landing zone 3 was blocked for 20 min. In the sham operation group, only thoracotomy without thoracic aortic occlusion was performed. The mean arterial blood pressure (MABP) of the thoracic aorta and caudal artery before and after thoracic aortic occlusion was monitored intraoperatively. Spinal cord function was monitored by a transcranial motor evoked potential (Tc-MEP) during the operation. Spinal cord function was evaluated by the BBB scale (Basso, Beattie, & Bresnahan locomotor rating scale) scores at multiple postoperative time points. The spinal cord sections of the rats were observed for 7 days after surgery, and the survival curves were analyzed for 28 days after surgery. RESULTS: After aortic occlusion, the MABP of thoracic aorta decreased to 6% of that before occlusion, and the MABP of caudal artery decreased to 63% of that before occlusion in the treatment group. In the control group, the MABP of both thoracic aorta and caudal artery decreased to 19% of that before occlusion. The Tc-MEP waveform of the treatment group disappeared after 6 min, and that of the control group disappeared after 8 min until the end of surgery. There was no change in the Tc-MEP waveform in the sham operation group. The BBB score of the treatment group decreased more obviously than the control group, and there was a significant difference. There was no decrease in the sham group. Spinal cord sections showed a large number of degeneration and necrosis of neurons, infiltration of inflammatory cells, and proliferation of surrounding glial cells in the treatment group. In the control group, multiple neurons were necrotic. The histology of the sham operation group was normal. The 28-day survival rate of the treatment group was 73.3%, which was higher than the control group (40.0%), and there was a significant difference (p < 0.05). CONCLUSION: Thoracic aortic occlusion combined with aortic bypass is an effective modeling method for rats with accurate modeling effects and high long-term survival rates.
Assuntos
Doenças da Aorta , Arteriopatias Oclusivas , Isquemia do Cordão Espinal , Ratos , Animais , Isquemia do Cordão Espinal/etiologia , Isquemia , Medula Espinal/irrigação sanguínea , Medula Espinal/patologia , Medula Espinal/fisiologia , Aorta Torácica/diagnóstico por imagem , Aorta Torácica/cirurgia , Aorta Torácica/patologia , Doenças da Aorta/patologia , Necrose/patologiaRESUMO
Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for multispectral image pansharpening, called LRTCFPan. The tensor completion technique is commonly used for image recovery, but it cannot directly perform the pansharpening or, more generally, the super-resolution problem because of the formulation gap. Different from previous variational methods, we first formulate a pioneering image super-resolution (ISR) degradation model, which equivalently removes the downsampling operator and transforms the tensor completion framework. Under such a framework, the original pansharpening problem is realized by the LRTC-based technique with some deblurring regularizers. From the perspective of regularizer, we further explore a local-similarity-based dynamic detail mapping (DDM) term to more accurately capture the spatial content of the panchromatic image. Moreover, the low-tubal-rank property of multispectral images is investigated, and the low-tubal-rank prior is introduced for better completion and global characterization. To solve the proposed LRTCFPan model, we develop an alternating direction method of multipliers (ADMM)-based algorithm. Comprehensive experiments at reduced-resolution (i.e., simulated) and full-resolution (i.e., real) data exhibit that the LRTCFPan method significantly outperforms other state-of-the-art pansharpening methods. The code is publicly available at: https://github.com/zhongchengwu/code_LRTCFPan.