基于环磷酸鸟苷-腺苷合成酶/膜蛋白干扰素基因刺激因子通路探讨奥拉西坦对脑出血大鼠神经功能的影响
Journal of Clinical Neurology（2022）
Abstract
目的 从环磷酸鸟苷-腺苷合成酶(cGAS)/膜蛋白干扰素基因刺激因子(STING)通路方面,探讨奥拉西坦(ORC)对脑出血大鼠认知功能的影响.方法 将SPF级SD大鼠随机分为假手术组、模型组、ORC组(50 mg/kg)、RU.521组(cGAS抑制剂,50 mg/kg)、DMXAA组(STING激活剂,25 mg/kg)、ORC+DMXAA组,每组15只.用Longa法测大鼠神经功能变化;干湿比重法测脑含水量;电镜测血肿周围病理变化;铁染色法测血肿周围组织铁沉积,以评估血肿程度;TUNEL法测血肿周围组织神经元凋亡状况;免疫组化法测血肿周围组织cGAS阳性表达.Western blotting法测STING、TNF-α、IL-12、半胱氨酸天冬氨酸蛋白水解酶3(caspase3)、B淋巴细胞瘤-2基因(Bcl-2)、转铁蛋白(Tf)、转铁蛋白受体(Tf R)、水通道蛋白4(APQ4)蛋白表达.结果 与假手术组相比,模型组大鼠死亡、偏瘫及侧旋转等神经功能缺损行为评分明显升高,血肿周围组织神经元水肿及凋亡明显增加,脑水肿、铁沉积严重,cGAS/STING通路蛋白及其介导的炎症反应和促凋亡途径激活(均P<0.05).ORC组及RU.521组大鼠神经功能缺损评分明显降低、血肿周围组织神经元损伤、凋亡、脑水肿及铁沉积均明显缓解,cGAS/STING通路蛋白及其介导的炎症反应和促凋亡相关蛋白表达明显降低(均P<0.05).DMXAA可逆转ORC的上述作用(P<0.05).结论 ORC可能通过抑制cGAS/STING通路活化,缓解脑出血大鼠血肿周围神经元炎症损伤及凋亡,减轻神经功能缺损.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined