现已认识到免疫反应、转录因子核因子κB( NF-κB) 的激活、细胞因子、中性粒细胞的激活和肺泡渗入、凝血级联反应、肾素-血管紧张素系统等多种因素构成的复杂网络参与急性肺损伤/急性呼吸窘迫综合征( ALI/ARDS) 的发病过程[ 1-5] 。虽然脓毒症、创伤、肺炎等ALI/ARDS诱发因素很常见, 但仅有部分病人发生ALI/ARDS, 并且具有相似临床特征的ALI/ARDS病人可有截然不同的结果, 这种异质性引起研究者对影响ALI/ARDS 易感性和预后的遗传因子进行鉴别的浓厚兴趣[ 6] 。由于数量庞大的表现型变异, 不完全的基因外显率、复杂的基因-环境相互作用及高度可能的基因座不均一性而使ALI 遗传学的研究受到挑战[ 7] 。近年来基因组学技术被应用于ALI/ARDS 发病机制的研究, 加深了人们对ALI/ARDS的认识并有可能发展出新的治疗策略以降低其发病率和病死率。
With the development of life sciences and informatics, bioinformatics is developing as an interdisciplinary subject. Its main application is the relationship between genes and proteins and their expression. With the help of genomics, proteomics, transcriptomics, and metabolomics, researchers introduce bioinformatics research methods into fundus disease research. A series of gratifying research results have been achieved including the screening of genetic susceptibility genes, the screening of diagnostic markers, and the exploration of pathogenesis. Genomics has the characteristics of high efficiency and accuracy. It has been used to detect new mutation sites in retinoblastoma and retinal pigment degeneration research, which helps to further improve the pathogenesis of retinal genetic diseases. Transcriptomics, proteomics, and metabolomics have high throughput characteristics. They are used to analyze changes in the expression profiles of RNA, proteins, and metabolites in intraocular fluid or isolated cells in disease states, which help to screen biomarkers and further elucidate the pathogenesis. With the advancement of technology, bioinformatics will provide new ideas for the study of ocular fundus diseases.
UK Biobank is an extensive biomedical database and research resource. It contains in-depth genetic and health information from 500 000 UK subjects, comprising a wealth of basic structured data, high-throughput genomic and genetic data, and multimodal imaging data. However, difficulties in accessing the large amount of data mean that the database has not been widely used in China. We first introduced the health-related structural data, genetic data, and imaging data in the UK Biobank. We then described methods for using different types of data downloaded from UK Biobank, and explored recent research based on these data. We also discussed classic research focusing on applying artificial intelligence technology to UK Biobank data. Finally, we predicted future research trends in the utilization of UK Biobank data in areas such as anatomy, physiology, genetic variation, and phenotypic characteristics.