Temporal Trend of the Incidence and Characteristics of Renal Infarction: Korean Nationwide Population Study.

BACKGROUND: Large-scale studies about epidemiologic characteristics of renal infarction (RI) are few. In this study, we aimed to analyze the incidence and prevalence of RI with comorbidities in the South Korean population. METHODS: We investigated the medical history of the entire South Korean adult population between 2013 and 2019 using the National Health Insurance Service database (n = 51,849,591 in 2019). Diagnosis of RI comorbidities were confirmed with International Classification of Disease, Tenth Revision, Clinical Modification codes. Epidemiologic characteristics, distribution of comorbidities according to etiologic mechanisms, and trend of antithrombotic agents were estimated. RESULTS: During the 7-years, 10,496 patients were newly diagnosed with RI. The incidence rate increased from 2.68 to 3.06 per 100,000 person-years during the study period. The incidence rate of RI increased with age peaking in the 70s with 1.41 times male predominance. The most common comorbidity was hypertension, followed by dyslipidemia and diabetes mellitus. Regarding etiologic risk factor distribution, high embolic risk group, renovascular disease group, and hypercoagulable state group accounted for 16.6%, 29.1%, and 13.7% on average, respectively. For the antithrombotic treatment of RI, the prescription of antiplatelet agent gradually decreased from 17.0% to 13.0% while that of anticoagulation agent was maintained around 35%. The proportion of non-vitamin K antagonist oral anticoagulants remarkably increased from only 1.4% to 17.6%. CONCLUSION: Considering the progressively increasing incidence of RI and high prevalence of coexisting risk factors, constant efforts to raise awareness of the disease are necessary. The current epidemiologic investigation of RI would be the stepping-stone to establishing future studies about clinical outcomes and optimal treatment strategies.

A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint.

For decades, co-relating different data domains to attain the maximum potential of machines has driven research, especially in neural networks. Similarly, text and visual data (images and videos) are two distinct data domains with extensive research in the past. Recently, using natural language to process 2D or 3D images and videos with the immense power of neural nets has witnessed a promising future. Despite the diverse range of remarkable work in this field, notably in the past few years, rapid improvements have also solved future challenges for researchers. Moreover, the connection between these two domains is mainly subjected to GAN, thus limiting the horizons of this field. This review analyzes Text-to-Image (T2I) synthesis as a broader picture, Text-guided Visual-output (T2Vo), with the primary goal being to highlight the gaps by proposing a more comprehensive taxonomy. We broadly categorize text-guided visual output into three main divisions and meaningful subdivisions by critically examining an extensive body of literature from top-tier computer vision venues and closely related fields, such as machine learning and human-computer interaction, aiming at state-of-the-art models with a comparative analysis. This study successively follows previous surveys on T2I, adding value by analogously evaluating the diverse range of existing methods, including different generative models, several types of visual output, critical examination of various approaches, and highlighting the shortcomings, suggesting the future direction of research.