Background: Aspirin is a proven antiplatelet agent in acute ischemic stroke, and there are no current guidelines for other antiplatelet treatments. We aimed to compare the efficacy and safety of cilostazol with aspirin in acute stroke. Methods: Patients with measurable neurological deficits (NIHSS score ≤15) within 48 h of onset were randomly assigned to cilostazol (200 mg/day) or aspirin (300 mg/day) for 90 days. The primary endpoint was a modified Rankin Scale (mRS) score of 0–2 at 90 days. Cardiovascular events, bleeding complications, and other functional outcomes were also assessed. Statistical analysis was carried out by intention-to-treat and per-protocol bases. This trial is registered with ClinicalTrials.gov (NCT00272454). Results: In total, 458 patients were enrolled (mean age of 63 years, median NIHSS of 3), and mRS at 90 days was obtained in 447 patients. The primary endpoint was achieved in 76% (173/228) of those randomized to cilostazol and in 75% (165/219) assigned to aspirin, which supported the pre-specified non-inferiority of cilostazol to aspirin (95% CI of proportion difference: –6.15 to 7.22%, p = 0.0004). These results were also supported by per-protocol analysis (p = 0.045). Cardiovascular events occurred in 6 patients (3%) treated with cilostazol, and in 9 patients (4%) treated with aspirin (p = 0.41). Adverse events were more common in cilostazol-treated patients during the trial (91 vs. 85%, p = 0.055), while the frequencies of bleeding complications (cilostazol 11%, aspirin 13%, p = 0.43) or drug discontinuation (cilostazol 10%, aspirin 7%, p = 0.32) were not different. Conclusion: Cilostazol is feasible in acute ischemic stroke, and comparable to aspirin in its efficacy and safety.

Text-to-image technology enables computers to create images from text by simulating the human process of forming mental images. GAN-based text-to-image technology involves extracting features from input text; subsequently, they are combined with noise and used as input to a GAN, which generates images similar to the original images via competition between the generator and discriminator. Although images have been extensively generated from English text, text-to-image technology based on multilingualism, such as Korean, is in its developmental stage. Webtoons are digital comic formats for viewing comics online. The webtoon creation process involves story planning, content/sketching, coloring, and background drawing, all of which require human intervention, thus being time-consuming and expensive. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs multilingual BERT to extract feature vectors for multiple languages and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images similar to the original images when presented with multilingual input text after training. The evaluation metrics further support these findings, as the generated images achieved an Inception score of 4.99 and an FID score of 22.21.

Recent advances in deep learning technology have led to increased interest in text-to-image technology, which enables computers to create images from text by simulating the human process of forming mental images. The GAN-based text-to-image technology involves the extraction of features from input text, which are combined with noise and then used as input to a GAN that generates images that are similar to the original images through competition between the generator and discriminator. Although generating images from English text is a mature area of research, text-to-image technology based on multilingualism, such as Korean, is still in its early stages of development. Webtoon is a digital comic format that allows comics to be viewed online. The creation process for webtoons is divided into story planning, content/sketching, coloring, and background drawing. Since each stage of webtoon production requires human intervention, it is both time-consuming and expensive. As a result, deep learning technologies such as automatic coloring and automatic line drawing are being used to reduce human involvement. However, there is a shortage of technology that can assist authors with story creation in webtoon production. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs Multilingual BERT to extract feature vectors for multiple languages, and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images that are similar to the original images when presented with multilingual input text after training.

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Recently, three-dimensional (3D) content used in various fields has attracted attention owing to the development of virtual reality and augmented reality technologies. To produce 3D content, we need to model the objects as vertices. However, high-quality modeling is time-consuming and costly. Drawing-based modeling is a technique that shortens the time required for modeling. It refers to creating a 3D model based on a user’s line drawing, which is a 3D feature represented by two-dimensional (2D) lines. The extracted line drawing provides information about a 3D model in the 2D space. It is sometimes necessary to generate a line drawing from a 2D cartoon image to represent the 3D information of a 2D cartoon image. The extraction of consistent line drawings from 2D cartoons is difficult because the styles and techniques differ depending on the designer who produces the 2D cartoons. Therefore, it is necessary to extract line drawings that show the geometric characteristics well in 2D cartoon shapes of various styles. This paper proposes a method for automatically extracting line drawings. The 2D cartoon shading image and line drawings are learned using a conditional generative adversarial network model, which outputs the line drawings of the cartoon artwork. The experimental results show that the proposed method can obtain line drawings representing the 3D geometric characteristics with a 2D line when a 2D cartoon painting is used as the input.