Fucoidan is an l-fucose-enriched sulfated polysaccharide isolated from brown algae and marine invertebrates. In this study, we investigated the protective effect of fucoidan from Fucus vesiculosus on alcohol-induced murine liver damage. Liver injury was induced by oral administration of 25% alcohol with or without fucoidan (30 mg/kg or 60 mg/kg) for seven days. Alcohol administration increased serum aspartate aminotransferase and alanine aminotransferase levels, but these increases were suppressed by the treatment of fucoidan. Transforming growth factor beta 1 (TGF-β1), a liver fibrosis-inducing factor, was highly expressed in the alcohol-fed group and human hepatoma HepG2 cell; however, the increase in TGF-β1 expression was reduced following fucoidan administration. Treatment with fucoidan was also found to significantly reduce the production of inflammation-promoting cyclooygenase-2 and nitric oxide, while markedly increasing the expression of the hepatoprotective enzyme, hemeoxygenase-1, on murine liver and HepG2 cells. Taken together, the antifibrotic and anti-inflammatory effects of fucoidan on alcohol-induced liver damage may provide valuable insights into developing new therapeutics or interventions.

A better understanding of the clinical characteristics of coronavirus disease 2019 (COVID-19) is urgently required to address this health crisis. Numerous researchers and pharmaceutical companies are working on developing vaccines and treatments; however, a clear solution has yet to be found. The current study proposes the use of artificial intelligence methods to comprehend biomedical knowledge and infer the characteristics of COVID-19. A biomedical knowledge base was established via FastText, a word embedding technique, using PubMed literature from the past decade. Subsequently, a new knowledge base was created using recently published COVID-19 articles. Using this newly constructed knowledge base from the word embedding model, a list of anti-infective drugs and proteins of either human or coronavirus origin were inferred to be related, because they are located close to COVID-19 on the knowledge base. This study attempted to form a method to quickly infer related information about COVID-19 using the existing knowledge base, before sufficient knowledge about COVID-19 is accumulated. With COVID-19 not completely overcome, machine learning-based research in the PubMed literature will provide a broad guideline for researchers and pharmaceutical companies working on treatments for COVID-19.

Precision medicine has received a lot of attention in recent years and we have not yet found any research cases that apply Data Envelopment Analysis (DEA) to investment decision making in this area. The purpose of this study is to analyze the relative efficiency of candidate technology sectors in order to determine priorities for government investment in precision medicine. The results of the efficiency analysis can be used as an important reference for government policy makers to determine the amount of government investment in the next year for each candidate technology sector. The candidate technology for government investment in precision medicine was decided for 23 sectors based on the data analysis and the opinions of expert committees. This study applies the input-oriented DEA in regard to 23 technology sectors, which is widely used to analyze relative efficiency in terms of inputs versus outputs and to enhance efficiency through the propositional reduction of inputs. The input variables include the government’s research and development (R&D) investment and forward and backward industry linkage effects. The output variables are the employment creation effect, value-added effect, number of Korean patents, and number of Korean papers. Our analysis results show that the 23 technology sectors in precision medicine overall have a high efficiency, with the exception of the biobank technology sector. Therefore, since the Biobank technology sector has strong infrastructure characteristics, it seems to require continuous investment. The efficiency of DEA is high in most precision medicine sectors; therefore, overall, investing in these technologies is expected to yield good benefits.