This review aims to define the 4 types of the metaverse and to explain the potential and limitations of its educational applications. The metaverse roadmap categorizes the metaverse into 4 types: augmented reality, lifelogging, mirror world, and virtual reality. An example of the application of augmented reality in medical education would be an augmented reality T-shirt that allows students to examine the inside of the human body as an anatomy lab. Furthermore, a research team in a hospital in Seoul developed a spinal surgery platform that applied augmented reality technology. The potential of the metaverse as a new educational environment is suggested to be as follows: a space for new social communication; a higher degree of freedom to create and share; and the provision of new experiences and high immersion through virtualization. Some of its limitations may be weaker social connections and the possibility of privacy impingement; the commission of various crimes due to the virtual space and anonymity of the metaverse; and maladaptation to the real world for students whose identity has not been established. The metaverse is predicted to change our daily life and economy beyond the realm of games and entertainment. The metaverse has infinite potential as a new social communication space. The following future tasks are suggested for the educational use of the metaverse: first, teachers should carefully analyze how students understand the metaverse; second, teachers should design classes for students to solve problems or perform projects cooperatively and creatively; third, educational metaverse platforms should be developed that prevent misuse of student data.

BACKGROUND AND OBJECTIVES: We investigated the predictors of the recovery of depressed left ventricular ejection fraction (LVEF) in patients with moderate or severe left ventricular (LV) systolic dysfunction after acute myocardial infarction (MI). SUBJECTS AND METHODS: We analyzed 1307 patients, who had moderately or severely depressed LVEF (<45%) on echocardiography soon after acute MI and who underwent a follow-up echocardiography, among 27369 patients from the Korea Working Group on the Myocardial Infarction Registry. Patients were categorized into two groups according to recovery of LVEF: group I with consistently depressed LVEF (<45%) at the follow-up echocardiography and group II with a recovery of LVEF (> or =45%). RESULTS: Recovery of LV systolic dysfunction was observed in 51% of the subjects (group II, n=663; DeltaLVEF, 16.2+/-9.3%), whereas there was no recovery in the remaining subjects (group I, n=644; DeltaLVEF, 0.6+/-7.1%). In the multivariate analysis, independent predictors of recovery of depressed LVEF were as follows {odds ratio (OR) [95% confidence interval (CI)]}: moderate systolic dysfunction {LVEF > or =30% and <45%; 1.73 (1.12-2.67)}, Killip class I-II {1.52 (1.06-2.18)}, no need for diuretics {1.59 (1.19-2.12)}, non-ST-segment elevation MI {1.55 (1.12-2.16)}, lower peak troponin I level {<24 ng/mL, median value; 1.55 (1.16-2.07)}, single-vessel disease {1.53 (1.13-2.06)}, and non-left anterior descending (LAD) culprit lesion {1.50 (1.09-2.06)}. In addition, the use of statin was independently associated with a recovery of LV systolic dysfunction {OR (95% CI), 1.46 (1.07-2.00)}. CONCLUSION: Future contractile recovery of LV systolic dysfunction following acute MI was significantly related with less severe heart failure at the time of presentation, a smaller extent of myonecrosis, or non-LAD culprit lesions rather than LAD lesions.
Diuretics ; Echocardiography ; Follow-Up Studies ; Heart Failure ; Humans ; Hydroxymethylglutaryl-CoA Reductase Inhibitors ; Korea ; Multivariate Analysis ; Myocardial Infarction ; Prognosis ; Stroke Volume ; Troponin I

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.