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

With an incidence of 1 per 2,500-3,000 individuals, neurofibromatosis type 1 (NF1) is the most common autosomal dominant disorder in humans. NF1 is caused by germline mutations of the NF1 gene, but to date genotype-phenotype analyses have indicated no clear relationship between specific gene mutations and the clinical features of this disease, even among family members with the same mutation. The present study describes a case of two siblings with NF1 with the same genetic mutation but different clinical manifestations. The first patient was a female with iris Lisch nodules, an adrenal incidentaloma, Graves' disease, and skin manifestations, while the second patient, the first patient's younger brother, exhibited only skin neurofibromas and freckling. Further study is needed to reveal the molecular processes underlying gene expression and phenotypes. A better understanding of the genetics associated with NF1 will allow clinicians to detect complications earlier and provide better genetic counseling to NF1 families.
Female ; Gene Expression ; Genes, Neurofibromatosis 1* ; Genetic Counseling ; Genetics ; Germ-Line Mutation ; Graves Disease ; Humans ; Incidence ; Iris ; Neurofibroma ; Neurofibromatoses* ; Neurofibromatosis 1* ; Phenotype ; Siblings ; Skin ; Skin Manifestations

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.