| Kyung Ah Kim | 3 Articles |
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<b>Objectives</b>
<p>The aim of this research was to determine intra-oral factors that affect halitosis in young women.</p></sec>
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<b>Methods</b>
<p>This study was performed between March 2014 to May 2014, and included 35 women in their 20s with good oral health. Correlation and logistic regression analyses were performed to investigate the change in halitosis immediately, and 1 hour after scaling.</p></sec>
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<b>Results</b>
<p>In both oral gas (OG) and extraoral gas (EG) groups, halitosis was reduced after scaling compared to before scaling. The logistic regression analysis of oral state factors in OG showed that as oral fluid [odds ratio (OR) = 0.792, <italic>p</italic> = 0.045] and dental plaque (OR = 0.940, <italic>p</italic> = 0.016) decreased by 1 unit, the OR in the OG group decreased (> 50). In addition, as glucose levels in the oral cavity (OR = 1.245, <italic>p</italic> = 0.075) and tongue coating index (OR = 2.912, <italic>p</italic> = 0.064) increased by 1 unit, the OR in the OG group increased (> 50). Furthermore, in the EG group, as oral fluid (OR = 0.66, <italic>p</italic> = 0.01) and dental plaque (OR = 0.95, <italic>p</italic> = 0.04) decreased, the OR in the EG group decreased (> 50) significantly.</p></sec>
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<b>Conclusion</b>
<p>To control halitosis, it is necessary to increase oral fluid and decrease the amount of tongue plaque. Furthermore, maintaining a healthy oral environment, aided by regular scaling and removal of dental plaque, may significantly control halitosis.</p></sec>
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<p>N-terminal pro-brain natriuretic peptide (NT-proBNP) is a well-known biomarker for the diagnosis and prognosis of heart failure, and is directly associated with myocardial dysfunction. We evaluated the prognostic value of NT-proBNP for major adverse cardiac events (MACEs) among patients with non-ST-segment elevation myocardial infarction (NSTEMI) from the Korea Acute Myocardial Infarction Registry during their mid-term follow-up period. In this paper, we analyzed NT-proBNP according to various MACE and level of NT-proBNP. We used multivariate logistic regression to determine the risk factors according to MACE type and NT-proBNP levels, and to identify the cutoff value for each MACE by using the receiver operating characteristic (ROC) curve. NT-proBNP was a significant variable among cardiac deaths (<italic>p</italic> = 0.016), myocardial infarction (<italic>p</italic> = 0.000), and coronary artery bypass grafting (CABG) (<italic>p</italic> = 0.000) in patients with MACE compared with those without MACE. Two-vessel coronary artery disease (CAD) (<italic>p</italic> = 0.037) and the maximum creatinine kinase (max-CK) (<italic>p</italic> = 0.031) produced significant results in repeat percutaneous coronary intervention. The area under the ROC curve was found to be statistically significant for cardiac death and CABG. NT-proBNP is a useful predictor for 12-month MACEs among patients with NSTEMI and in those with heart failure. We propose that a new index incorporating NT-proBNP, max-CK, and CAD vessel will be useful as a prognostic indicator of MACEs in the future.</p>
Citations Citations to this article as recorded by
<b>Objectives</b><br/>This study developed deep neural network (DNN) models capable of accurately classifying major adverse cardiac events (MACE) in patients with acute myocardial infarction (AMI) after hospital discharge, across 3 follow-up intervals: 1, 6, and 12 months.
<br/><b>Methods</b><br/>DNN models were constructed to predict post-discharge MACE across 4 categories. Multiple traditional machine learning models were implemented as controls to benchmark the performance of our DNN approach. All models were evaluated based on their ability to predict MACE occurrence during the specified follow-up periods.
<br/><b>Results</b><br/>The DNN models demonstrated superior predictive performance over conventional machine learning methods, achieving high accuracies of 0.922, 0.884, and 0.913 for the 1-month, 6-month, and 12-month follow-up periods, respectively.
<br/><b>Conclusion</b><br/>The high accuracy of our DNN models highlights their practical advantages for AMI diagnosis and guidance of follow-up treatment. These models can serve as valuable decision support tools, enabling clinicians to optimize the overall management of AMI patients and potentially enhance their hospitalization experience.
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