BACKGROUND: Although the incidence of resistance to macrolides in group A streptococci (GAS) was low in the past, high incidences have now been reported from several countries. We tried to find out trends of the incidence of erythromycin-resistant GAS in Korea before and after adopting the separation of the dispensary from medical practice in the middle of the year 2000. METHODS: Five hundred thirty two isolates from children with suspected pharyngotonsillitis from 1998 through 2002 were serotyped by T-agglutination. Minimal inhibitory concentrations of 330 out of 532 isolates were determined by agar dilution methods. RESULTS: The prevalent T-serotypes were T12 (36.1%), T4 (12.8%), T1 (10.9%), T2/28 (8.8%), and nontypable (7.1%). Resistance rates to erythromycin (EM) by year were 46.2% in 1998, 18.4% in 1999, 15.4% in 2000, 27.6% in 2001, 36.5% in 2002. T12 in 1999 and 2000 were 36.4% and 25.9%, respectively, which seem to be lower than any other year. This relative low percentage of T12 is associated with increasing percentage of T1 in the same year. The frequency of T12, T1,and T4 were high in each group of isolates of Seoul and Masan. From this viewpoint, there was a similarity between the distribution of T-serotypes of both groups of Seoul and Masan. CONCLUSION: The frequency of serotype T12 and T4 of GAS were relatively high in Korea from 1998 through 2002. The low rate of EM resistance in 1999 and 2000 seemed to be caused by a sudden increase of T1. The increasing rate of EM resistance from 2000 to 2002 seemed to be caused by the increase in consumption of new macrolides and the increase of T12.
Agar ; Child ; Erythromycin ; Humans ; Incidence* ; Korea* ; Macrolides ; Seoul

BACKGROUND: Although the incidence of resistance to macrolides in group A streptococci (GAS) was low in the past, high incidences have now been reported from several countries. We tried to find out trends of the incidence of erythromycin-resistant GAS in Korea before and after adopting the separation of the dispensary from medical practice in the middle of the year 2000. METHODS: Five hundred thirty two isolates from children with suspected pharyngotonsillitis from 1998 through 2002 were serotyped by T-agglutination. Minimal inhibitory concentrations of 330 out of 532 isolates were determined by agar dilution methods. RESULTS: The prevalent T-serotypes were T12 (36.1%), T4 (12.8%), T1 (10.9%), T2/28 (8.8%), and nontypable (7.1%). Resistance rates to erythromycin (EM) by year were 46.2% in 1998, 18.4% in 1999, 15.4% in 2000, 27.6% in 2001, 36.5% in 2002. T12 in 1999 and 2000 were 36.4% and 25.9%, respectively, which seem to be lower than any other year. This relative low percentage of T12 is associated with increasing percentage of T1 in the same year. The frequency of T12, T1,and T4 were high in each group of isolates of Seoul and Masan. From this viewpoint, there was a similarity between the distribution of T-serotypes of both groups of Seoul and Masan. CONCLUSION: The frequency of serotype T12 and T4 of GAS were relatively high in Korea from 1998 through 2002. The low rate of EM resistance in 1999 and 2000 seemed to be caused by a sudden increase of T1. The increasing rate of EM resistance from 2000 to 2002 seemed to be caused by the increase in consumption of new macrolides and the increase of T12.
Agar ; Child ; Erythromycin ; Humans ; Incidence* ; Korea* ; Macrolides ; Seoul

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.