No abstract available.
Aged ; Humans ; Levofloxacin ; Plasma

Diagnosis of Kawasaki disease (KD) is occasionally delayed because it is solely based on clinical symptoms. Previous studies have attempted to identify diagnostic biomarkers for KD. Recently, patients with KD were reported to have elevated serum ferritin levels. We investigated the usefulness of the serum ferritin level as a diagnostic biomarker for distinguishing KD from other acute febrile illnesses. Blood samples were obtained from pediatric patients with KD (N = 77) and those with other acute febrile illnesses (N = 32) between December 2007 and June 2011 for measuring various laboratory parameters, including serum ferritin levels. In patients with KD, laboratory tests were performed at diagnosis and repeated at 2, 14, and 56 days after intravenous immunoglobulin treatment. At the time of diagnosis, serum ferritin levels in patients with KD (188.8 µg/L) were significantly higher than those in patients with other acute febrile illnesses (106.8 µg/L, P = 0.003). The serum ferritin cut-off value of 120.8 µg/L effectively distinguished patients with KD from those with other acute febrile illnesses, with a sensitivity and specificity of 74.5% and 83.3%, respectively. Serum ferritin may be a useful biomarker to distinguish KD from other acute febrile illnesses.

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters;minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians’ decisionmaking by real-time application in the catheterization laboratory.

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters;minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians’ decisionmaking by real-time application in the catheterization laboratory.

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters;minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians’ decisionmaking by real-time application in the catheterization laboratory.

BACKGROUND: Risperidone is one of the atypical antipsychotic drugs that have effectiveness in the management of a range of psychiatric illnesses. Orally disintegrating (OD) formulations of risperidone that rapidly dissolve in the mouth, prior to swallowing without water have been developed to overcome any problems related to swallowing and improve acceptability. The goal of this study was to evaluate the bioequivalence of Risperdal OD(R) tablet 1mg and Quicklet(R) tablet 1mg. METHODS: This randomized, open-label, 2-way crossover trial was conducted in 36 healthy male volunteers that received OD risperidone tablet, either the reference formulation (Risperdal Quicklet(R) tablet 1mg), or the test formulation (Risperdal OD(R) tablet 1mg), each in a single administration. Blood samples were obtained during a 24-hour period after dosing. Plasma was analyzed for risperidone by a validated LC-MS/MS. Adverse events were monitored by safety assessments including clinical interview by clinician. Pharmacokinetics were calculated by noncompartmental analysis and compared between two formulations. RESULTS: A total of 36 male volunteers (mean age, 24.2 years; height 174.5 cm; weight 67.6 kg) completed the study. The ANOVA showed no significant effect of sequence, formulation and period of Ln (AUClast) and Ln (Cmax). The 90% confidence intervals for the mean treatment ratios of the Ln (AUClast) and Ln (Cmax) were Ln 0.96 ~ Ln 1.12, Ln 0.97 ~ Ln 1.16, respectively. No serious adverse events were caused by both formulations. CONCLUSION: In this study, a single administration of Risperdal OD(R) tablet 1mg was bioequivalent to a single administration of Risperdal Quicklet(R) tablet 1mg.
Antipsychotic Agents ; Deglutition ; Humans ; Male ; Mouth ; Plasma ; Risperidone ; Therapeutic Equivalency

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1–16 segments) were high (85.5– 100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

Enteropathy-associated T-cell lymphoma (EATL) is a rare extranodal T-cell lymphoma arising from the intestine. Two types of EATL have been reported. In contrast to the classic EATL type I, EATL type II occurs sporadically, is unrelated to celiac disease, and comprises 10% to 20% of all EATL cases. A total of five cases of EATL type II were diagnosed at our clinic from January 2009 to September 2012. Four of the five patients were diagnosed with the help of endoscopy. Among the four patients, two of the cases involved both the small and large intestines, whereas in the other two patients, EATL was limited to the small intestine. Common endoscopic findings included innumerable fine granularities (also called mosaic mucosal patterns) and diffuse thickening of the mucosa with a semicircular shallow ulceration in the lesions of the small bowel. In contrast, the endoscopic findings of the colon were nonspecific and could not distinguish EATL type II from other diseases. There are only few published reports regarding the representative endoscopic findings of EATL. Here, we present the clinical and endoscopic findings of four cases of EATL type II diagnosed by endoscopy.
Aged ; Celiac Disease/complications ; *Colonoscopy ; Enteropathy-Associated T-Cell Lymphoma/etiology/*pathology ; Female ; Humans ; Intestinal Mucosa/pathology ; Intestine, Large/pathology ; Intestine, Small/pathology ; Male ; Middle Aged

PURPOSE: Few studies have been investigated the in vivo efficacy of generic vancomycin products available outside of the United States. In this study, we aimed to compare the in vivo pharmacokinetics (PK) and pharmacodynamics (PD) of five generic vancomycin products available in Korea with those of the innovator.MATERIALS AND METHODS: The in vitro vancomycin purity of each product was examined using high-pressure liquid chromatography. Single-dose PK analyses were performed using neutropenic mice. The in vivo efficacy of vancomycin products was compared with that of the innovator in dose-effect experiments (25 to 400 mg/kg per day) using a thigh-infection model with neutropenic mice.RESULTS: Generic products had a lower proportion of vancomycin B (range: 90.3–93.8%) and a higher proportion of impurities (range: 6.2–9.7%) than the innovator (94.5% and 5.5%, respectively). In an in vivo single-dose PK study, the maximum concentration (C(max)) values of each generic were lower than that of the innovator, and the geographic mean area under the curve ratios of four generics were significantly lower than that of the innovator (all p<0.1). In the thigh-infection model, the maximum efficacies of generic products reflected in maximal effect (E(max)) values were not significantly different from the innovator. However, the PD profile curves of some generic products differed significantly from that of the innovator in mice injected with a high level of Mu3 (all p≤0.05).CONCLUSION: Some generic vancomycin products available in Korea showed inferior PK and PD profiles, especially in hetero-vancomycin-resistant mice infected with Staphylococcus aureus.