PURPOSE: Transforming growth factor beta1 (TGF-beta1) inhibits the growth of bladder cancer cells and this effect is prominent and constant in 253J bladder cancer cells. We performed a microarray analysis to search for genes that were altered after TGF-beta1 treatment to understand the growth inhibitory action of TGF-beta1. MATERIALS AND METHODS: 253J bladder cancer cells were exposed to TGF-beta1 and total RNA was extracted at 6, 24, and 48 hours after exposure. The RNA was hybridized onto a human 22K oligonucleotide microarray and the data were analyzed by using GeneSpring 7.1. RESULTS: In the microarray analysis, a total of 1,974 genes showing changes of more than 2.0 fold were selected. The selected genes were further subdivided into five highly cohesive clusters with high probability according to the time-dependent expression pattern. A total of 310 genes showing changes of more than 2.0 fold in repeated arrays were identified by use of simple t-tests. Of these genes, those having a known function were listed according to clusters. Microarray analysis showed increased expression of molecules known to be related to Smad-dependent signal transduction, such as SARA and Smad4, and also those known to be related to the mitogen-activated protein kinase (MAPK) pathway, such as MAPKK1 and MAPKK4. CONCLUSIONS: A list of genes showing significantly altered expression profiles after TGF-beta1 treatment was made according to five highly cohesive clusters. The data suggest that the growth inhibitory effect of TGF-beta1 in bladder cancer may occur through the Smad-dependent pathway, possibly via activation of the extracellular signal-related kinase 1 and Jun amino-terminal kinases Mitogen-activated protein kinase pathway.
Antineoplastic Agents/*pharmacology ; Cluster Analysis ; Gene Expression Profiling/methods ; Gene Expression Regulation, Neoplastic/*drug effects ; Genes, Neoplasm ; Humans ; MAP Kinase Signaling System/drug effects/genetics ; Neoplasm Proteins/genetics/metabolism ; Oligonucleotide Array Sequence Analysis/methods ; Reverse Transcriptase Polymerase Chain Reaction/methods ; Signal Transduction/drug effects/genetics ; Smad Proteins/genetics/metabolism ; Transforming Growth Factor beta1/*pharmacology ; Tumor Cells, Cultured/drug effects ; Urinary Bladder Neoplasms/*genetics/metabolism/pathology

PURPOSE: Transforming growth factor-beta1 (TGF-beta1) plays a dual role in apoptosis and in proapoptotic responses in the support of survival in a variety of cells. The aim of this study was to determine the function of TGF-beta1 in bladder cancer cells. MATERIALS AND METHODS: The role of TGF-beta1 in bladder cancer cells was examined by observing cell viability by using the tetrazolium dye (MTT) assay after treating the bladder cancer cell lines 253J, 5637, T24, J82, HT1197, and HT1376 with TGF-beta1. Among these cell lines, the 253J and T24 cell lines were coincubated with TGF-beta1 and the pan anti-TGF-beta antibody. Fluorescence-activated cell sorter (FACS) analysis was performed to determine the mechanism involved after TGF-beta1 treatment in 253J cells. RESULTS: All six cell lines showed inhibited cellular growth after TGF-beta1 treatment. Although the T24 and J82 cell lines also showed inhibited cellular growth, the growth inhibition was less than that observed in the other 4 cell lines. The addition of pan anti-TGF-beta antibodies to the culture media restored the growth properties that had been inhibited by TGF-beta1. FACS analysis was performed in the 253J cells and the 253J cells with TGF-beta1. There were no significant differences in the cell cycle between the two treatments. However, there were more apoptotic cells in the TGF-beta1-treated 253J cells. CONCLUSIONS: TGF-beta1 did not stimulate cellular proliferation but was a growth inhibitory factor in bladder cancer cells. However, the pattern of its effects depended on the cell line. TGF-beta1 achieved growth inhibition by enhancing the level of apoptosis.
Antineoplastic Agents/administration & dosage/*pharmacology ; Apoptosis/drug effects ; Cell Line, Tumor/drug effects/pathology ; Cell Proliferation/drug effects ; Cell Separation/methods ; Dose-Response Relationship, Drug ; Drug Screening Assays, Antitumor/methods ; Flow Cytometry/methods ; Humans ; Transforming Growth Factor beta1/administration & dosage/*pharmacology ; Urinary Bladder Neoplasms/*pathology

Norovirus is an important cause of acute nonbacterial gastroenteritis in communities worldwide. It was evaluated the prevalence of norovirus infections in patients with acute gastroenteritis occurring in Seoul from May 2013 to April 2015, with regular surveillance. 7.3% (252/3,485) of the fecal specimens were determined to be positive for noroviruses by reverse transcription-polymerase chain reaction (RT-PCR). Norovirus genogroup distribution was 19.1% (48/252) genogroup GI, 71.4% (180/252) genogroup GII, and 9.5% (24/252) genogroup G1+GII respectively. It was most norovirus detection rates from November 2013 to March 2015. And it was rotavirus 0.2% (7/3,485), astrovirus 0.03% (1/3,485), sapovirus 0.03% (1/3,485) and, it was non-detective on adenovirus. Norovirus genotypes identified were nine kinds of genogroup GI (GI-1, GI-2, GI-3, GI-4, GI-6, GI-7, GI-8, GI-12, GI-14) and eight kinds of genogroup GII (GII-2, GII-3, GII-4, GII-5, GII-6, GII-7, GII-14, GII-16, GII-17). The genetic characteristics of norovirus and the epidemiological patterns of a viral pathogen from acute gastroenteritis patients may give potentially effective data for epidemiological studies in Seoul, Korea.
Adenoviridae ; Epidemiologic Studies ; Gastroenteritis* ; Genotype* ; Humans* ; Korea ; Norovirus* ; Prevalence ; Rotavirus ; Sapovirus ; Seoul*

Korea declared in 2006 that measles had been eliminated; however, a measles outbreak occurred in the southeastern area of Korea in 2011. Active surveillance of measles patients was conducted in Seoul 3 cases were detected in 2013 and 103 cases in 2014. Of 106 confirmed measles patients, 32 cases were within one university in Seongbukgu, and 23 were within three schools in Yongsangu. Students 14~29 years old comprised 78.3% (83/106) of the cases, and 75.5% (80/106) of the measles viruses were of genotype B3. One foreign traveler played an important role in the measles outbreak in Seoul. This measles outbreak in Seoul may provide useful data for future epidemiological studies of measles.
Epidemiologic Studies ; Genotype ; Humans ; Korea ; Measles virus ; Measles* ; Seoul*

Background: Heart rate variability (HRV) extracted from electrocardiogram measured for a short period during a resting state is clinically used as a bio-signal reflecting the emotional state. However, as interest in wearable devices increases, greater attention is being paid to HRV extracted from long-term electrocardiogram, which may contain additional clinical information. The purpose of this study was to examine the characteristics of HRV parameters extracted through long-term electrocardiogram and explore the differences between participants with and without depression and anxiety symptoms. Methods: Long-term electrocardiogram was acquired from 354 adults with no psychiatric history who underwent Holter monitoring. Evening and nighttime HRV and the ratio of nighttime-to-evening HRV were compared between 127 participants with depressive symptoms and 227 participants without depressive symptoms. Comparisons were also made between participants with and without anxiety symptoms. Results: Absolute values of HRV parameters did not differ between groups based on the presence of depressive or anxiety symptoms. Overall, HRV parameters increased at nighttime compared to evening. Participants with depressive symptoms showed a significantly higher nighttime-to-evening ratio of high-frequency HRV than participants without depressive symptoms. The nighttime-to-evening ratio of HRV parameters did not show a significant difference depending on the presence of anxiety symptoms. Conclusion HRV extracted through long-term electrocardiogram showed circadian rhythm. Depression may be associated with changes in the circadian rhythm of parasympathetic tone.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.