PURPOSE: The purpose of this study was to describe the clinical characteristics of neonatal urinary tract infection (UTI) caused by Klebsiella pneumoniae and non- Klebsiella pneumoniae UTI. METHODS: We compared clinical characteristics of 84 neonatal patients with UTI caused by Klebsiella pneumoniae who were hospitalized at the Department of Pediatricsat Han Dong University, Sunlin Hospital during the period between May, 1994 and August, 1998. The cases were divided into two groups depending upon causative organisms' Klebsiella pneumoniae UTI vs non-Klebsiella pneumoniae UTI, and the clinical characteristics of these groups were compared. RESULTS: Escherichia coli was the most common bacterial pathogen causing neonatal UTI, followed by Klebsiella pneumoniae. There was no significant difference in the sex distribution of Klebsiella pneumoniae UTI, but non-Klebsiella pneumoniae UTI showed male predominence. There were no significant differences in the incidences of hematologic, urologic, radiologic findings and perinatal complications in between these 2 groups. CONCLUSION: Klebsiella pneumoniae is the second most common pathogen causing neonatal UTI. There were no specific differences in the laboratory, symptomatologic, and radiologic findings in these two groups.
Escherichia coli ; Humans ; Incidence ; Klebsiella pneumoniae* ; Klebsiella* ; Male ; Pneumonia ; Sex Distribution ; Urinary Tract Infections* ; Urinary Tract*

We have experienced a case of 13 ring chromosome in a 40-month-old girl who demonstrated psychomotor retardation with delayed speech, growth retardation, hearing loss(left), microcephaly, trigonocephaly with flat occiput, hypertelorism, epicanthal folds, microophthalmia, broad prominamt nasal bridge, high arched palate, micrognathia, large auricles and other anomalies. Cytogenetic studies of peripheral blood lymphocytes with differential staining of chromosomes revealed 46, XX, r13. Her parents' karyotypes were normal. We reported the case with the review of the associated literatures.
Child, Preschool ; Craniosynostoses ; Cytogenetics ; Female ; Hearing ; Humans ; Hypertelorism ; Karyotype ; Lymphocytes ; Microcephaly ; Palate ; Ring Chromosomes*

No abstract available.
Vibration*

No abstract available.
Ammonia* ; Pulmonary Fibrosis*

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

In order to observe the changes in left ventricular function during coronary artery occlusion and reperfusion, left anterior descending (LAD) coronary arteries in the anesthetized dogs were occluded for 1 hour and then reperfused for 4 hours. Hemodynamic indexes of global systolic and diastolic function and regional wall thickness changes as a regional contractile index were measured during occlusion and reperfusion. The results were as follows; 1) Indexes of global systolic function (left ventricular peak systolic pressure, peak positive dP/dt) and global diastolic function (peak negative dP/dt, time constant, left ventricular end-diastolic pressure) showed deterioration in early occlusion period (10-30 minutes) but gradually improved even if coronary occlusion persisted. Reperfusion did not induce significant changes except that peak positive dP/dt transiently deteriorated 30 minutes after reperfusion and left ventricular end-diastolic pressure decreased 1.5-2 hours after reperfusion. 2) Indexed of regional function (i.e, end-diastolic thickness and % systolic thickening of anterior left ventricular wall) deteriorated by 10 minutes' occlusion which persisted during the entire occlusion period. Reperfusion induced no significant improvement in regional contractile function compared with occlusion 60 minutes' data, which suggested reperfusion for 4 hours after 1 hour's LAD occlusion may be insufficient for the ischemic region to recover its contractility. 3) Reperfusion arrhythmia (ventricular tachycardia) was noted in most (6/9) of the dogs, one of which deteriorated into ventricular fibrillation and the others spontaneously converted to normal sinus rhythm.
Animals ; Arrhythmias, Cardiac ; Blood Pressure ; Coronary Occlusion ; Coronary Vessels* ; Dogs ; Hemodynamics ; Reperfusion* ; Ventricular Fibrillation ; Ventricular Function, Left*