Ileal atresia, a subtype of intestinal atresia, is one of the well-recognized causes of bowel obstruction in newborns. Prenatal diagnosis of intestinal atresia is very important in its management and outcome. Unfortunately, there are few cases of ileal atresia diagnosed prenatally, so more appropriate diagnoses and management plans are needed. As an associated gastrointestinal malformation with ileal atresia, hypertrophic pyloric stenosis is rarely reported. We report one case of postnatally diagnosed ileal atresia associated with hypertrophic pyloric stenosis which was complicated initially by bowel perforation and later by vomiting due to pyloric obstruction. Vomiting in the postoperative period is a common problem. But, if vomiting continues after the operation for ileal atresia, hypertrophic pyloric stenosis should be considered as a possible cause of medically retractable non-bilious vomiting.
Diagnosis ; Humans ; Infant, Newborn ; Intestinal Atresia ; Postoperative Period ; Prenatal Diagnosis ; Pyloric Stenosis, Hypertrophic* ; Vomiting

BACKGROUND: We addressed the problem of the circadian changes in neural control of the circulation in ambulant hypertensive subjects. With spectral analysis of heart rate variability the tonic sympathetic and vagal activities and their changes are respectively assessed by the power of 0.050 - 0.015Hz(low frequency, LF) and 0.150 - 0.350Hz(respiratory linked, high frequency, HF) components of the spectrum of the beat by beat variability of RR interval. METHODS: Heart rate variability(HRV) and its circadian rhythm were evaluated in 15 patients with hypertension. By using 24-h Holter monitoring, HRV and its spectral components were measured. Finding were compared with 15 age-matched normal controls. RESULTS: The 24-hour plot of the SDs revealed that heart rate variability was significantly lower in the hypertensive patients, and the differences reached statistical significance during hours 2, 3, 9, 13, 16, 18, 19, and 23(p<0.05). Spectral analysis showed that power in the high-frequency range(0.150 to 0.350Hz) was lower among the hypertensive patients than among the normal controls during 22 of 24 hours but that the difference was statistically significant only during 2 hours(p<0.05). Power in the low frequency range(0.050 to 0.150Hz) was low at night, increased in the morning, and high during the day among controls ; this circadian rhythm was absent among hypertensive patients. CONCLUSIONS: Among hypertensive patients, HRV is decreased with a partial withdrawal of parasympathetic tone, and the circadian rhythm of sympathetic/parasympathetic tone is altered.
Circadian Rhythm ; Electrocardiography, Ambulatory ; Heart Rate ; Humans ; Hypertension

Dextromethorphan and chlorpeniramine are common ingredients of over-the-counter (OTC) cough pills. They are known to be safe when used alone, however, combination with other serotonergic drugs or use of an overdose can cause serotonergic toxicity. We report on a 43-year-old male and a 57-year-old female who ingested an overdose of antitussive drugs containing dextromethorphan and chlorpeniramine. They commonly presented with altered mentality and hyperreflexia on both upper and lower extremities. After conservative therapies, they were discharged with alert mentality. These cases are meaningful in that there are few cases of serotonin syndrome with an overdose of a combination of dextromethorphan and chlorpeniramine. Careful use with medication counseling for OTC cough pills is needed in order to prevent overdose of these ingredients.
Adult ; Antitussive Agents ; Cough ; Counseling ; Dextromethorphan ; Female ; Humans ; Lower Extremity ; Male ; Middle Aged ; Reflex, Abnormal ; Serotonin ; Serotonin Agents ; Serotonin Syndrome

PURPOSE: To evaluate the relationship between depressive symptoms and health care costs in outpatients with chronic medical illnesses in Korea, we screened for depressive symptoms in 1,118 patients with a chronic medical illness and compared the severity of somatic symptoms and health care costs. PATIENTS AND METHODS: Data were compared between outpatients with depressive symptoms and those without depressive symptoms. Depression and somatic symptoms were measured by Zung's Self-rating Depression Scale (SDS) and Patient Health Questionnaire (PHQ)-15, respectively. We also investigated additional data related to patients' health care costs (number of visited clinical departments, number of visits made per patients, and health care costs). A total of 468 patients (41.9%) met the criteria for depressive disorder. RESULTS: A high rate of severe depressive symptoms was found in elderly, female and less-educated patients. A positive association between the severity of somatic symptoms and depressive symptoms was also identified. The effects of depressive symptoms in patients with chronic illnesses on three measures of health services were assessed by controlling for the effects of demographic variables and the severity of somatic symptoms. We found that the effects of depressive symptoms on the number of visited departments and number of visits made per patients were mediated by the severity of somatic symptoms. However, for health care costs, depressive symptoms had a significant main effect. Furthermore, the effect of gender on health care costs is moderated by the degree of a patient's depressive symptoms. CONCLUSION: In summary, there is clearly a need for increased recognition and treatment of depressive symptoms in outpatients with chronic medical illnesses.
Adult ; Aged ; Aged, 80 and over ; Chronic Disease/*economics ; Depression/*complications/diagnosis/*economics ; Female ; *Health Care Costs ; Humans ; Male ; Middle Aged ; *Outpatients ; Regression Analysis ; Sex Factors

PURPOSE: The objective of the present study was to predict the gestational age at preterm birth using artificial neural networks for singleton pregnancy. METHODS: Artificial neural networks (ANNs) were used as a tool for the prediction of gestational age at birth. ANNs trained using obstetrical data of 125 cases, including 56 preterm and 69 non-preterm deliveries. Using a 36-variable obstetrical input set, gestational weeks at delivery were predicted by 89 cases of training sets, 18 cases of validating sets, and 18 cases of testing sets (total: 125 cases). After training, we validated the model by another 12 cases containing data of preterm deliveries. RESULTS: To define the accuracy of the developed model, we confirmed the correlation coefficient (R) and mean square error of the model. For validating sets, the correlation coefficient was 0.839, but R of testing sets was 0.892, and R of total 125 cases was 0.959. The neural networks were well trained, and the model predictions were relatively good. Furthermore, the model was validated with another dataset of 12 cases, and the correlation coefficient was 0.709. The error days were 11.58±13.73. CONCLUSION: In the present study, we trained the ANNs and developed the predictive model for gestational age at delivery. Although the prediction for gestational age at birth in singleton preterm birth was feasible, further studies with larger data, including detailed risk variables of preterm birth and other obstetrical outcomes, are needed.
Dataset ; Gestational Age ; Parturition ; Pregnancy ; Premature Birth