Turner syndrome and associated problems in turkish children: A multicenter study

Objective: Turner syndrome (TS) is a chromosomal disorder caused by complete or partial X chromosome monosomy that manifests various clinical features depending on the karyotype and on the genetic background of affected girls. This study aimed to systematically investigate the key clinical features of TS in relationship to karyotype in a large pediatric Turkish patient population. Methods: Our retrospective study included 842 karyotype-proven TS patients aged 0-18 years who were evaluated in 35 different centers in Turkey in the years 2013-2014. Results: The most common karyotype was 45,X (50.7%), followed by 45,X/46,XX (10.8%), 46,X,i(Xq) (10.1%) and 45,X/46,X,i(Xq) (9.5%). Mean age at diagnosis was 10.2±4.4 years. The most common presenting complaints were short stature and delayed puberty. Among patients diagnosed before age one year, the ratio of karyotype 45,X was significantly higher than that of other karyotype groups. Cardiac defects (bicuspid aortic valve, coarctation of the aorta and aortic stenosi) were the most common congenital anomalies, occurring in 25% of the TS cases. This was followed by urinary system anomalies (horseshoe kidney, double collector duct system and renal rotation) detected in 16.3%. Hashimoto’s thyroiditis was found in 11.1% of patients, gastrointestinal abnormalities in 8.9%, ear nose and throat problems in 22.6%, dermatologic problems in 21.8% and osteoporosis in 15.3%. Learning difficulties and/or psychosocial problems were encountered in 39.1%. Insulin resistance and impaired fasting glucose were detected in 3.4% and 2.2%, respectively. Dyslipidemia prevalence was 11.4%. Conclusion: This comprehensive study systematically evaluated the largest group of karyotype-proven TS girls to date. The karyotype distribution, congenital anomaly and comorbidity profile closely parallel that from other countries and support the need for close medical surveillance of these complex patients throughout their lifespan. © Journal of Clinical Research in Pediatric Endocrinology

Cardiovascular parameters for mental workload detection of air traffic controllers

In our study, we focused on air traffic controller’s working position for arrival management. Our aim was to evaluate cardiovascular parameters regarding their ability to distinguish between conditions with different traffic volumes and between conditions with and without the occurrence of an extraordinary event. Our sample consisted of 21 subjects. During an interactive simulation, we varied the load situations with two independent variables: the traffic volume and the occurrence of a priority-flight request. Dependent variables for registering mental workload were cardiovascular parameters, i.e., the heart rate, relative low-frequency and high-frequency band powers, and bandpower ratio of the low- and high-frequency bands. Heart rate was the only parameter able to differentiate significantly between simulations with minimal and high air-traffic volume, while the effect of the priority-flight request remained doubtful. No significant interaction between traffic volume and priority request could be identified for any of the cardiovascular parameter

Anxiety and depression states of adolescents with polycystic ovary syndrome

Background/aim: Various studies have shown that adult patients with polycystic ovary syndrome (PCOS) have higher levels of anxiety and depression compared to their normal counterparts. However, it is still unclear whether these mood disorders already exist in adolescents affected by PCOS. The aim of the present study is to assess differences in anxiety and depression levels between adolescents with PCOS and age-and body mass index (BMI)-matched controls and to determine the possible factor(s) impacting these psychological parameters in adolescents with PCOS. Materials and methods: The study included 80 adolescents with PCOS and 50 age-and BMI-matched controls. All participants completed standardized questionnaires assessing anxiety and depression. A multiple linear regression model was used to analyze the impact of potential variables on anxiety and depression scores of the adolescents with PCOS. Results: Significantly higher levels of anxiety, specifically generalized and social anxieties, as well as depression were found in adolescents with PCOS compared to controls. Higher BMI was found to be associated with higher levels of depression and generalized anxiety, and higher modified Ferriman-Gallwey score with higher level of panic disorder in adolescents affected by PCOS. Conclusion: Adolescents with PCOS experience significantly more emotional distress compared to adolescents without PCOS. This emotional distress may be related, at least in part, to certain clinical features of PCOS including obesity and hirsutism. PCOS in adolescents should be assessed not only for the gynecological and metabolic aspects but also for the emotional aspects of the disease

Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep

Work should be a source of health, pride, and happiness, in the sense of enhancing motivation and strengthening personal development. Healthy and motivated employees perform better and remain loyal to the company for a longer time. But, when the person constantly experiences high workload over a longer period of time and is not able to recover, then work may lead to prolonged negative effects and might cause serious illnesses like chronic stress disease. In this work, we present a solution for assessing the stress experience of people, using features derived from smartphones and wearable chest belts. In particular, we use information from audio, physical activity, and communication data collected during workday and heart rate variability data collected at night during sleep to build multinomial logistic regression models. We evaluate our system in a real work environment and in daily-routine scenarios of 35 employees over a period of 4 months and apply the leave-one-day-out cross-validation method for each user individually to estimate the prediction accuracy. Using only smartphone features, we get an accuracy of 55 %, and using only heart rate variability features, we get an accuracy of 59 %. The combination of all features leads to a rate of 61 % for a three-stress level (low, moderate, and high perceived stress) classification problem