The effects of sexual abuse on female adolescent brain structures.

Objective: Sexual abuse (SA) is known for its effects on brain structures in adolescents. We aimed to explore if SA has any effect on limbic and prefrontal cortex (PFC) structures. We hypothesized that children with SA would have a thinner PFC with larger amygdala and hippocampus that lead to aberrations in threat detection, orientation and response circuit; that would be highly adaptive in a dangerous environment in the short term. Method: We included 57 SA and 33 healthy control (HC) female participants. In addition to psychiatric evaluation, we acquired 3 T MR images from all participants. We compared prefrontal cortical thicknesses, hippocampus and amygdala volumes between groups. Results: The age and education levels of study groups were matched, however, IQ scores and socioeconomic status (SES) scores of the SA group were lower than the controls. Total CTQ scores of the SA group were higher than the HC. Nevertheless, the mean value of sexual abuse scores was above the cut-off scores only for the SA participants. SA participants had larger right and left hippocampus and right amygdala volumes than the controls. SA group had reduced inferior frontal gyrus cortical thickness (T=3.5, p<0.01, cluster size=694 mm2, x=51 y=-30 z=6) than HC group. None of the structural findings were correlated with total or sexual abuse CTQ scores. Conclusion: Children with SA history has structural abnormalities in threat detection, orientation and response circuit. SA victims with no psychiatric diagnosis have a high probability of psychiatric problems with a possible contribution of these aberrations. SA cases that do not have a diagnosis must not be overlooked as they may have structural changes in emotion related brain regions. Careful follow-up is needed for all of all SA cases.

Effect of COVID-19 Pandemic on Sleep Quality and Mental State of Frontline Pulmonologists.

Introduction: This study aims to determine the sleep quality and its relationship with anxiety and depressive symptoms, working conditions and other factors among the frontline pulmonologists on active duty during the COVID-19 pandemic. Method: An online survey was conducted among pulmonologists in Turkey. The survey link was e-mailed to the members of the professional societies of pulmonologists. The volunteers were asked to fill in questions about their sociodemographics, medical and psychiatric history, working and housing conditions, perceived levels of support during the pandemic, as well as the sleep habits before the pandemic. Also, questions investigating the severity/level of their worries were inquired and they were asked to fill in two scale forms (the Hospital Anxiety Depression Scale and Pittsburgh Sleep Quality Index-PSQI). Results: The sample consisted of 179 pulmonologists who were divided into two groups according to PSQI as good sleepers (PSQI ≤5) and poor sleepers (PSQI >5). It was observed that 59.2% of the participants had poor sleep quality during the pandemic. Being anxious (p<0.0001, Odds ratio [OR]=0.139, 95% Confidence Interval [CI] [0.052-0.372]), working in intensive care unit (p=0.046, OR=2.363, 95% CI [1.015-5.497]), worry level about excessive increase of the number of patients above the capacity of the institution they worked in (p=0.018, OR=1.755, 95% CI [1.102-2.794]) and being dissatisfied with ones' sleep before the pandemic (p<0.016, OR=0.272, 95% CI [0.094-0.786]) were found to be the main factors that negatively affected the quality of sleep of pulmonologists during the pandemic. Conclusion: More than half of the pulmonologists in our sample group had low sleep quality during the pandemic. For establishing a good sleep regime for clinicians, its crucial to consider certain interventions on the affecting factors.

Computer based classification of MR scans in first time applicant Alzheimer patients.

In this study, we aimed to classify MR images for recognizing Alzheimer Disease (AD) in a group of patients who were recently diagnosed by clinical history and neuropsychiatric exams by using non-biased machine-learning techniques. T1 weighted MRI scans of 31 patients with probable AD and 31 age- and gender-matched cognitively normal elderly were analyzed with voxel-based morphometry and classified by support vector machine (SVM), a machine learning technique. SVM could differentiate patients from controls with accuracy of 74% (sensitivity: 70% and specificity: 77%) when the whole brain was included the analyses. The classification accuracy was increased to 79% (sensitivity: 65 % and specificity: 93%) when the analyses restricted to hippocampus. Our results showed that SVM is a promising tool for diagnosis of AD, but needed to be improved.