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Key Clinical Message: Identifying any potential comorbidity such as bulimia nervosa (BN) and ADHD in trichotillomania patients is essential for a thorough treatment plan. Combining a multidisciplinary approach was found to be feasible and effective in the treatment. Abstract: Trichotillomania is frequently considered an isolated disorder; nevertheless, emerging evidence suggests that other psychiatric conditions, including obsessive-compulsive disorder (OCD), eating disorders, and attention-deficit/hyperactivity disorder (ADHD), are often found to coexist. Several studies showed that eating disorders, such as bulimia nervosa, were found in chronic hair-pullers, while OCD was considered a factor in predicting the prevalence of eating disorders, as well as the severity of trichotillomania in the populations. While the relationship between trichotillomania and OCD has been quite well-documented, the evidence of its association with bulimia nervosa and ADHD remains limited. Here, we report a case of trichotillomania with comorbid bulimia nervosa, major depressive disorder, and ADHD.
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Background There are multiple studies that indicate that the psychological well-being of a couple and their life satisfaction depend on the family and society. Various factors such as family, family values, marriage style, married life, and education have a great impact on people's lives both directly and indirectly. It is important to understand the effects of these factors on married individuals' lives that lead to depression so that appropriate measures can be taken for its prevention. Objectives This research aims to find the relationship of depressive symptoms among married individuals with various factors such as their marriage style, education, and having children. Materials and methods The study included 433 married individuals from Istanbul who met the criteria for depression. The early identification and prediction of depression in married individuals have been demonstrated to benefit significantly from machine learning techniques. In this study, we used decision tree (DT) and random forest (RF) predictive modeling techniques to create a model to predict the occurrence of depression among married individuals. Results The accuracy of the DT approach was found to be 80%, and the RF approach was 60%. Our results showed that as compared to conventional statistical methods, machine learning models performed better for classifying couples. Conclusion Future support systems that employ a range of data sources to identify individuals who are extremely susceptible to developing depression among married people may be developed using these effective models.
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A 10-year-old boy, presented with a one-year history of persistent cough, insidious in onset, which often exacerbated to vomiting, perspiration, and breathlessness. Symptoms were exacerbated in school when a teacher was present in the classroom and relieved when he was occupied with hobbies such as watching television or just relaxing. He was prescribed multiple medications for over a year, but his symptoms persisted and did not show any improvement even after the use of appropriate medicines as per the doctor's advice. Despite detailed medical evaluation, no organic cause was found. So considering a non-organic cause, a psychological assessment was done and he was found to have an anxiety disorder (Child Anxiety Related Disorders questionnaire score of 26). He was initiated on cognitive behavioral therapy, mindfulness, and distress tolerance skill lessons. Subsequently, he showed significant improvement in his symptoms. This case emphasizes the necessity to recognize underlying anxiety disorder and psychosocial problems such as dysfunctional parenting in the management of persistent chronic cough with no identifiable organic cause.
