Search

Objective The aim of this study was to explore the effect of physical activity and metabolic parameters on irisin levels in patients with schizophrenia and healthy controls. Methods Ninety-six patients with schizophrenia and 63 healthy controls comprised the study population. The participants were separated into three groups: inactive, low activity, and sufficiently active according to International Physical Activity Questionnaire short form (IPAQ-SF). We measured irisin levels using Enzyme linked immunosorbent assay. We also calculated exercise levels by using the IPAQ-SF for each individual. The independent samples ttest was used in the data analysis to compare irisin levels according to the activity levels of the patients with schizophrenia and controls. Results The levels of irisin were higher in the healthy controls (p < 0.001) compared to schizophrenia groups. When the activity levels of the schizophrenia and healthy control groups were compared, the irisin levels of the low activity and sufficiently active groups with schizophrenia were found to be lower than those of the low activity and sufficiently active groups in the healthy controls (respectively p = 0.014; p < 0.001). Conclusion Irisin levels could be affected by physical activity and these results must be supported with new studies.

Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.

Objective: Bipolar disorder and unipolar depressive disorder are complex phenotypes. There appear to be phenotypical, mechanistic, and therapeutic differences between bipolar depression (BD) and unipolar depression (UD). There is a need for understanding the underlying biological variation between these clinical entities. The role of oxidative processes underlying bipolar disorder and depression has been demonstrated. Thiol-disulfide homeostasis (TDH) is a recent oxidative stress marker. In this study, we aimed to inspect patients with bipolar depression and unipolar depression in terms of thiol-disulfide balance and to compare them with healthy controls.Methods: Patients admitted to the outpatient clinic of Ankara Numune Training and Research Hospital and diagnosed either as a depressive episode with bipolar disorder (n = 37) or unipolar depression (n = 24) according to DSM-5 criteria, along with healthy controls (HC) (n = 50), were included in the study. Native thiol, total thiol, and disulfide levels were compared across the groups.Results: In comparison to HC, both BD and UD groups had higher disulfide levels, disulfide/native thiol ratio, and disulfide/total thiol ratio. No significant differences between BD and UD were detected in terms of disulfide level, disulfide/native thiol ratio, and disulfide/total thiol ratio.Conclusion: Increased levels of disulfide, native thiol, and disulfide/total thiol ratios compared to healthy controls in both UD and BD groups may be indicative of the presence of oxidative damage in these two clinical conditions. To clarify the role of oxidative stress in the pathophysiology of depressive disorders and investigate TDH, longitudinal studies in patients with medication-free UD and BD are required.