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OBJECTIVE: Bipolar disorder (BD) is marked by significant change in mood and energy levels with sleep disturbance a common feature, resulting in diminished quality of life and impaired daily functioning. This study assessed the association between BD-polygenic risk scores (PRS) and hypnotics in bipolar I disorder (BD-I) patients. METHODS: Large-sample data were collected from the genome-wide association study of a multicenter Bipolar Genomic Study, and 1,394 BD-I patients with available medication information were divided into two groups depending on whether they used hypnotics or not. The Diagnostic Interview for Genetic Studies (DIGS) score was used to assess the clinical manifestations and function of the participants and the association between the use of hypnotics and genetic risk was analyzed. RESULTS: Of the 1,394 total participants, 556 (40%) patients received hypnotics, mostly benzodiazepines, administered singly or in combination with other sleeping agents such as, Z-drugs, melatonin-related drugs, and trazodone. The DIGS score was significantly higher for negative categories in the group prescribed hypnotics as was the BD-PRS score, according to the four p value thresholds (p = 0.3, 0.2, 0.1, and 0.05). Logistic regression analysis confirmed a statistically significant association between the BD-PRS and hypnotic use. CONCLUSION: Our results suggest an association between hypnotic use and genetic susceptibility to BD. Sleep disturbances in participants were based on the prescription status of hypnotics supporting the hypothesis that sleep disturbances may be associated with genetic aspects of BD-I. Further genetic studies on genetic overlaps between BD and specific phenotypes or medication responses are required.

The interaction between genes and environment often occurs when they depend on one another. We hypothesized that adverse childhood experiences (ACEs) would interact with genetic predispositions to bipolar disorder (BD), demonstrating earlier age at onset (AAO) and worse clinical outcomes. We aimed to clarify the effects of the interaction between ACEs and genetic susceptibility using polygenic risk score (PRS) on AAO and clinical outcomes. Single nucleotide polymorphisms and clinical data, including ACEs, were obtained from the Bipolar Genomic Study, which contains a large sample of BD participants. A total of 1615 subjects with BD I were obtained and divided into two groups according to the presence or absence of ACEs and an additional four groups based on the number of ACEs (none versus one versus two versus ≥ three types). ACEs was evaluated using the childhood life events scale (CLES). BD-PRS was obtained from the Psychiatric Genomics Consortium, which compared BD patients and healthy controls. The BD-PRS was higher in the group with ACEs than without ACEs at most p-value thresholds. In multivariate linear regression analyses, both groups with more ACEs and higher BD-PRS were independently and interactively associated with an earlier AAO of BD; however, only greater ACEs were associated with worsened clinical outcome. These findings highlight the clinical importance of evaluating ACEs and polygenic risk in research of the etiology of BD.

: Studies have reported an association between adverse childhood experiences (ACEs) and the clinical outcomes of bipolar disorder (BD). However, these studies have several limitations; therefore, we aimed to clarify the effect of the type and number of ACEs and the timing of adverse experiences on clinical outcomes in patients with BD. We analyzed the data of patients with BD (N = 2675) obtained from the National Institute of Mental Health: Bipolar Disorder Genetic Association Information Network, Translational Genomic Institute-I, and Translational Genomic Institute-II. All patients had been diagnosed using the Diagnostic Interview for Genetic Studies. ACEs were evaluated using the Childhood Life Events Scale (CLES). We analyzed the relationship between childhood trauma and clinical outcome in patients with and without exposure to ACEs. We found that ACEs had a robust negative effect on clinical outcomes, including earlier age at onset, presence of psychotic episodes, suicide attempts, mixed symptoms or episodes, substance misuse comorbidity, and worse life functioning. Specifically, the number of ACEs had the most significant effect on clinical outcomes; however, specific ACEs, such as physical abuse, had a considerable influence. Moreover, post-childhood adverse experiences had a weaker effect on clinical outcomes than ACEs did. There was an association of ACEs with negative clinical outcomes in patients with BD. This indicates the importance of basic and clinical research on ACEs in patients with BD.

Heat generated by electronic devices can lead to thermal deformation, damage, and fatigue failure, underscoring the importance of monitoring heat distribution. This study introduces an artificial neural network using two thermocouples for cost-effective temperature distribution prediction. Experimental data from heated systems on chip with attached heat sinks were used for training and validation, integrating thermocouple measurements and infrared camera data. The method's applicability was verified across four different heat sinks. Additionally, finite element analysis compared stress and strain based on predicted and actual temperature distributions, addressing conventional limitations that focus solely on temperature validation. Furthermore, the temperature of any coordinate could be output by including the coordinate as an input of neural networks, eliminating the hassle of re-constructing or learning the DNN to obtain the temperature of the desired coordinate. Results showed that the temperature distribution could be predicted with high accuracy (over 0.95), and the maximum error rate for stress and strain predictions was 7% in the worst case. This confirmed the feasibility of artificial neural networks to predict the temperature distribution using a minimal number of sensors and ensure robust performance even if the heat sink changes. [ABSTRACT FROM AUTHOR]