Establishment of insomnia model of chronic unpredictable stress in rats

Hippocampal Transcriptome Analysis in a Mouse Model of Chronic Unpredictable Stress Insomnia

Background: This study aimed to develop a model for understanding stress-induced sleep disturbances and to explore the potential interactions between sleep disturbances and mood disturbances. Methods: The chronic unpredictable mild stress (CUMS) group was established using the CUMS method, while the CUMS+Noise group was subjected to an additional 8-h exposure to noise in conjunction with the CUMS protocol. Each group was tested for anxiety and depressive-like behavior using the open-field, elevated plus maze, tail suspension, and forced swimming tests in male C57BL/6J mice. Subsequently, we assessed sleep status using sleep recordings and a standardized scoring system alongside the pentobarbital sodium-induced sleep test. Results: The mice in both model groups exhibited anxiety-like behavior. Sleep disturbances observed in the CUMS+Noise group were characterized by disruptions in sleep duration and circadian rhythm. This observation was supported by a marked reduction in multiple sleep time intervals and single sleep duration, as well as a significant increase in sleep duration at the final time interval of ZT23-24. To further investigate the potential mechanisms of interaction, we conducted an analysis of hub genes present in the hippocampal sequencing data utilizing weighted gene co-expression network analysis (WGCNA). Pearson correlation analysis revealed a significant association between the hub genes Alb, P2rx1, and Npsr1 and key phenotypic traits. However, PCR experiments indicated that only Alb showed a significant difference, which aligns with the sequencing results. Conclusions: Albumin is a crucial transporter protein for thyroid hormones and plays a vital role in their metabolism. The interaction between sleep disorders and anxiety-like behavior may be closely linked to the dysfunctional transportation of thyroid hormones by albumin.

Research progress on the effect of medicine and food homology resources for sleep improvement

Insomnia can have a negative impact on people's life or even cause mental or physical diseases. In China, the usage of medicine food homology herbal resources to treat insomnia has a long history. This review, which is based on the theory of traditional Chinese medicine (TCM), summarizes the research progress of medicine and food homology (MFH) resources in treating insomnia. Through literature search from the last 8 years, we compared the understanding of insomnia between TCM and modern pharmacology, found 21 kinds of MFH plants and 15 kinds of prescriptions containing MFH plants that have the effect of improving sleep and summarized the mechanism of their treatment of insomnia. Our study will provide theoretical support for the development and utilization of MFH plant resources with sleep-enhancing properties and provide positive insights and direction references for more effective treatment of insomnia disease.

Development and validation of a nomogram for sleep disorders among stroke patients

BACKGROUND

Precisely identifying high-risk sleep disorder patients and implementing suitable measures are important for decreasing the incidence of sleep disorders. In this study, a nomogram method was adopted to construct a tool to predict sleep disorders in stroke based on four factors: individual characteristics, treatment-related factors, psychological factors, and family-related factors.

METHODS

A total of 450 stroke patients were continuously diagnosed at the Affiliated Hospital of Nantong University, and the data on participants were randomly distributed into a training set (n = 315) and a validation set (n = 135). Within the training set, using LASSO regression and random forest methods, five optimal predictors of sleep disorders were identified. Five optimal predictors were used to develop a model. The calibration, discrimination, generalization, and clinical applicability of the model were evaluated using calibration curves, receiver operating characteristic (ROC) curves, internal validation, and decision curve analysis (DCA).

RESULTS

We found that the place of residence, average daily infusion time, the Hospital Anxiety and Depression Scale (HADS), the Type D Personality Scale-14 (DS14), and the Fatigue Severity Scale (FSS) were crucial factors associated with sleep disorders. The validation data showed an area under the curve (AUC) of 0.903 compared to 0.899 in the training set. There was an approach to the diagonal in the calibration curve of this model, and the results of DCA noted that it is clinically beneficial across a range of thresholds from 5 % to 99 %.

CONCLUSION

A model was developed to predict sleep disorders among stroke patients to help hospital staff evaluate the risk among patients and screen high-risk patients.