Purpose: The aim of this study was to investigate the relationship between stressful life events and sleep quality and to probe the role of rumination and resilience in the relationship. Method: The Adolescent Self-Rating Life Events Checklist, Ruminative Responses Scale, Connor–Davidson Resilience Scale, and Pittsburgh Sleep Quality Index were used among 1,065 college students. Statistical Product and Service Solutions (SPSS) 20.0 and the SPSS macro Process, which were specifically developed for assessing complex models including both mediators and moderators, were used to analyze the data. Results: High scores of stressful life events predicted worse sleep quality. Rumination partially mediated the relations between stressful life events and sleep quality. Resilience moderated the direct and indirect paths leading from stressful life events to sleep quality. Conclusions: The results demonstrate that stressful life events can directly affect the sleep quality of college students and indirectly through rumination. Additionally, increasing psychological resilience could decrease both the direct effect and the indirect effect of stressful life events affecting sleep quality. The results of this study may contribute to a better understanding of the effects, as well as the paths and conditions, of stressful life events on sleep quality in college students. Moreover, these findings can provide constructive suggestions for improving college students’ sleep quality.

Previously, we have shown that neuromodulators are important factors in stress-induced emotional disorders, such as depression, for example, serotonin is the major substance for depression. Many psychological studies have proved that depression is due to insecure attachment. In addition, sleep is a major symptom of depression. Furthermore, serotonin is the substrate for both sleep and depression. To explore the role of sleep in the relationships between insecure attachment and depression, we investigated 755 college students with Close Relationship Inventory, Emotion Regulation Questionnaire, Self-rated Depression Scale, and Pittsburgh Sleep Quality Index. The results showed that (1) insecure attachment positively predicted poor sleep quality; (2) sleep quality partially affected depression, possibly due the same stress neuromodulators such as norepinephrine and cortisol; and (3) cognitive reappraisal moderated the mediating path leading from attachment anxiety to poor sleep quality. These findings highlight the moderating role of cognitive reappraisal in the effects of attachment anxiety on sleep quality and finally on depression. In conclusion, sleep quality links attachment anxiety and emotional disorders.

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Background Allergen-specific immunotherapy (AIT) is a causative treatment in allergic rhinitis (AR), comprising long-term allergen administration and over three years of treatment. This study is carried out for revealing the mechanisms and key genes of AIT in AR. Methods The present study utilized online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE37157 and GSE29521 to analyze the hub genes changes related to AIT in AR. Based on limma package, differential expression analysis for the two groups (samples of allergic patients prior to AIT and samples of allergic patients undergoing AIT) was performed to obtain differentially expressed genes (DEGs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were conducted using DAVID database. A Protein-Protein Interaction network (PPI) was built and a significant network module was acquired by using Cytoscape software (Cytoscape, 3.7.2). Utilizing the miRWalk database, we identified potential gene biomarkers, constructed interaction networks of target genes and microRNAs (miRNAs) using Cytoscape software, and explore the cell type-specific expression patterns of these genes in peripheral blood using publicly available single-cell RNA sequencing data (GSE200107). Finally, we are using PCR to detect changes in the hub genes that are screened using the above method in peripheral blood before and after AIT treatment. Results GSE37157 and GSE29521 included 28 and 13 samples, respectively. A total of 119 significantly co-upregulated DEGs and 33 co-downregulated DEGs were obtained from two datasets. The GO and KEGG analyses demonstrated that protein transport, positive regulation of apoptotic process, Natural killer cell mediated cytotoxicity, T cell receptor signaling pathway, TNF signaling pathway, B cell receptor signaling pathway and Apoptosis may be potential candidate therapeutic targets for AIT of AR. From the PPI network, 20 hub genes were obtained. Among them, the PPI sub-networks of CASP3, FOXO3, PIK3R1, PIK3R3, ATF4, and POLD3 screened out from our study have been identified as reliable predictors of AIT in AR, especially the PIK3R1. Conclusion Our analysis has identified novel gene signatures, thereby contributing to a more comprehensive understanding of the molecular mechanisms underlying AIT in the treatment of AR.