Effect of fear of hypoglycaemia on sleep quality of patients with type 2 mellitus diabetes: The mediating role of alexithymia.

Background: Patients with type 2 diabetes mellitus (T2DM) commonly experience poor sleep quality. This study aimed to investigate whether alexithymia mediates the association between fear of hypoglycaemia (FoH) and sleep quality in patients with T2DM. Methods: From September 2021 to November 2021, a cross-sectional survey was conducted on 407 patients with T2DM in China. Data collection was made possible through the administration of the Chinese Version of the Worry Scale, Toronto Alexithymia Scale and Chinese version of the Pittsburgh Sleep Quality Index (CPSQI). Multiple linear regression analyses were also performed. Results: A total of 65.6% of the participants were male, and 75.7% were aged 18-40 years. FoH showed a moderate and positive correlation with CPSQI scores (r = 0.308, p < 0.001). Alexithymia was weakly and positively correlated with CPSQI scores (r = 0.185, p < 0.001). Meanwhile, FoH exhibited a moderate and positive correlation with alexithymia (r = 0.422, p < 0.001), and difficulty in identifying (r = 0.414, p < 0.001) and describing feelings (r = 0.416, p < 0.001) and a weak and positive correlation with externally oriented thinking (r = 0.221, p < 0.001). The total effect (ß = 0.408, p < 0.001) of FoH on CPSQI comprised not only the direct (ß = 0.293, 95% confidence interval: 0.174-0.411, p < 0.001) but also the indirect effect (ß = 0.115, p < 0.001) of alexithymia. Conclusions: Alexithymia can mediate the association between FoH and sleep quality. Clinicians should recognize the potential effect of alexithymia and incorporate it in intervention planning and care. Addressing the affective disturbances arising from FoH can enhance emotional expression and sleep quality among T2DM patients.

Patterns of sleep quality and its influence factors: A latent class model among students of medical university in Hubei Province, China.

BACKGROUND: Sleep problem among undergraduate students has become one of the most pressing public health problems. This study aimed to explore the latent class of sleep patterns and the factors affecting sleep in Chinese students of medical university. METHODS: 3423 students participated in the cross-sectional study. The survey consisted of the reduced Morningness-Evening Questionnaire, the Pittsburgh Sleep Quality Index, and Health-Promoting Lifestyle Profile-II. Latent profile analysis and multinominal logistic regression analysis were performed. RESULTS: Three potential sleep categories were identified: "sleep disorder group" (1.87 %), "daytime dysfunction group" (24.42 %), and "good sleep group" (73.71 %). Compared with the "good sleep group," the "sleep disorder group" showed monthly living expenses (RMB) ≥ 3000 yuan (OR) = 13.04), interpersonal relationships as poor (OR = 3.71), health status as poor (OR = 45.09), circadian rhythm as eveningness (OR = 6.17), and poor health-promoting lifestyles (OR = 2.090) as its risk factors (all p < 0.05). Meanwhile, sophomore (OR = 1.75), junior (OR = 1.52), interpersonal relationships as poor (OR = 1.88), health status as poor (OR = 4.62), intermediate-chronotype (OR = 2.19), eveningness chronotype (OR = 5.66), and health-promoting lifestyles as poor (OR = 1.55) were identified as risk factors for the "daytime dysfunction group" (all p < 0.05). LIMITATIONS: Causal conclusions can not be drawn and recall bias in data collection. CONCLUSIONS: Significant population heterogeneity was found in the sleep quality. Implementing targeted interventions focusing on circadian rhythm and lifestyle is crucial to improve the sleep quality of students with different conditions.

Circadian rhythms and sleep quality among undergraduate students in China: The mediating role of health-promoting lifestyle behaviours.

BACKGROUND: Poor sleep quality have become one of the most pressing public health problems for undergraduate students. The aim of this cross-sectional study was to investigate the relationship between circadian rhythms and sleep quality and the meditating role of health-promoting lifestyles in the relationship of Chinese undergraduate students. METHODS: A total of 3423 students participated. The online survey consisted of the reduced Morningness-Evening Questionnaire (rMEQ), the Pittsburgh Sleep Quality Index (PSQI) and Health-Promoting Lifestyle Profile-II (HPLP-II). Logistic regression models were employed. RESULTS: The prevalence of poor sleep quality is 43.03 %. The total mean scores of HPLP - II, PSQI, and rMEQ are 96.94 ± 17.26, 5.20 ± 2.70 and 14.83 ± 2.10, respectively. A significant negative correlation exists between the rMEQ and PSQI scores (r = -0.262, p < 0.001), but a positive correlation exists between the rMEQ and HPLP scores (r = 0.232, p < 0.001). The total and sub-domain scores of HPLP are also negatively correlated with the PSQI scores (r = -[0.166, 0.291], p < 0.001). Mediation analysis demonstrates the mediation of HPLP (indirect effect = -0.036, p < 0.001) on the effect of the rMEQ on PSQI scores that accounts for 13.30 % of the total effect. LIMITATIONS: Cross-sectional design and recall bias in data collection. CONCLUSIONS: The effect of circadian rhythm on sleep quality is partially mediated by the health-promoting lifestyle. In addition to maintaining a normal circadian rhythm, helping undergraduate students develop a healthy lifestyle is also an effective measure to improve sleep quality.

Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis.

Mastering the molecular mechanism of breast cancer (BC) can provide an in-depth understanding of BC pathology. This study explored existing technologies for diagnosing BC, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) and summarized the disadvantages of the existing cancer diagnosis. The purpose of this article is to use gene expression profiles of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to classify BC samples and normal samples. The method proposed in this article triumphs over some of the shortcomings of traditional diagnostic methods and can conduct BC diagnosis more rapidly with high sensitivity and have no radiation. This study first selected the genes most relevant to cancer through weighted gene co-expression network analysis (WGCNA) and differential expression analysis (DEA). Then it used the protein-protein interaction (PPI) network to screen 23 hub genes. Finally, it used the support vector machine (SVM), decision tree (DT), Bayesian network (BN), artificial neural network (ANN), convolutional neural network CNN-LeNet and CNN-AlexNet to process the expression levels of 23 hub genes. For gene expression profiles, the ANN model has the best performance in the classification of cancer samples. The ten-time average accuracy is 97.36% (±0.34%), the F1 value is 0.8535 (±0.0260), the sensitivity is 98.32% (±0.32%), the specificity is 89.59% (±3.53%) and the AUC is 0.99. In summary, this method effectively classifies cancer samples and normal samples and provides reasonable new ideas for the early diagnosis of cancer in the future.

PrGeFNE: Predicting disease-related genes by fast network embedding.

Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.

Knowledge-driven drug repurposing using a comprehensive drug knowledge graph.

Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.

Electronic structure-dependent formaldehyde gas sensing performance of the In2O3/Co3O4 core/shell hierarchical heterostructure sensors.

Constructing p-n heterojunction is considered as an effective approach to improve gas-sensing performance of nanomaterials, and the general focus is that the formation of a p-n junction can effectively broaden the electron-depletion layer, enhancing the amount of the adsorption oxygen, and being beneficial to the improvement of the gas-sensing performance. However the widening of the depletion layer can only contribute to the improvement of the sensitivity, the effect of p-n junction on other sensing parameters is still not well understood. Herein, the In2O3/Co3O4 core/shell hierarchical heterostructures (In2O3/Co3O4 HHS) are investigated to discern how p-n junction alters the sensing process. The construction of p-n junction can effectively adjust Fermi level, influence the oxidation ability of the adsorbed oxygen and significantly heighten the selectivity of sensing materials, resulting in superior sensing activity. Especially, In2O3/Co3O4 HHS exhibits obviously enhanced gas sensing performance toward formaldehyde at 180 °C with high response and good selectivity. Our findings promote the recognition of the important role of electronic structure on gas sensing performance and provide a new strategy to design sensing materials for gas detection.