Search

It has been estimated that approximately 20% of employed adults worldwide are required to undertake shift work, which has led to severe public health decline and depression. Sini powder (SNP) has been shown to be a promising medicine. However, the effectiveness and mechanism of reducing the risk of depression for shift workers are still unclear. Thus, network pharmacological analysis and gene expression profiling were introduced to reveal the mechanism of SNP in the prevention of depression. First, five databases were used to collect the chemical compounds of SNP, and absorption, distribution, metabolism, and excretion (ADME) properties were selected to identified high-quality compounds. Second, the targets of compounds were obtained by BATMAN-TCM. And the differentially expressed genes associated with depression among shift workers were obtained using gene expression profiling (GSE98793, GSE122541). Subsequently, key targets were screened out with a protein–protein interaction (PPI) network by String. Furthermore, functional enrichment was carried out by Metascape. Molecular docking was introduced to verify the interaction between core targets and compounds. SNP reduced the risk of depression by regulating ESR2, GPER1, TNF and APOE through the estrogen pathway and NOD-like receptor signaling pathway. This study provides a scientific basis for further investigating the mechanism by which SNP prevents depression and reduces the risk of depression for shift workers.

Objective: The incidence of post-traumatic stress disorder (PTSD) increased among healthcare workers (HCWs) during the outbreak of COVID-19. The purpose of this study was to examine the relationship between eating behavior and PTSD, considering the mediation effect of anxiety, depression and sleep. Methods: A total of 101 HCWs completed a survey. The Food-Frequency Questionnaires (FFQ) were used to evaluate the diet. A special survey was conducted on the eating time of each shift mode. The PTSD Checklist-Civilian Version (PCL-C), Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Pittsburgh Sleep Quality Index (PSQI), and Morning-Evening Questionnaire (MEQ) were utilized to assess clinical symptoms. Results: There was a statistically significant correlation between the night shift eat midpoint (NEMP) and PTSD symptoms, anxiety and depression as significant mediators. The last meal jet lag between night shift and day shift (NDLM) was related to PTSD symptoms significantly, and sleep and anxiety were significant mediators. The relationship between animal-based protein pattern and PTSD symptoms was statistically significant, and anxiety was the significant mediator. Conclusions: The earlier the HCWs eat in the night shift, the lighter the symptoms of PTSD. This is mediated by improving anxiety, depression and sleep disorder. Furthermore, the consumption of animal protein could reduce symptoms of PTSD by improving anxiety.

Vital signs have been widely used to assess the disease severity of patients, but there is still a lack of research on their circadian rhythms. The objective is to explore the circadian rhythms of vital signs in critically ill patients and establish an in-hospital mortality prediction model. Study patients from the recorded eICU Collaborative Research Database were included in the present analyses. The circadian rhythms of vital signs are analyzed in critically ill patients using the cosinor method. Logistic regression was used to screen independent predictors and establish a prediction model for in-hospital mortality by multivariate logistic regression analysis and to show in the nomogram. Internal validation is used to evaluate the prediction model by bootstrapping with 1000 resamples. A total of 29,448 patients were included in the current analyses. The Mesor, Amplitude, and Peak time of vital signs, such as heart rate (HR), temperature, respiration rate (RR), pulse oximetry-derived oxygen saturation (SpO2), and blood pressure (BP), were significant differences between survivors and non-survivors . Logistic regression analysis showed that Mesor, Amplitude, and Peak time of HR, RR, and SpO2 were independent predictors for in-hospital mortality in critically ill patients. The area under the curve (AUC) and c-index of the prediction model for the Medical intensive care unit (MICU) and Surgical intensive care unit (SICU) were 0.807 and 0.801, respectively. The Hosmer- Lemeshow test P-values were 0.076 and 0.085, respectively, demonstrating a good fit for the prediction model. The calibration curve and decision curve analysis (DCA) also demonstrated its accuracy and applicability. Internal validation assesses the consistency of the results. There were significant differences in the circadian rhythms of vital signs between survivors and non-survivors in critically ill patients. The prediction model established by the Mesor, Amplitude, and Peak time of HR, RR, and SpO2 combined with the Acute Physiology and Chronic Health Evaluation (APACHE) IV score has good predictive performance for in-hospital mortality and may eventually support clinical decision-making. [ABSTRACT FROM AUTHOR]