Objective To analyze the relationship among perceived stress, resilience and sleep quality in patients with coronary heart disease, and to examine the mediating effect of resilience between perceived stress and sleep quality. Methods Totally 231 patients with coronaryheartdisease were selected. The Pittsburgh Sleep Quality Index (PSQI), Perceived Stress Scale (PSS), and 10-item Connor-Davidson Resilience Scale (CD-RISC-10) were used to assess the sleep quality, perceived stress and resilience respectively. Results The average scores of PSQI were (7.2 ± 2.8) points, and 71.4％ (165/231) of the participants had sleep problems (PSQI>5). CD-RISC-10 scores were negatively correlated with PSQI scores and PSS scores (r=-0.62,-0.43, P<0.01), and PSS scores were positively correlated with PSQI scores (r=0.33, P<0.01). Furthermore, Bootstrap-generated 95％CI was (0.130-0.319, P<0.01) and did not include 0, which indicated that resilience significantly mediated the relationship between perceived stress and sleep quality. The mediating effect size of resilience was 56.1％. Conclusion It suggests that patients with coronary heart disease have poor sleep quality. Perceived stress and resilience could affect sleep quality, and resilience could mediate the relationship between perceived stress and sleep quality.

Objective:To investigate the efficacy and safety of liraglutide combined with metformin in the treatment of overweight or obese patients with type 2 diabetes, and to analyze the factors influencing the response to liraglutide.Methods:Seventy-three overweight or obese patients with well-controlled type 2 diabetes on metformin were selected and treated with liraglutide at 1.8 mg/d in addition to metformin at 1500 mg/d for 48 weeks. Relevant data were collected before and after treatment, including blood glucose, glycosylated hemoglobin (HbA1c), fasting insulin, serum lipid, body weight, waist circumference, hip circumference, body mass index (BMI), homeostatic model assessment for β-cell function (HOMA-β) and homeostatic model assessment for insulin resistance (HOMA-IR). Changes in metabolic markers, incidence of side effects, weight loss efficacy and corresponding influencing factors were evaluated.Results:After 48 weeks of treatment, fasting blood glucose, 2-hour postprandial blood glucose, HbA1c, fasting insulin, HOMA-IR, blood lipid, waist circumference, hip circumference and BMI decreased significantly compared with baseline ( P < 0.05). The most common side effects were tolerable gastrointestinal adverse events. The average weight loss after the initial 4-week treatment was 3.99 kg, accounting for 48.8% of the total weight loss, and then the change displayed a more subdued trend during the remaining treatment period. After the 48-week treatment, 73.1% and 34.6% of the patients lost more than 5% and 10% of body weight, respectively. Absolute weight loss was positively correlated with baseline weight and weight loss within the initial 4-week treatment was an independent predictor of weight loss ≥ 5% at the 48th week. Conclusions:Liraglutide combined with metformin is safe and effective in the treatment of overweight or obese patients with type 2 diabetes mellitus. Weight loss is significant during the initial 4 weeks and the early response seems to be a predictor for better long-term effect on weight loss.

Objective:The decline in nutritional status in patients with severe pneumonia may contribute to an increase in in-hospital mortality. Enteral nutrition support can improve the nutritional status of patients, and is relatively easy to manage, with low cost and fewer serious complications. On the other hand, adverse reactions such as gastric retention and gastric microbiota translocation may increase the incidence of nosocomial pneumonia and increase the uncertainty of patient prognosis. There is no predictive model for in-hospital death in severe pneumonia patients receiving enteral nutrition support. The objective of this study was to investigate the risk factors of in-hospital death in patients with severe pneumonia receiving enteral nutrition support and to establish a prognostic model for such patients.Methods:This was a single-center retrospective study. Patients with severe pneumonia who were hospitalized in Peking Union Medical College Hospital and received enteral nutrition support were included from January 1, 2015 to December 31, 2020. The primary endpoints were in-hospital mortality rate and unordered discharge rate. The independent risk factors were determined using univariate and multifactorial logistic regression analysis, the nomogram scoring model was constructed, and the decision curve analysis (DCA) was performed.Results:A total of 632 severe pneumonia patients who received enteral nutrition support were included. Patients were divided into death and survival groups according to the presence or absence of in-hospital death, and 24 parameters were found with significant differences between groups. Nine parameters were independent predictors of mortality, namely the duration of ventilator use, the presence of malignant hyperplasia diseases, the maximal levels of platelet and prothrombin during hospitalization, and the nadir levels of alanine aminotransferase, serum albumin, sodium, potassium, and blood glucose. Based on these variables, a risk prediction scoring model was established (ROC = 0.782; 95% CI: 0.744 to 0.819, concordance index: 0.772). Calibration curves, DCA, and clinical impact curve were plotted to evaluate the goodness of function, accuracy, and applicability of the predictive nomogram, using the training and test sets. Conclusion:This study summarized the clinical characteristics of patients with severe pneumonia receiving enteral nutrition support and developed a scoring model to identify risk factors and establish prognostic models.

Gut microbiota is the microbial community that resides on the surface of human intestinal mucosa. During normal pregnancy, the composition of gut microbiota may change dynamically with the progress of pregnancy. Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which can affect maternal and neonatal intestinal flora, and affect the long-term glucose metabolism of mothers and infants through exacerbating insulin resistance and promoting inflammatory response. Adjustment of dietary structure and application of probiotics may regulate intestinal microbiota and improve maternal and neonatal glucose metabolism in GDM. Here we reviewed the correlation between intestinal flora and glucose metabolism during pregnancy, and discussed the effects of diet and probiotics on gut microbiota.

The elucidation of resources pertaining to the Chimonanthus praecox varieties and the establishment of a fingerprint serve as crucial underpinnings for advancing scientific inquiry and industrial progress in relation to C. praecox. Employing the SSR molecular marker technology, an exploration of the genetic diversity of 175 C. praecox varieties (lines) in the Yanling region was conducted, and an analysis of the genetic diversity among these varieties was carried out using the UPDM clustering method in NTSYSpc 2.1 software. We analyzed the genetic structure of 175 germplasm using Structure v2.3.3 software based on a Bayesian model. General linear model (GLM) association was utilized to analyze traits and markers. The genetic diversity analysis revealed a mean number of alleles (Na) of 6.857, a mean expected heterozygosity (He) of 0.496 3, a mean observed heterozygosity (Ho) of 0.503 7, a mean genetic diversity index of Nei՚s of 0.494 9, and a mean Shannon information index of 0.995 8. These results suggest that the C. praecox population in Yanling exhibits a rich genetic diversity. Additionally, the population structure and the UPDM clustering were examined. In the GLM model, a total of fifteen marker loci exhibited significant (P < 0.05) association with eight phenotypic traits, with the explained phenotypic variation ranging from 14.90% to 36.03%. The construction of fingerprints for C. praecox varieties (lines) was accomplished by utilizing eleven primer pairs with the highest polymorphic information content, resulting in the analysis of 175 SSR markers. The present study offers a thorough examination of the genetic diversity and SSR molecular markers of C. praecox in Yanling, and establishes a fundamental germplasm repository of C. praecox, thereby furnishing theoretical underpinnings for the selection and cultivation of novel and superior C. praecox varieties, varietal identification, and resource preservation and exploitation.
Bayes Theorem ; Biomarkers ; Phenotype ; Cluster Analysis ; Genetic Variation