Background: Good sleep is essential to human health. Insufficient quality sleep may compromise the wellness of nurses and even jeopardize the safety of patients. Although the contributors of sleep quality in nurses have been previously studied, the direct and indirect effects of modifiable work-related predictors remain uncertain. Purpose: The study was designed to explore the direct and indirect effects of modifiable work-related factors on sleep quality in Chinese nurses. Methods: A multistage sampling method was employed in this cross-sectional study to recruit 923 participants. An evidence-based predicting model was postulated and then subsequently tested and optimized using path analysis. Results: The final model fit the data well, with the involved predictors accounting for 34.1% of the variance in sleep quality of the participants. Shift work, job demands, exposure to hazards in work environments, chronic fatigue, and inter-shift recovery were identified as direct predictors, while whereas job satisfaction, job control, support at work, and acute fatigue were identified as indirect predictors. Conclusions/Implications for Practice: Sleep quality in Chinese nurses is influenced directly and indirectly by various modifiable work-related factors. Interventions such as adjusting work shifts and reducing job burdens should be prioritized by administrative staff to ensure the sleep quality and clinical performance of Chinese nurses and to subsequently improve nursing care quality.

Background: Most of the previous studies on nursing practice environment and job burnout employed conventional analyses ignoring the impact of unit-level data clusters. This study addressed this gap by examining the effects of the nursing practice environments on dimensions of occupational burnout among a sample of Chinese nurses using multilevel logistic regression models and demonstrating the superiority of employing multilevel models over conventional models within this context. Methods: A proportionate stratified sampling method was applied in this cross-sectional study that invited 1,300 registered nurses (RNs) from nine clinical units of a large, academic hospital in urban China to complete the questionnaire. Nurse-reported information was obtained using the Practice Environment Scale of the Nursing Work Index (PES-NWI) and the Maslach Burnout Inventory (MBI). Findings: A total of 1,178 valid questionnaires were returned for a response rate of 90.62%. RNs generally perceived their nursing practice environment as favorable as measured by the PES-NWI. Approximately 40% of the respondents reported experiencing emotional exhaustion and depersonalization. The multivariate models indicated that nurse burnout was significantly associated with nurse participation in hospital affairs, nursing foundations for quality of care, and adequate staffing. In addition, our results illustrated the advantage of multilevel modeling over the conventional modeling for handling hierarchical data in terms of the accuracy of the estimates and the goodness-of-fit of the model. Conclusions/Application to Practice: These findings underscore the importance of measures aimed at enhancing nursing practice environments to prevent RNs from experiencing feelings of burnout and of considering multilevel analysis in future nursing research.

ObjectiveEmerging studies have demonstrated the promising clinical value of circulating tumor cells (CTCs) for diagnosis, disease assessment, treatment monitoring and prognosis in epithelial ovarian cancer. However, the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards. MethodsWe enrolled 160 patients with epithelial ovarian cancer as the experimental group, and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group. We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens (EpCAM and MUC1), and used multiple reverse transcription-polymerase chain reaction (RT-PCR) detecting three markers (EpCAM, MUC1 and WT1) for quantification. And then we used a binary logistic regression analysis and focused on EpCAM, MUC1 and WT1 to establish an optimized CTC detection model. ResultsThe sensitivity and specificity of the optimized model is 79.4% and 92.2%, respectively. The specificity of the CTC detection model is significantly higher than CA125 (92.2% vs. 82.2%, P=0.044), and the detection rate of CTCs was higher than the positive rate of CA125 (74.5% vs. 58.2%, P=0.069) in early-stage patients (stage I and II). The detection rate of CTCs was significantly higher in patients with ascitic volume ≥500 mL, suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy (P<0.05). The detection rate of CTC^EpCAM+ and CTC^MUC1+ was significantly higher in chemo-resistant patients (26.3% vs. 11.9%; 26.4% vs. 13.4%, P<0.05). The median progression-free survival time for CTC^MUC1+ patients trended to be longer than CTC^MUC1− patients, and overall survival was shorter in CTC^MUC1+ patients (P=0.043). ConclusionsOur study presents an optimized detection model for CTCs, which consists of the expression levels of three markers (EpCAM, MUC1 and WT1). In comparison with CA125, our model has high specificity and demonstrates better diagnostic values, especially for early-stage ovarian cancer. Detection of CTC^EpCAM+ and CTC^MUC1+ had predictive value for chemotherapy resistance, and the detection of CTC^MUC1+ suggested poor prognosis.

Background: Periviable infants are a highly vulnerable neonatal group, and their survival rates are considerably affected by patient-, caregiver-, and institution-level factors, exhibiting wide variability across different income countries and time periods. This study aims to systematically review the literature on the survival rates of periviable infants and compare rates among countries with varied income levels and across different time periods.Methods: Comprehensive searches were conducted across MEDLINE, Embase, CENTRAL, and Web of Science. Cohort studies reporting survival outcomes by gestational age (GA) for periviable infants born between 22 + 0 and 25 + 6 weeks of gestation were considered. Paired reviewers independently extracted data and assessed the risk of bias and quality of evidence. Data pooling was achieved using random-effects meta-analyses.Results: Sixty-nine studies from 25 countries were included, covering 56,526 live births and 59,104 neonatal intensive care unit (NICU) admissions. Survival rates for infants born between 22 and 25 weeks of GA ranged from 7% (95% CI 5–10; 22 studies, n = 5,658; low certainty) to 68% (95% CI 63–72; 35 studies, n = 21,897; low certainty) when calculated using live births as the denominator, and from 30% (95% CI 25–36; 31 studies, n = 3,991; very low certainty) to 74% (95% CI 70–77; 48 studies, n = 17,664, very low certainty) for those admitted to NICUs. The survival rates improved over the two decades studied; however, stark contrasts were evident across countries with varying income levels.Conclusion: Although the survival rates for periviable infants have improved over the past two decades, substantial disparities persist across different economic settings, highlighting global inequalities in perinatal health. Continued research and collaborative efforts are imperative to further improve the global survival and long-term outcomes of periviable infants, especially those in Low- and Middle-Income Countries.Systematic review registration: PROSPERO, CRD42022376367, available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022376367.