The Association Between Sleep Disturbance and Proinflammatory Markers in Patients With Cancer: A Meta-analysis.

BACKGROUND: Sleep disturbance is one of the symptoms with high incidence and negative influence in patients with cancer. A better understanding of the biological factors associated with sleep disturbance is critical to predict, treat, and manage this condition. OBJECTIVE: The aim of this study was to determine the correlation between sleep disturbance and proinflammatory markers in adult patients with cancer. METHODS: A systematic search was conducted in 7 databases from inception to March 1, 2020, for this meta-analysis. Two reviewers independently screened the studies, extracted data, and appraised the quality of the studies. Meta-analyses were conducted using Stata 12.0 software. RESULTS: Sixteen studies were included. Results indicated that sleep disturbance was associated with higher levels of the overall proinflammatory markers and that the effect size was small yet significant. Further subgroup analyses suggested that sleep disturbance was significantly associated with interleukin-6 and C-reactive protein, but not with interleukin-1ß or tumor necrosis factor-α. Meta-regression results indicated that only the sample source affected the association between sleep disturbance and proinflammatory markers. CONCLUSION: There was a positive relationship between sleep disturbance and selected proinflammatory markers in adult patients with cancer. IMPLICATION FOR PRACTICE: This review provides empirical support for the association between sleep disturbance and certain proinflammatory markers. Healthcare providers can further explore specific biomarkers to precisely identify the individuals at risk of sleep disturbance and develop targeted strategies for therapeutic and clinical interventions.

Predictive Model of Psychological Distress in Patients With Lung Cancer: A Cross-sectional Study.

BACKGROUND: Patients with lung cancer suffer from significant psychological distress. The underlying theoretical model that may explain what predicts or mediates the degree of psychological distress has not been elucidated. OBJECTIVES: To describe the incidence of psychological distress in patients with lung cancer and to test a predictive theoretical model of psychological distress based on symptom burden, type D personality, social support, and intrusive thoughts. METHODS: Three hundred eighty-nine patients with stages I to IV lung cancer were recruited. Participants completed a battery of scales, including measures of psychological distress, symptom burden, type D personality, perceived social support, intrusive thoughts, and demographic and clinical characteristics. The predictive theoretical model was tested using structural equation modeling. RESULTS: Experiencing clinically significant psychological distress was reported by 63.75% of participants. Consistent with the social cognitive processing model, symptom burden, type D personality, social support, and intrusive thoughts all significantly and directly predicted the level of psychological distress in patients with lung cancer. Moreover, intrusive thoughts mediated the effects of type D personality and symptom burden on psychological distress; social support and symptom burden mediated the effects of type D personality on psychological distress. CONCLUSIONS: The majority of the participants experienced psychological distress at a clinically significant level. Intrusive thoughts and social support mediated the effects of type D personality and symptom burden on psychological distress. IMPLICATIONS FOR PRACTICE: Patients with type D personality and symptom burden should be identified. Interventions for targeting social support and intrusive thoughts might ultimately reduce their psychological distress.

Mapping refuse profile in Singapore old dumping ground through electrical resistivity, S-wave velocity and geotechnical monitoring.

The purpose of this study was to track the refuse profile in Lorong Halus Dumping Ground, the largest landfill in Singapore, by electrical resistivity and surface wave velocity after 25 years of closure. Data were analyzed using an orthogonal set of plots by spreading 24 lines in two perpendicular geophone-orientation directions. Both geophysical techniques determined that refuse boundary depth was 13 ± 2 m. The refuse boundary revealed a certain degree of variance, mainly ascribed to the different principle of measurements, as well as the high heterogeneity of the subsurface. Discrepancy was higher in spots with greater heterogeneity. 3D analysis was further conducted detecting refuse pockets, leachate mounding and gas channels. Geotechnical monitoring (borehole) confirmed geophysical outcomes tracing different layers such as soil capping, decomposed refuse materials and inorganic wastes. Combining the geophysical methods with borehole monitoring, a comprehensive layout of the dumping site was presented showing the hot spots of interests.