ABSTRACT
The gold-standard for bone substitution of large bone defects continues to be autogenous bone graft. Artificial bone substitutes are difficult to replace the autogenous bone grafting due to excessive immune response, fast biodegradation characteristics and inappropriate biocompatibility. Given these drawbacks, osteoimmunology and its advanced functional biomaterials have gained growing attention in recent years. Immune system plays an essential role during bone healing via regulating the shift from inflammatory to anti-inflammation phenotype, and inflammatory cytokines response. The inflammatory reaction mainly include infiltration of immune cells (such as macrophages, neutrophils, T cells, B cells, etc) and release of inflammatory factors (such as IL-1β, IL-6, TNF-α, etc.) at the bone defects, which subsequently affect the step-wised process of bone healing rejuvenation. Hence, advanced bone biomaterials with immunomodulatory properties is of great significance for the treatment of patients with recalcitrant bone defects, especially for delayed healing or non-union. The reciprocal mechanism of immuno-modulated bone healing, however, is not fully understood and more research is required in the future.
Subject(s)
Osteogenesis , Cytokines , Biocompatible Materials , Macrophages , T-Lymphocytes , Bone RegenerationABSTRACT
Objective: To describe the current reporting of pneumonia of unknown etiology (PUE) and factors that affect reporting by clinicians in China using the PUE surveillance system in order to provide a reference for improving PUE reporting rates in the future. Methods: Clinicians were recruited via the Sojump platform and requested to complete an anonymous self-administered questionnaire. Multivariate logistic regression analysis was used to assess factors influencing clinicians' reporting activities. Results: This study showed a low PUE case reporting rate and a poor understanding of PUE reporting among the investigated clinicians. Of the 136 clinicians who had diagnosed PUE cases, multivariate logistic regression analysis results showed that clinicians who had attended in-hospital training were more likely to report PUE than those who had not (OR 4.48, 95% CI 1.49-13.46). Clinicians with an expert panel on PUE in their hospital were more likely to report PUE cases than those without (OR 5.46, 95% CI 1.85-16.11). Conclusions: There is a need to promote and reinforce PUE surveillance system training among medical staff. In addition, PUE testing technologies in hospital laboratories should be upgraded, especially in primary and unclassified hospitals, to increase surveillance efficiency and improve PUE reporting rates.
ABSTRACT
Objective: To describe the current reporting of pneumonia of unknown etiology (PUE) and factors that affect reporting by clinicians in China using the PUE surveillance system in order to provide a reference for improving PUE reporting rates in the future. Methods: Clinicians were recruited via the Sojump platform and requested to complete an anonymous self-administered questionnaire. Multivariate logistic regression analysis was used to assess factors influencing clinicians' reporting activities. Results: This study showed a low PUE case reporting rate and a poor understanding of PUE reporting among the investigated clinicians. Of the 136 clinicians who had diagnosed PUE cases, multivariate logistic regression analysis results showed that clinicians who had attended in-hospital training were more likely to report PUE than those who had not (OR 4.48, 95% CI 1.49-13.46). Clinicians with an expert panel on PUE in their hospital were more likely to report PUE cases than those without (OR 5.46, 95% CI 1.85-16.11). Conclusions: There is a need to promote and reinforce PUE surveillance system training among medical staff. In addition, PUE testing technologies in hospital laboratories should be upgraded, especially in primary and unclassified hospitals, to increase surveillance efficiency and improve PUE reporting rates.
ABSTRACT
<p><b>OBJECTIVE</b>To establish a risk early warning model of human infection with avian influenza A (H7N9) virus and predict the area with high risk of the outbreak of H7N9 virus infection.</p><p><b>METHODS</b>The incidence data of human infection with H7N9 virus at prefecture level in China from February 2013 to June 2014 were collected, and the geographic and meteorological data during the same period in these areas were collected too. Spatial auto regression (SAR) model and generalized additive model (GAM) were used to estimate different risk factors. Afterwards, the risk area map was created based on the predicted value of both models.</p><p><b>RESULTS</b>All the human infections with H7N9 virus occurred in the predicted areas by the early warning model in February 2014. The early warning model successfully predicted the spatial moving trend of the disease, and this trend was verified by two outbreaks in northern China in April and May 2014.</p><p><b>CONCLUSION</b>The established early warning model showed accuracy and precision in short-term prediction, which might be applied in the active surveillance, early warning and prevention/control of the outbreak of human infection with H7N9 virus.</p>