ChemR23 Dampens Lung Inflammation and Enhances Anti-viral Immunity in a Mouse Model of Acute Viral Pneumonia

Viral diseases of the respiratory tract, which include influenza pandemic, children acute bronchiolitis, and viral pneumonia of the elderly, represent major health problems. Plasmacytoid dendritic cells play an important role in anti-viral immunity, and these cells were recently shown to express ChemR23, the receptor for the chemoattractant protein chemerin, which is expressed by epithelial cells in the lung. Our aim was to determine the role played by the chemerin/ChemR23 system in the physiopathology of viral pneumonia, using the pneumonia virus of mice (PVM) as a model. Wild-type and ChemR23 knock-out mice were infected by PVM and followed for functional and inflammatory parameters. ChemR23−/− mice displayed higher mortality/morbidity, alteration of lung function, delayed viral clearance and increased neutrophilic infiltration. We demonstrated in these mice a lower recruitment of plasmacytoid dendritic cells and a reduction in type I interferon production. The role of plasmacytoid dendritic cells was further addressed by performing depletion and adoptive transfer experiments as well as by the generation of chimeric mice, demonstrating two opposite effects of the chemerin/ChemR23 system. First, the ChemR23-dependent recruitment of plasmacytoid dendritic cells contributes to adaptive immune responses and viral clearance, but also enhances the inflammatory response. Second, increased morbidity/mortality in ChemR23−/− mice is not due to defective plasmacytoid dendritic cells recruitment, but rather to the loss of an anti-inflammatory pathway involving ChemR23 expressed by non-leukocytic cells. The chemerin/ChemR23 system plays important roles in the physiopathology of viral pneumonia, and might therefore be considered as a therapeutic target for anti-viral and anti-inflammatory therapies

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

Development of a 3D Collagen Model for the In Vitro Evaluation of Magnetic-assisted Osteogenesis

Abstract Magnetic stimulation has been applied to bone regeneration, however, the cellular and molecular mechanisms of repair still require a better understanding. A three-dimensional (3D) collagen model was developed using plastic compression, which produces dense, cellular, mechanically strong native collagen structures. Osteoblast cells (MG-63) and magnetic iron oxide nanoparticles (IONPs) were incorporated into collagen gels to produce a range of cell-laden models. A magnetic bio-reactor to support cell growth under static magnetic fields (SMFs) was designed and fabricated by 3D printing. The influences of SMFs on cell proliferation, differentiation, extracellular matrix production, mineralisation and gene expression were evaluated. Polymerase chain reaction (PCR) further determined the effects of SMFs on the expression of runt-related transcription factor 2 (Runx2), osteonectin (ON), and bone morphogenic proteins 2 and 4 (BMP-2 and BMP-4). Results demonstrate that SMFs, IONPs and the collagen matrix can stimulate the proliferation, alkaline phosphatase production and mineralisation of MG-63 cells, by influencing matrix/cell interactions and encouraging the expression of Runx2, ON, BMP-2 and BMP-4. Therefore, the collagen model developed here not only offers a novel 3D bone model to better understand the effect of magnetic stimulation on osteogenesis, but also paves the way for further applications in tissue engineering and regenerative medicine

Beyond equilibrium climate sensitivity

ISSN:1752-0908ISSN:1752-089