Multiple myeloma is a bone marrow plasma cell tumor which is supported by the external growth factors APRIL and IL-6, among others. Recently, we identified eosinophils and megakaryocytes to be functional components of the micro-environmental niches of benign bone marrow plasma cells and to be important local sources of these cytokines. Here, we investigated whether eosinophils and megakaryocytes also support the growth of tumor plasma cells in the MOPC315.BM model for multiple myeloma. As it was shown for benign plasma cells and multiple myeloma cells, IL-6 and APRIL also supported MOPC315.BM cell growth in vitro, IL-5 had no effect. Depletion of eosinophils in vivo by IL-5 blockade led to a reduction of the early myeloma load. Consistent with this, myeloma growth in early stages was retarded in eosinophil-deficient ΔdblGATA-1 mice. Late myeloma stages were unaffected, possibly due to megakaryocytes compensating for the loss of eosinophils, since megakaryocytes were found to be in contact with myeloma cells in vivo and supported myeloma growth in vitro. We conclude that eosinophils and megakaryocytes in the niches for benign bone marrow plasma cells support the growth of malignant plasma cells. Further investigations are required to test whether perturbation of these niches represents a potential strategy for the treatment of multiple myeloma.

Recently, attention has been drawn to the need to integrate sex/gender more comprehensively into environmental health research. Considering theoretical approaches, we define sex/gender as a multidimensional concept based on intersectionality. However, operationalizing sex/gender through multiple covariates requires the usage of statistical methods that are suitable for handling such complex data. We therefore applied two different decision tree approaches: classification and regression trees (CART) and conditional inference trees (CIT). We explored the relevance of multiple sex/gender covariates for the exposure to green spaces, measured both subjectively and objectively. Data from 3742 participants from the Cooperative Health Research in the Region of Augsburg (KORA) study were analyzed within the INGER (Integrating gender into environmental health research) project. We observed that the participants’ financial situation and discrimination experience was relevant for their access to high quality public green spaces, while the urban/rural context was most relevant for the general greenness in the residential environment. None of the covariates operationalizing the individual sex/gender self-concept were relevant for differences in exposure to green spaces. Results were largely consistent for both CART and CIT. Most importantly we showed that decision tree analyses are useful for exploring the relevance of multiple sex/gender dimensions and their interactions for environmental exposures. Further investigations in larger urban areas with less access to public green spaces and with a study population more heterogeneous with respect to age and social disparities may add more information about the relevance of multiple sex/gender dimensions for the exposure to green spaces.

During the last years the need to integrate sex and gender in health-related research for better and fairer science became increasingly apparent. Various guidelines and checklists were developed to encourage and support researchers in considering the entangled dimensions of sex/gender in their research. However, a tool for the assessment of sex/gender consideration and its visualization is still missing. We aim to fill this gap by introducing an assessment matrix that can be used as a flexible instrument for comprehensively evaluating the sex/gender consideration in quantitative health-related research. The matrix was developed through an iterative and open process based on the interdisciplinary expertise represented in our research team and currently published guidelines. The final matrix consists of 14 different items covering the whole research process and the publication of results. Additionally, we introduced a method to graphically display this evaluation. By developing the matrix, we aim to provide users with a tool to systematically compare sex/gender consideration qualitatively between different publications and even different fields of study. This way, the assessment matrix represents a tool to identify research gaps and a basis for future research. In the long term, the implementation of this tool to evaluate the consideration of sex/gender should contribute to more sex/gender equitable health-related research.

Background Physical and social neighbourhood characteristics can vary according to the neighbourhood socio-economic status (SES) and influence residents’ perceptions, behaviours and health outcomes both positively and negatively. Neighbourhood SES has been shown to be predictive of mental health, which is relevant for healthy ageing and prevention of dementia or depression. Positive affectivity (PA) is an established indicator of mental health and might indicate a positive emotional response to neighbourhood characteristics. In this study, we focussed on the association of neighbourhood SES with PA among older residents in Germany and considered social integration and environmental perceptions in this association. Methods We used questionnaire-based data of the ongoing population-based Heinz Nixdorf Recall Study for our cross-sectional analysis, complemented by secondary data on social welfare rates in the neighbourhood of residents’ address. PA was assessed using the Positive and Negative Affect Schedule (PANAS) in 2016. Linear regression models were performed to estimate the associations and adjusted for socio-demographic variables. Results Higher social welfare rates were associated with lower PA scores. The strongest negative association from the crude model (b = −1.916, 95%-CI [−2.997, −0.835]) was reduced after controlling for socio-demographic variables (b = −1.429, 95%-CI [−2.511, −0.346]). Social integration factors (b = −1.199, 95%-CI [−2.276, −0.121]) and perceived environmental factors (b = −0.875, 95%-CI [−1.971, 0.221]) additionally diminished the association of social welfare rates with PA in the full model (b = −0.945, 95%-CI [−2.037, 0.147]). Conclusion Our results suggest that neighbourhoods have an influence on the occurrence and the extent of PA. Public health interventions that address socio-economic disadvantage in the neighbourhood environment could be an effective and far-reaching way to reduce the risk of depression and depressive symptoms due to low PA in older residents.

No abstract