Do social support and community engagement act as mechanisms in the association between neighbourhood income inequality and the mental health of mothers in Calgary,Canada? A mediation analysis

PurposeAccording to the social determinants of health framework, income inequality is a potential risk factor for adverse mental health. However, few studies have explored the mechanisms suspected to mediate this relationship. The current study addresses this gap through a mediation analysis to determine if social support and community engagement act as mediators linking neighbourhood income inequality to maternal anxiety and depressive symptoms within a cohort of new mothers living in the City of Calgary, Canada.MethodsData collected at three years postpartum from mothers belonging to the All Our Families (AOF) cohort were used in the current study. Maternal data were collected between 2012 and 2015 and linked to neighbourhood socioeconomic data from the 2006 Canadian Census. Income inequality was measured using Gini coefficients derived from 2006 after-tax census data. Generalized structural equation models were used to quantify the associations between income inequality and mental health symptoms, and to assess the potential direct and indirect mediating effects of maternal social support and community engagement.ResultsIncome inequality was not significantly associated with higher depressive symptoms (β = 0.32, 95%CI = −0.067, 0.70), anxiety symptoms (β = 0.11, 95%CI = −0.39, 0.60), or lower social support. Income inequality was not associated with community engagement. For the depression models, higher social support was significantly associated with lower depressive symptoms (β = −0.13, 95%CI = −0.15, −0.097), while community engagement was not significantly associated with depressive symptoms (β = 0.059, 95%CI = −0.15, 0.27). Similarly, for the anxiety models, lower anxiety symptoms were significantly associated with higher levels of social support (β = −0.17, 95%CI = −0.20, −0.13) but not with higher levels of community engagement (β = 0.14, 95%CI = −0.14, 0.41).ConclusionThe current study did not find clear evidence for social support or community engagement mediating the relationship between neighbourhood income inequality and maternal mental health. Future investigations should employ a broader longitudinal approach to capture changes in income inequality, potential mediators, and mental health symptomatology over time.     

宫颈癌幸存者社会疏离感现状及影响因素研究

目的 探讨宫颈癌幸存者社会疏离现状及其影响因素，为实施针对性干预提供参考。方法选取395例宫颈癌幸存者作为研究对象，采用一般情况调查表、一般疏离感量表、社会影响量表、社会支持评定量表进行调查。结果宫颈癌幸存者一般疏离感量表得分（41.98±6.81）分；家庭人均月收入、治疗后时间、病耻感总分、社会支持总分是社会疏离的主要影响因素(均P＜0.05），可以解释46.1%的变异量。结论宫颈癌幸存者社会疏离总体水平呈中等偏高。医护人员应关注宫颈癌幸存者的社会疏离水平，尤其是低收入、治疗后时间短的患者，可通过降低患者病耻感和提高患者社会支持水平，降低其社会疏离水平。     

Immunogenicity and protective efficacy of adenoviral and subunit RSV vaccines based on stabilized prefusion F protein in pre-clinical models

Respiratory Syncytial Virus (RSV) remains a leading cause of severe respiratory disease for which no licensed vaccine is available. We have previously described the derivation of an RSV Fusion protein (F) stabilized in its prefusion conformation (preF) as vaccine immunogen and demonstrated superior immunogenicity in naive mice of preF versus wild type RSV F protein, both as protein and when expressed from an Ad26 vaccine vector. Here we address the question if there are qualitative differences between the two vaccine platforms for induction of protective immunity. In naïve mice, both Ad26.RSV.preF and preF protein induced humoral responses, whereas cellular responses were only elicited by Ad26.RSV.preF. In RSV pre-exposed mice, a single dose of either vaccine induced cellular responses and strong humoral responses. Ad26-induced RSV-specific cellular immune responses were detected systemically and locally in the lungs. Both vaccines showed protective efficacy in the cotton rat model, but Ad26.RSV.preF conferred protection at lower virus neutralizing titers in comparison to RSV preF protein. Factors that may contribute to the protective capacity of Ad26.RSV.preF elicited immunity are the induced IgG2a antibodies that are able to engage Fcγ receptors mediating Antibody Dependent Cellular Cytotoxicity (ADCC), and the induction of systemic and lung resident RSV specific CD8 + T cells. These data demonstrate qualitative improvement of immune responses elicited by an adenoviral vector based vaccine encoding the RSV preF antigen compared to the subunit vaccine in small animal models which may inform RSV vaccine development.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.