Human cysticercosis is a disease caused by larvae of the cestode Taenia solium. It is the most common cause of adult-acquired epilepsy world-wide where it exacts a debilitating toll on the health and well-being of affected communities. It is commonly assumed that the major symptoms associated with cysticercosis are a result of the direct presence of larvae in the brain. As a result, the possible effect of peripherally located larvae on the central nervous system are not well understood. To address this question, we utilised the Taenia crassiceps intra-peritoneal murine model of cysticercosis, where larvae are restricted to the peritoneal cavity. In this model, previous research has observed behavioural changes in rodents but not the development of seizures. Here we used ELISAs, immunoblotting and the Evans Blue test for blood-brain barrier permeability to explore the central effects of peripheral infection of mice with Taenia crassiceps. We identified high levels of parasite-targeting immunoglobulins in the serum of Taenia crassiceps infected mice. We show that the Taenia crassciceps larvae themselves also contain and release host immunoglobulins over time. Additionally, we describe, for the first time, significantly increased levels of IgG within the hippocampi of infected mice, which are accompanied by changes in blood-brain barrier permeability. However, these Taenia crassiceps induced changes were not accompanied by alterations to the levels of proinflammatory, pro-seizure cytokines in the hippocampus. These findings contribute to the understanding of systemic and neuroimmune responses in the Taenia crassiceps model of cysticercosis, with implications for the pathogenesis of human cysticercosis.
This study uses international respondents to a COVID-lockdown related questionnaire (n = 1,688) to assess the determinants of adherence and poor coping in response to lockdown measures. A regression analysis was used to compare the relative importance of clusters derived from a K-means cluster analysis as well as various demographics (age, gender, level of education, political affiliation, a factor reflecting social security and a factor reflecting the lockdown harshness). Three distinct clusters (General Population, Extreme Responders and Sufferers) were identified, corresponding well to a previous study. Clusters appeared to be the best overall predictors of coping and adherence although gender, political affiliation and lockdown harshness were also important predictors. The large proportion of variance that remains unexplained, combined with the relatively weak effects of traditional demographics, suggest that less concrete variables such as personality traits, health and environmental factors may be better predictors of adherence and coping during a pandemic.
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