Distinct Temporal and Anatomical Distributions of Amyloid-β and Tau Abnormalities following Controlled Cortical Impact in Transgenic Mice

Traumatic brain injury (TBI) is a major environmental risk factor for Alzheimer's disease. Intracellular accumulations of amyloid-β and tau proteins have been observed within hours following severe TBI in humans. Similar abnormalities have been recapitulated in young 3xTg-AD mice subjected to the controlled cortical impact model (CCI) of TBI and sacrificed at 24 h and 7 days post injury. This study investigated the temporal and anatomical distributions of amyloid-β and tau abnormalities from 1 h to 24 h post injury in the same model. Intra-axonal amyloid-β accumulation in the fimbria was detected as early as 1 hour and increased monotonically over 24 hours following injury. Tau immunoreactivity in the fimbria and amygdala had a biphasic time course with peaks at 1 hour and 24 hours, while tau immunoreactivity in the contralateral CA1 rose in a delayed fashion starting at 12 hours after injury. Furthermore, rapid intra-axonal amyloid-β accumulation was similarly observed post controlled cortical injury in APP/PS1 mice, another transgenic Alzheimer's disease mouse model. Acute increases in total and phospho-tau immunoreactivity were also evident in single transgenic TauP301L mice subjected to controlled cortical injury. These data provide further evidence for the causal effects of moderately severe contusional TBI on acceleration of acute Alzheimer-related abnormalities and the independent relationship between amyloid-β and tau in this setting

Gendered dimensions of obesity in childhood and adolescence

BACKGROUND: The literature on childhood and adolescent obesity is vast. In addition to producing a general overview, this paper aims to highlight gender differences or similarities, an area which has tended not to be the principal focus of this literature. METHODS: Databases were searched using the terms 'obesity' and 'child', 'adolescent', 'teenager', 'youth', 'young people', 'sex', 'gender', 'masculine', 'feminine', 'male', 'female', 'boy' and 'girl' (or variations on these terms). In order to limit the potential literature, the main focus is on other reviews, both general and relating to specific aspects of obesity. RESULTS: The findings of genetic studies are similar for males and females, and differences in obesity rates as defined by body mass index are generally small and inconsistent. However, differences between males and females due to biology are evident in the patterning of body fat, the fat levels at which health risks become apparent, levels of resting energy expenditure and energy requirements, ability to engage in certain physical activities and the consequences of obesity for the female reproductive system. Differences due to society or culture include food choices and dietary concerns, overall physical activity levels, body satisfaction and the long-term psychosocial consequences of childhood and adolescent obesity. CONCLUSION: This review suggests differences between males and females in exposure and vulnerability to obesogenic environments, the consequences of child and adolescent obesity, and responses to interventions for the condition. A clearer focus on gender differences is required among both researchers and policy makers within this field

Multilevel socioeconomic effects on quality of life in adolescent and young adult survivors of leukemia and lymphoma

PURPOSE: Cancer registry survival analyses have shown that adolescent and young adult patients with low socioeconomic status (SES) have reduced survival compared to those with higher SES. The objective of this study was to determine whether neighborhood- (nSES) and/or individual-level SES (iSES) also predicted current quality of life in adolescent and young adult survivors. METHODS: The Socioeconomics and Quality of Life study surveyed adolescent and young adult survivors of leukemia and lymphoma at least one year post-diagnosis using population-based ascertainment. Factor analysis was used to create a multidimensional age-relevant iSES score and compared with a preexisting census-block-group derived nSES score. Four quality of life domains were assessed: physical health, psychological and emotional well-being, social relationships, and life skills. Nested multivariable linear regression models were run to test the associations between both SES measures and quality of life and to compare the explanatory power of nSES and iSES. RESULTS: Data from 110 individuals aged 16–40 were included in the final analysis. After adjustment for sociodemographic confounders, low nSES was associated only with poorer physical health, whereas low iSES was related to poorer quality of life in all four domains with iSES accounting for an additional 14, 12, 25, and 10 % of the variance, respectively. CONCLUSIONS: Measures of SES at the individual as compared to the neighborhood level may be stronger indicators of outcomes in adolescents and young adults, which has important implications for SES measurement in the context of cancer surveillance