19,934 research outputs found

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    BackgroundMultiple sclerosis (MS) is a chronic neuroinflammatory disease of the central nervous system, characterized by demyelination and neurodegeneration, which has a profound impact on the quality of life. Dysregulated inflammatory processes are a major driver of MS progression, with many areas of research being dedicated to modulating inflammation in people with MS. Several dietary patterns have been associated with improvements in inflammatory biomarkers; although, the findings have been inconsistent. Thus, this study aims to evaluate the effects of dietary interventions on inflammatory markers in adults with MS.MethodsElectronic databases, including PubMed/MEDLINE, Web of Science, Scopus, and Cochrane/Central, will be searched. Screening, selection, and extraction of data, along with quality assessment of included studies, will be done by two separate reviewers, and any potential conflicts will be settled through discussion. Two reviewers will independently assess the risk of bias in included studies using the Cochrane Risk of Bias Tool. If plausible, the results will be synthesized and pooled for meta-analysis. The overall quality of evidence of each study will be evaluated using the NutriGRADE tool, which is a modification to the Grading Recommendations Assessment, Development, and Evaluation (GRADE) developed specifically for nutrition research.DiscussionStudies have demonstrated conflicting results regarding the effects of dietary interventions on serum levels of inflammatory biomarkers among people with MS. Thus, it is expected that the planned systematic review and meta-analysis will yield robust evidence on the effects of diet on inflammatory profile in the setting of MS.</div

    Lowest and highest intersection-specific intersectional predicted probabilities (and 95% CIs) of having a PC provider.

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    Lowest and highest intersection-specific intersectional predicted probabilities (and 95% CIs) of having a PC provider.</p

    Neuromorphometric associations with mood, cognition, and self-reported exercise levels in epilepsy and healthy individuals

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    Regular physical activity may promote beneficial neuroplasticity, e.g., increased hippocampus volume. However, it is unclear whether self-reported physical exercise in leisure (PEL) levels are associated with the brain structure features demonstrated by exercise interventions. This pilot study investigated the relationship between PEL, mood, cognition, and neuromorphometry in patients with idiopathic generalized epilepsy (IGEs) compared to healthy controls (HCs). Seventeen IGEs and 19 age- and sex-matched HCs underwent magnetic resonance imaging (MRI) at 3T. The Baecke Questionnaire of Habitual Physical Activity, Profile of Mood States, and Montreal Cognitive Assessment (MoCA) assessed PEL, mood, and cognition, respectively. Structural MRI data were analyzed by voxel- and surface-based morphometry. IGEs had significantly lower PEL (p < 0.001), poorer mood (p = 0.029), and lower MoCA scores (p = 0.027) than HCs. These group differences were associated with reduced volume, decreased gyrification, and altered surface topology (IGEs < HCs) in frontal, temporal and cerebellar regions involved in executive function, memory retrieval, and emotional regulation, respectively.These preliminary results support the notion that increased PEL may promote neuroplasticity in IGEs, thus emphasizing the role of physical activity in promoting brain health in people with epilepsy

    Consistent patterns of common species across tropical tree communities

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    International audienceAbstract Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations 1–6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories 7 , we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees

    Intersectional predicted probabilities and corresponding confidence intervals in S1A-S1C Tables were obtained from Model 3, a multilevel regression analysis wherein the level-1 units were the individual respondents, and the level-2 units were intersections.

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    This model was fully adjusted for the individual dimensions used to construct the intersections (i.e., gender, age, immigration status, race, income). Age categories were defined as young adult (18–39 years), middle-aged adult (40–59 years), and older adult (60+ years). Immigration status categories were defined as recent immigrant (0–9 years) and established immigrant (10–121 years). Income categories were defined as low (bottom 30%), middle (middle 40%), high (upper 30%). S1A Table provides a complete list of intersection descriptions, ranked from lowest to highest predicted probability of having a primary care provider. S1B and S1C Tables provide a list of intersections with predicted probabilities that fall within the widest and narrowest 10% of confidence intervals, respectively. (DOCX)</p

    Stratum-specific intersectional predicted probability (and 95% confidence interval) of having a PC provider.

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    PC: Primary care. Intersectional predicted probabilities are based on the Model 3, a multilevel regression analysis wherein the level-1 units were the individual respondents, and the level-2 units were intersections. This model was fully adjusted for the individual dimensions used to construct the intersections (i.e., gender, age, immigration status, race, income). Age categories were defined as young adult (18–39 years), middle-aged adult (40–59 years), and older adult (60+ years). Immigration status categories were defined as recent immigrant (0–9 years) and established immigrant (10–121 years). Income categories were defined as low (bottom 30%), middle (middle 40%), high (upper 30%). Intersections are ranked by the size of intersectional predicted probabilities. Corresponding intersection descriptions can be found in S1A Table.</p

    Model 3 summary.

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    BackgroundDespite the Canadian healthcare system’s commitment to equity, evidence for disparate access to primary care (PC) providers exists across individual social identities/positions. Intersectionality allows us to reflect the realities of how social power shapes healthcare experiences at an individual’s interdependent and intersecting social identities/positions. The objectives of this study were to determine: (1) the extent to which intersections can be used classify those who had/did not have a PC provider; (2) the degree to which each social identity/position contributes to the ability to classify individuals as having a PC provider; and (3) predicted probabilities of having a PC provider for each intersection.Methods and findingsUsing national cross-sectional data from 241,445 individuals in Canada aged ≥18, we constructed 320 intersections along the dimensions of gender, age, immigration status, race, and income to examine the outcome of whether one had a PC provider. Multilevel analysis of individual heterogeneity and discriminatory accuracy, a multi-level model using individual-level data, was employed to address intersectional objectives. An intra-class correlation coefficient (ICC) of 23% (95%CI: 21–26%) suggests that these intersections could, to a very good extent, explain individual variation in the outcome, with age playing the largest role. Not all between-intersection variance in this outcome could be explained by additive effects of dimensions (remaining ICC: 6%; 95%CI: 2–16%). The highest intersectional predicted probability existed for established immigrant, older South Asian women with high income. The lowest intersectional predicted probability existed for recently immigrated, young, Black men with low income.ConclusionsDespite a “universal” healthcare system, our analysis demonstrated a substantial amount of inequity in primary care across intersections of gender, age, immigration status, race, and income.</div

    Understanding intersectional inequality in access to primary care providers using multilevel analysis of individual heterogeneity and discriminatory accuracy.

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    BackgroundDespite the Canadian healthcare system's commitment to equity, evidence for disparate access to primary care (PC) providers exists across individual social identities/positions. Intersectionality allows us to reflect the realities of how social power shapes healthcare experiences at an individual's interdependent and intersecting social identities/positions. The objectives of this study were to determine: (1) the extent to which intersections can be used classify those who had/did not have a PC provider; (2) the degree to which each social identity/position contributes to the ability to classify individuals as having a PC provider; and (3) predicted probabilities of having a PC provider for each intersection.Methods and findingsUsing national cross-sectional data from 241,445 individuals in Canada aged ≥18, we constructed 320 intersections along the dimensions of gender, age, immigration status, race, and income to examine the outcome of whether one had a PC provider. Multilevel analysis of individual heterogeneity and discriminatory accuracy, a multi-level model using individual-level data, was employed to address intersectional objectives. An intra-class correlation coefficient (ICC) of 23% (95%CI: 21-26%) suggests that these intersections could, to a very good extent, explain individual variation in the outcome, with age playing the largest role. Not all between-intersection variance in this outcome could be explained by additive effects of dimensions (remaining ICC: 6%; 95%CI: 2-16%). The highest intersectional predicted probability existed for established immigrant, older South Asian women with high income. The lowest intersectional predicted probability existed for recently immigrated, young, Black men with low income.ConclusionsDespite a "universal" healthcare system, our analysis demonstrated a substantial amount of inequity in primary care across intersections of gender, age, immigration status, race, and income
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