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Record W3101792118 · doi:10.1093/brain/awaa326

Evolution of neuroinflammation across the lifespan of individuals with Down syndrome

2020· article· en· W3101792118 on OpenAlexafffund
Lisi Flores Aguilar, M. Florencia Iulita, Olivia Kovecses, M.D. Martínez del Valle Torres, Sarah M Levi, Yian Zhang, Manor Askenazi, Thomas Wısnıewskı, Jorge Busciglio

Bibliographic record

VenueBrain · 2020
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsMcGill University
FundersNational Institute on AgingCanadian Institutes of Health ResearchAlzheimer SocietyMcGill University
KeywordsNeuroinflammationNeurosciencePsychologyMedicineInflammationInternal medicine

Abstract

fetched live from OpenAlex

Epidemiological and experimental studies suggest that a disease-aggravating neuroinflammatory process is present at preclinical stages of Alzheimer's disease. Given that individuals with Down syndrome are at increased genetic risk of Alzheimer's disease and therefore develop the spectrum of Alzheimer's neuropathology in a uniform manner, they constitute an important population to study the evolution of neuroinflammation across the Alzheimer's continuum. Therefore, in this cross-sectional study, we characterized the brain inflammatory profile across the lifespan of individuals with Down syndrome. Microglial morphology and inflammatory cytokine expression were analysed by immunohistochemistry and electrochemiluminescent-based immunoassays in the frontal cortex from foetuses to adults with Down syndrome and control subjects (16 gestational weeks to 64 years), totalling 127 cases. Cytokine expression in mixed foetal primary cultures and hippocampus of adults with Down syndrome, as well as the effects of sex on cytokine expression were also analysed. A higher microglial soma size-to-process length ratio was observed in the frontal cortex of children and young adults with Down syndrome before the development of full-blown Alzheimer's pathology. Moreover, young adults with Down syndrome also displayed increased numbers of rod-like microglia. Increased levels of interleukin-8 and interleukin-10 were observed in children with Down syndrome (1-10 years; Down syndrome n = 5, controls n = 10) and higher levels of interleukin-1β, interleukin-1α, interleukin-6, interleukin-8, interleukin-10, interleukin-15, eotaxin-3, interferon gamma-induced protein 10, macrophage-derived chemokine, and macrophage inflammatory protein-beta, were found in young adults with Down syndrome compared to euploid cases (13-25 years, Down syndrome n = 6, controls n = 24). Increased cytokine expression was also found in the conditioned media of mixed cortical primary cultures from second trimester foetuses with Down syndrome (Down syndrome n = 7, controls n = 7). Older adults with Down syndrome (39-68 years, Down syndrome n = 22, controls n = 16) displayed reduced levels of interleukin-10, interleukin-12p40, interferon-gamma and tumour necrosis factor-alpha. Microglia displayed larger somas and shorter processes. Moreover, an increase in dystrophic microglia and rod-like microglia aligning to neurons harbouring tau pathology were also observed. Sex stratification analyses revealed that females with Down syndrome had increased interleukin-6 and interleukin-8 levels compared to males with Down syndrome. Finally, multivariate projection methods identified specific cytokine patterns among individuals with Down syndrome. Our findings indicate the presence of an early and evolving neuroinflammatory phenotype across the lifespan in Down syndrome, a knowledge that is relevant for the discovery of stage-specific targets and for the design of possible anti-inflammatory trials against Alzheimer's disease in this population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.309
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations145
Published2020
Admission routes2
Has abstractyes

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