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Record W3112701072 · doi:10.1002/alz.045613

The effects of peripheral lipoprotein metabolism on cerebrovascular inflammation in APP/PS1 mice

2020· article· en· W3112701072 on OpenAlexaff
Emily B. Button, Guilaine K. Boyce, Anna Wilkinson, Sophie Stukas, Arooj Hayat, Wai Hang Cheng, Brennan J. Wadsworth, Jianjia Fan, Jérôme Robert, Kris M. Martens, Cheryl L. Wellington

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAstrogliosisApolipoprotein EGlial fibrillary acidic proteinInternal medicineInflammationEndocrinologyMedicineCognitive declinePathologyDementiaDiseaseCentral nervous system

Abstract

fetched live from OpenAlex

Abstract Background Although Alzheimer’s disease (AD) is characterized by amyloid beta (Aβ) plaques and neurofibrillary tangles, most AD patients also exhibit cerebrovascular dysfunction. Therefore, strategies promoting cerebrovascular resilience have potential as therapeutic or preventative interventions against AD. High‐density lipoproteins (HDL) have several known beneficial functions on peripheral vessels. Further, increased plasma HDL concentrations have been associated with reduced dementia risk and HDL supplementation reduces both amyloid pathology and cognitive deficits in AD model mice. We therefore hypothesized that HDL may promote cerebrovascular health to protect against AD. We investigated the effects of HDL specifically on the cerebrovasculature in APP/PS1 mice, an AD model, using two methods. Methods First, we reduced plasma HDL levels by creating APP/PS1 mice genetically deficient for apolipoprotein (apo)A‐I, the primary protein component of HDL. Second, we treated 12‐month old APP/PS1 mice with a non‐brain penetrant liver X receptor (LXR) agonist for seven days to selectively upregulate the HDL biogenesis pathways in peripheral tissues. We measured cognitive deficits using cued and contextual fear conditioning tests. Fluorescent amyloid staining was used to assess vascular amyloid deposition. Immunofluorescent imaging evaluating glial fibrillary acidic protein (GFAP) and intercellular adhesion molecule 1 (ICAM‐1) association with CD31 positive area was used to assess vessel‐associated astrogliosis and endothelial inflammation, respectively. Results At 12 months of age, apoAI‐null mice had increased cognitive deficits in cued and contextual fear conditioning tests, increased vascular amyloid burden, increased vessel‐associated astrogliosis, and increased brain endothelial cell inflammation. Seven days of LXR agonist treatment had no effect on amyloid pathology or astrogliosis. However, LXR agonist‐treated APP/PS1 mice displayed reduced cognitive deficits in cued fear conditioning tests and reduced endothelial cell inflammation. Conclusions Circulating HDL promotes cerebrovascular health in APP/PS1 mice by limiting both Aβ‐induced vascular inflammation and vascular Aβ deposition resulting in reduced amyloid pathology and cognitive deficits.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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 designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

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