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Record W7039966305

Neuroinflammation in Alzheimer's Disease: a therapeutic target

2020· dissertation· en· W7039966305 on OpenAlexaff

Bibliographic record

VenueMspace (University of Manitoba) · 2020
Typedissertation
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNeuroinflammationMicrogliaNeurodegenerationProinflammatory cytokineCognitive declineInflammationContext (archaeology)Amyloid (mycology)Genetically modified mouse
DOInot available

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD), the most common dementia, is an age-related neurodegenerative disorder characterized by memory loss, extracellular β-amyloid (Aβ) peptide deposits and intracellular neurofibrillary tangle formation. Current animal models of AD center on the precipitating role of Aβ deposition in a cascade of biological events that ultimately lead to neurodegeneration and dementia. However, increasing evidence shows that the amount of measurable amyloid deposition is a relatively weak correlate of cognitive function impairment among AD patients or individuals at risk for AD. Furthermore, autopsy studies reveal that older individuals with significantly elevated amyloid levels have no symptoms of AD. Together, these findings suggest that Aβ is necessary but perhaps not sufficient to cause the AD syndrome. Neuroinflammation has been implicated in cognitive aging for years. Neuroinflammatory response consists of activated microglia and astrocytes and the release of proinflammatory markers. Acute inflammation plays a critical role in brain function against insults. Neuropathological and neuroimaging studies have demonstrated that uncontrolled glial activation and neuroinflammation in the AD brain may contribute independently to neural dysfunction and cell death. This thesis shows that both microglial and astroglial activation occurred in paralleled with the pattern of learning deficits rather than amyloid pathology in an APP/PS1 transgenic mouse model of AD. These findings suggest that neuroinflammation might directly contribute to the development and progression of cognitive deficits in APP/PS1 mice. In the context of significant amyloid deposition, the results of the current study suggest that mechanisms underlying the inflammatory process might be an important therapeutic target for AD. Finally, this work presents a chronic administration of quetiapine in APP/PS1 transgenic mice resulted in a marked change in microglial and astrocyte activation, proinflammatory cytokine levels, and an improvement in behavioural performance. The beneficial effects of quetiapine occurred when there were only marginal changes in levels of total Aβ, suggesting the anti-inflammatory effect of quetiapine could account for the cognitive improvement observed in APP/PS1 transgenic mice. Moreover, we confirmed that quetiapine significantly reduced Aβ1-42 induced secretion of proinflammatory cytokines in primary cultured microglia. Both in vitro and in vivo experiments demonstrated that quetiapine ameliorated proinflammatory cytokine increases via suppression of NF-κB pathway activation. Since risk factors for the development of inflammation are modifiable, these findings suggest intervention and prevention strategies for the clinical syndrome of AD.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.027
GPT teacher head0.262
Teacher spread0.235 · 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 designNot applicable
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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