MétaCan
Menu
Back to cohort
Record W2153290611 · doi:10.1186/1742-2094-11-84

Neuroinflammation and related neuropathologies in APPSL mice: further value of this in vivomodel of Alzheimer’s disease

2014· article· en· W2153290611 on OpenAlexaff
Tina Löffler, Stefanie Flunkert, Daniel Havas, Cornelia Schweinzer, Marni D. Uger, Manfred Windisch, Ernst Steyrer, Birgit Hutter‐Paier

Bibliographic record

VenueJournal of Neuroinflammation · 2014
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsAmorfix (Canada)
FundersMedizinische Universität GrazKarl-Franzens-Universität Graz
KeywordsNeuroinflammationTBARSLipid peroxidationNeocortexAlzheimer's diseaseChemistryAstrogliosisHippocampusThiobarbituric acidPathologyEndocrinologyOxidative stressNeuroscienceInternal medicinePsychologyBiologyMedicineDiseaseCentral nervous system

Abstract

fetched live from OpenAlex

BACKGROUND: Beyond cognitive decline, Alzheimer's disease (AD) is characterized by numerous neuropathological changes in the brain. Although animal models generally do not fully reflect the broad spectrum of disease-specific alterations, the APPSL mouse model is well known to display early plaque formation and to exhibit spatial learning and memory deficits. However, important neuropathological features, such as neuroinflammation and lipid peroxidation, and their progression over age, have not yet been described in this AD mouse model. METHODS: Hippocampal and neocortical tissues of APPSL mice at different ages were evaluated. One hemisphere from each mouse was examined for micro- and astrogliosis as well as concomitant plaque load. The other hemisphere was evaluated for lipid peroxidation (quantified by a thiobarbituric acid reactive substances (TBARS) assay), changes in Aβ abundance (Aβ38, Aβ40 and Aβ42 analyses), as well as determination of aggregated Aβ content (Amorfix A4 assay). Finally, correlation analyses were performed to illustrate the time-dependent correlation between neuroinflammation and Aβ load (soluble, insoluble, fibrils), or lipid peroxidation, respectively. RESULTS: As is consistent with previous findings, neuroinflammation starts early and shows strong progression over age in the APPSL mouse model. An analyses of concomitant Aβ load and plaque deposition revealed a similar progression, and high correlations between neuroinflammation markers and soluble or insoluble Aβ or fibrillar amyloid plaque loads were observed. Lipid peroxidation, as measured by TBARS levels, correlates well with neuroinflammation in the neocortex but not the hippocampus. The hippocampal lipid peroxidation correlated strongly with the increase of LOC positive fiber load, whereas neocortical TBARS levels were unrelated to amyloidosis. CONCLUSIONS: These data illustrate for the first time the progression of major AD related neuropathological features other than plaque load in the APPSL mouse model. Specifically, we demonstrate that microgliosis and astrocytosis are prominent aspects of this AD mouse model. The strong correlation of neuroinflammation with amyloid burden and lipid peroxidation underlines the importance of these pathological factors for the development of AD. The new finding of a different relation of lipid peroxidation in the hippocampus and neocortical regions show that the model might contribute to the understanding of complex pathological mechanisms and their interplay in 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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 teacher head, 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

Citations15
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeuroinflammationSame topicAlzheimer's disease research and treatmentsFrench-language works237,207