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Record W4312087422 · doi:10.1002/alz.065789

GFAP expression levels are associated with brain glucose metabolism in a rat model of Alzheimer’s disease

2022· article· en· W4312087422 on OpenAlexaff
Luiza Santos Machado, Andréia Silva da Rocha, Carolina Soares, Débora Guerini de Souza, Vanessa G. Ramos, Bruna Bellaver, Pâmela C.L. Ferreira, Eduardo R. Zimmer

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsMcGill University
Fundersnot available
KeywordsGlial fibrillary acidic proteinCerebellumCortex (anatomy)PonsBiologyInternal medicineEndocrinologyPathologyNeuroscienceMedicineImmunohistochemistry

Abstract

fetched live from OpenAlex

Abstract Background Glial Fibrillary Acidic Protein (GFAP) is an intermediate filament expressed almost exclusively in astrocytes, and it is usually overexpressed in reactive astrocytes. We have previously shown that astrocytes play a critical role in brain energy metabolism and are significant contributors to the [18F]FDG‐PET signal. The cortex is majorly composed of glial cells, whereas the cerebellum is predominantly composed of neurons. In light of this, whether GFAP levels in the brain tissue are associated with [18F]FDG‐PET signal remains unknown. Thus, we aimed to investigate the associations between cortical and cerebellar GFAP levels with brain glucose metabolism in a rat model of Alzheimer’s disease. Methods Brain metabolism of ten‐month‐old TgF344‐AD (n=8) and wild‐type (WT,n=6) rats was assessed with [18F]FDG‐PET. Then, GFAP RNA levels were quantified in the frontal cortex (CXF), temporoparietal cortex (CXTP), and cerebellum (CB) using qRT‐PCR. The [18F]FDG‐PET standardized uptake ratio (SUVr) was calculated using pons as the reference region. T‐tests were used to assess the differences between groups in GFAP RNA expression levels and regional brain [18F]FDG‐PET. Comparisons were conducted using t‐statistical analyses at voxel level (RMINC). Differences were considered statistically significant at p<0.05 (t>2). Results No differences between groups were found in the whole brain FDG metabolism or regions of interest (CXF, CXTP, and CB;t(13)<2). At the voxel level, we identified small cortical hypermetabolic clusters in the TgF344‐AD rats (t(13)=5.55, local maxima). The GFAP RNA was increased in the CXTP of TgF344‐AD rats (p=0.001). No significant differences were found between groups in GFAP RNA levels in other brain regions evaluated. Interestingly, we found positive correlations between [18F]FDG‐PET metabolism and the GFAP RNA expression in the CXTP (local maxima, t(13)=8.33;Fig.1a) and CXF (local maxima, t(13)=4.82;Fig.1b). By contrast, [18F]FDG metabolism did not associate with GFAP RNA expression levels in the CB (t(13)<2;Fig.1c). Conclusion GFAP RNA levels were increased in the CXTP of TgF344‐AD rats, which may indicate early astrocyte reactivity. GFAP RNA expression was associated with [18F]FDG metabolism only in cortical regions, suggesting that astrocytes may have major roles in cortical glucose uptake, but not in cerebellum.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.258
Teacher spread0.228 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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