Expression of Phenotypic Astrocyte Marker Is Increased in a Transgenic Mouse Model of Alzheimer’s Disease versus Age-Matched Controls: A Presymptomatic Stage Study
Bibliographic record
Abstract
Recent mouse studies of the presymptomatic stage of Alzheimer's disease (AD) have suggested that proinflammatory changes, such as glial activation and cytokine induction, may occur already at this early stage through unknown mechanisms. Because TNFα contributes to increased Aβ production from the Aβ precursor protein (APP), we assessed a putative correlation between APP/Aβ and TNFα during the presymptomatic stage as well as early astrocyte activation in the hippocampus of 3-month-old APPswe/PS1dE9 mice. While Western blots revealed significant APP expression, Aβ was not detectable by Western blot or ELISA attesting that 3-month-old, APPswe/PS1dE9 mice are at a presymptomatic stage of AD-like pathology. Western blots were also used to show increased GFAP expression in transgenic mice that positively correlated with both TNFα and APP, which were also mutually correlated. Subregional immunohistochemical quantification of phenotypic (GFAP) and functional (TSPO) markers of astrocyte activation indicated a selective and significant increase in GFAP-immunoreactive (IR) cells in the dentate gyrus of APPswe/PS1dE9 mice. Our data suggest that subtle morphological and phenotypic alterations, compatible with the engagement of astrocyte along the activation pathway, occur in the hippocampus already at the presymptomatic stage of AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".