A Novel Transgenic Rat Model with a Full Alzheimer's-Like Amyloid Pathology Displays Pre-Plaque Intracellular Amyloid-β-Associated Cognitive Impairment
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative pathology in which amyloid-beta (Abeta) peptide accumulates in different brain areas leading to deposition of plaques and a progressive decline of cognitive functions. After a decade in which a number of transgenic (Tg) mouse models mimicking AD-like amyloid-deposition pathology have been successfully generated, few rat models have been reported that develop intracellular and extracellular Abeta accumulation, together with impairment of cognition. The generation of a Tg rat reproducing the full AD-like amyloid pathology has been elusive. Here we describe the generation and characterization of a new transgenic rat line, coded McGill-R-Thy1-APP, developed to express the human amyloid-beta precursor protein (AbetaPP) carrying both the Swedish and Indiana mutations under the control of the murine Thy1.2 promoter. The selected mono-transgenic line displays an extended phase of intraneuronal Abeta accumulation, already apparent at 1 week after birth, which is widespread throughout different cortical areas and the hippocampus (CA1, CA2, CA3, and dentate gyrus). Homozygous Tg animals eventually produce extracellular Abeta deposits and, by 6 months of age, dense, thioflavine S-positive, amyloid plaques are detected, associated with glial activation and surrounding dystrophic neurites. The cognitive functions in transgenic McGill-R-Thy1-APP rats, as assessed using the Morris water maze task, were found already altered as early as at 3 months of age, when no CNS plaques are yet present. The spatial cognitive impairment becomes more prominent in older animals (13 months), where the behavioral performance of Tg rats positively correlates with the levels of soluble Abeta (trimers) measured in the cortex.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".