Abnormal Cognition, Sleep, EEG and Brain Metabolism in a Novel Knock-In Alzheimer Mouse, PLB1
Bibliographic record
Abstract
Late-stage neuropathological hallmarks of Alzheimer's disease (AD) are β-amyloid (βA) and hyperphosphorylated tau peptides, aggregated into plaques and tangles, respectively. Corresponding phenotypes have been mimicked in existing transgenic mice, however, the translational value of aggressive over-expression has recently been questioned. As controlled gene expression may offer animal models with better predictive validity, we set out to design a transgenic mouse model that circumvents complications arising from pronuclear injection and massive over-expression, by targeted insertion of human mutated amyloid and tau transgenes, under the forebrain- and neurone-specific CaMKIIα promoter, termed PLB1(Double). Crossing with an existing presenilin 1 line resulted in PLB1(Triple) mice. PLB1(Triple) mice presented with stable gene expression and age-related pathology of intra-neuronal amyloid and hyperphosphorylated tau in hippocampus and cortex from 6 months onwards. At this early stage, pre-clinical (18)FDG PET/CT imaging revealed cortical hypometabolism with increased metabolic activity in basal forebrain and ventral midbrain. Quantitative EEG analyses yielded heightened delta power during wakefulness and REM sleep, and time in wakefulness was already reliably enhanced at 6 months of age. These anomalies were paralleled by impairments in long-term and short-term hippocampal plasticity and preceded cognitive deficits in recognition memory, spatial learning, and sleep fragmentation all emerging at ∼12 months. These data suggest that prodromal AD phenotypes can be successfully modelled in transgenic mice devoid of fibrillary plaque or tangle development. PLB1(Triple) mice progress from a mild (MCI-like) state to a more comprehensive AD-relevant phenotype, which are accessible using translational tools such as wireless EEG and microPET/CT.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".