Generation of a hybrid <i>App</i> <sup>NL-G-F/NL-G-F</sup> × <i>Thy1</i> -GCaMP6s <sup>+/-</sup> Alzheimer disease mouse mitigates the behavioral and hippocampal encoding deficits of <i>APP</i> knock-in mutations of <i>App</i> <sup>NL-G-F/NL-G-F</sup> mice
Bibliographic record
Abstract
ABSTRACT In contrast to most transgenic mouse models of Alzheimer disease (AD), knock-in mice expressing familial AD-linked mutations of the amyloid precursor protein ( App ) gene exhibit stereotypical age-dependent amyloid beta (Aβ) pathology and cognitive impairment without physiologically unrealistic App overexpression. This study investigated the effect of familial AD-linked App mutations on hippocampal CA1 neuronal activity and function. To enable calcium imaging of neuronal activity, App NL-G-F/NL-G-F knock-in (APPki) mice were crossed with Thy1 -GCaMP6s +/- (C-TG) mice to generate App NL-G-F/NL-G-F × Thy1 -GCaMP6s +/- (A-TG) mice, which were characterized at 12 months of age. A-TG mice exhibited Aβ pathology in the hippocampus. In several configurations of an air-induced running task, A-TG mice and C-TG mice were equally successful in learning to run or to stay immobile. In the Morris water place test, A-TG mice were impaired, but learned the task. Comparisons of hippocampal CA1 neuronal activity in the air-induced running task showed that A-TG mice displayed neuronal hypoactivity both during movement and immobility. A-TG mice and C-TG CA1 neuronal encoding of distance or time in the air induced running task were not different. These results suggest that knock-in of familial AD-linked mutations in A-TG mice results in Aβ pathology, neuronal hypoactivity, and cognitive impairment without severely affecting CA1 neuronal encoding. In comparison to APPki mice, A-TG mice had less severe AD-like memory impairments at 12 months of age (Saito et al., 2014; Mehla et al., 2019), suggesting that the disease onset was delayed in A-TG mice. The effect of APP mutations may have been mitigated through genetic mechanisms when APPKi mice were crossed with C-TG mice.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".