Covert strokes prior to Alzheimer’s disease onset accelerate peri-lesional pathology but not cognitive deficits in an inducible APP mouse model
Bibliographic record
Abstract
It is estimated that up to 1 in 3 healthy middle-aged adults will have had a covert stroke during their lifetime. Furthermore, post-stroke, survivors are more than twice as likely to develop dementia. In the present study, we aimed to model the impact of focal subclinical ischemia prior to the onset of AD pathogenesis in a preclinical model. We utilized endothelin-1 to induce ischemia in an iducible transgenic mouse model of Alzheimer's disease, APPsi:tTA, allowing for temporal control of APP gene expression. We induced the focal subclinical ischemic events in the absence of APP expression, thus prior to AD onset. T2 structural magnetic resonance imaging confirmed the volume and location of focal subclinical ischemic lesions to the medial prefrontal cortex. Following recovery from surgery and 7 weeks of APP expression, we found that two subclinical ischemic lesions resulted in a significant localized increase in amyloid load and in microglial activation proximal to the lesion. However, no differences were found in astrogliosis. A battery of behaviour tests was conducted, in which no significant differences were detected in activities of daily living and cognitive function between stroked and sham cohorts. Overall, our results demonstrated that APP expression was the sole driving force behind behavioural deficits. In conclusion, our results suggest that a history of two subclinical strokes prior to AD onset does not worsen early disease trajectory in a mouse model.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".