Early postnatal defects in neurogenesis in the 3xTg mouse model of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder leading to dementia. The hippocampus, which is one of the sites where neural stem cells reside and new neurons are born, exhibits the most significant neuronal loss in AD. A decline in adult neurogenesis has been described in several animal models of AD. However, the age at which this defect first appears remains unknown. To determine at which stage, from birth to adulthood, the neurogenic deficits are found in AD, we used the triple transgenic mouse model of AD (3xTg). We show that defects in neurogenesis are present as early as postnatal stages, well before the onset of any neuropathology or behavioral deficits. We also show that 3xTg mice have significantly fewer neural stem/progenitor cells, with reduced proliferation and decreased numbers of newborn neurons at postnatal stages, consistent with reduced volumes of hippocampal structures. To determine whether there are early changes in the molecular signatures of neural stem/progenitor cells, we perform bulk RNA-seq on cells sorted directly from the hippocampus. We show significant changes in the gene expression profiles at one month of age, including genes of the Notch and Wnt pathways. These findings reveal impairments in neurogenesis very early in the 3xTg AD model, which provides new opportunities for early diagnosis and therapeutic interventions to prevent neurodegeneration in 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".