Possible Involvement of Transthyretin in Hippocampal β-Amyloid Burden and Learning Behaviors in a Mouse Model of Alzheimer’s Disease (TgCRND8)
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive memory loss, possibly triggered by the accumulation of beta-amyloid (Abeta) peptides and the hyperphosphorylation of Tau neurofilament protein. Recent findings have shown that transthyretin (TTR) is a potent scavenger of Abeta peptide deposits, suggesting a possible neuroprotective role for TTR in neurodegenerative processes associated with amyloidogenesis, such as AD. METHODS: To investigate the relationship between TTR and Abeta deposition, we crossed mouse carrying a deletion of TTR (TTR(- or -)) with a transgenic mouse model of AD (TgCRND8), and Abeta burden and spatial learning capacities were evaluated at 4 and 6 months of age (exclusion of the 6 month-old TgCRND8/TTR(- or -) group due to low survival rate). RESULTS: Rather surprisingly, Abeta plaque burden was significantly reduced in the hippocampus of 4-month-old TgCRND8/TTR(+ or -), and to a lesser extent in TgCRND8/TTR(- or -), as compared to age-matched TgCRND8/TTR(+ or +). No difference in plaque burden was found between any groups in 6-month-old animals. At 4 and 6 months of age, all populations of these hybrid transgenic mice displayed similar magnitude of spatial memory deficits in the Morris water maze task. CONCLUSION: Since TgCRND8 mice represent an aggressive model of Abeta deposition with plaques developing as early as 3 months of age, along with spatial learning deficits, it may be already too late at 4 and 6 months of age to observe significant changes due to the deletion of the TTR gene.
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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) | 0.001 | 0.000 |
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".