Early-onset Amyloid Deposition and Cognitive Deficits in Transgenic Mice Expressing a Double Mutant Form of Amyloid Precursor Protein 695
Bibliographic record
Abstract
We have created early-onset transgenic (Tg) models by exploiting the synergistic effects of familial Alzheimer's disease mutations on amyloid beta-peptide (Abeta) biogenesis. TgCRND8 mice encode a double mutant form of amyloid precursor protein 695 (KM670/671NL+V717F) under the control of the PrP gene promoter. Thioflavine S-positive Abeta amyloid deposits are present at 3 months, with dense-cored plaques and neuritic pathology evident from 5 months of age. TgCRND8 mice exhibit 3,200-4,600 pmol of Abeta42 per g brain at age 6 months, with an excess of Abeta42 over Abeta40. High level production of the pathogenic Abeta42 form of Abeta peptide was associated with an early impairment in TgCRND8 mice in acquisition and learning reversal in the reference memory version of the Morris water maze, present by 3 months of age. Notably, learning impairment in young mice was offset by immunization against Abeta42 (Janus, C., Pearson, J., McLaurin, J., Mathews, P. M., Jiang, Y., Schmidt, S. D., Chishti, M. A., Horne, P., Heslin, D., French, J., Mount, H. T. J., Nixon, R. A., Mercken, M., Bergeron, C., Fraser, P. E., St. George-Hyslop, P., and Westaway, D. (2000) Nature 408, 979-982). Amyloid deposition in TgCRND8 mice was enhanced by the expression of presenilin 1 transgenes including familial Alzheimer's disease mutations; for mice also expressing a M146L+L286V presenilin 1 transgene, amyloid deposits were apparent by 1 month of age. The Tg mice described here suggest a potential to investigate aspects of Alzheimer's disease pathogenesis, prophylaxis, and therapy within short time frames.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".