Intracellular Amyloid-β in the Normal Rat Brain and Human Subjects and Its relevance for Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Amyloid-β (Aβ) is a normal product of neuronal activity, including that of the aggregation-prone Aβ42 variant that is thought to cause Alzheimer's disease (AD). Much knowledge about AD comes from studies of transgenic rodents expressing mutated human amyloid-β protein precursor (AβPP) to increase Aβ production or the Aβ42/40 ratio. Yet, little is known about the normal expression of Aβ42 in rodent brains. OBJECTIVE: To characterize the brain-wide expression of Aβ42 throughout the life span of outbred Wistar rats, and to relate these findings to brains of human subjects without neurological disease. METHODS: Aβ42 immunolabeling of 12 Wistar rat brains (3-18 months of age) and brain sections from six human subjects aged 20-88 years. RESULTS: In healthy Wistar rats, we find intracellular Aβ42 (iAβ42) in neurons throughout the brain at all ages, but levels vary greatly between brain regions. The highest levels are in neurons of entorhinal cortex layer II, alongside hippocampal neurons at the CA1/subiculum border. Concerning entorhinal cortex layer II, we find similarly high levels of iAβ42 in the human subjects. CONCLUSION: Expression of iAβ42 in healthy Wistar rats predominates in the same structures where iAβ accumulates and Aβ plaques initially form in the much used, Wistar based McGill-R-Thy1-APP rat model for AD. The difference between wild-type Wistar rats and these AD model rats, with respect to Aβ42, is therefore quantitative rather that qualitative. This, taken together with our human results, indicate that the McGill rat model in fact models the underlying wild-type neuronal population-specific vulnerability to Aβ42 accumulation.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".