Serum β-Amyloid Peptide Levels Spike in the Early Stage of Alzheimer- Like Plaque Pathology in an APP/PS1 Double Transgenic Mouse Model
Bibliographic record
Abstract
Serum levels of β-amyloid (Aβ) peptides may represent an early biomarker in the diagnosis of Alzheimer's disease (AD). In the present study, we investigated the temporal kinetic changes in the levels of serum Aβ 1-42 and 40 in an amyloid precursor protein (APP)/presenilin (PS)1 double transgenic mouse model of AD. Serum Aβ peptide levels in 2-, 3-, 6-, 9- and 18-month old, and liver Aβ 1-40 level in 6-month old mice were measured using enzyme-linked immunosorbent assay (ELISA) kits. Results revealed that serum Aβ levels peaked in 3-month old transgenic mice, and the Aβ level in non-transgenic and transgenic mice is comparable in liver. Compared to the 6-month old transgenic mice, Congo red staining showed that the 3-month old transgenic mice had minimum brain Aβ plaques, corresponding to the early stage of Alzheimer-like plaque pathology, and confocal microscope images showed that the deposition of Aβ in their cerebral vessels was minimal. Furthermore, results of the water maze test, showed that memory was normal for the 3- month old transgenic mice when compared to age-matched non-transgenic mice. These results suggest that serum Aβ peptide levels may be peaked during the early stage of AD. Monitoring serum Aβ peptide levels in the potential AD population may provide an early diagnosis of AD prior to the appearance of clinical symptoms.
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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) | 0.002 | 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".