Oral Immunization with Soybean Storage Protein Containing Amyloid-β 4–10 Prevents Spatial Learning Decline
Bibliographic record
Abstract
Amyloid-β (Aβ) plays a central role in the pathogenesis of Alzheimer's disease (AD). Because AD pathologies begin two decades before the onset of dementia, prevention of Aβ amyloidosis has been proposed as a mean to block the pathological cascade. Here, we generate a transgenic plant-based vaccine, a soybean storage protein containing Aβ4-10, named Aβ+, for oral Aβ immunization. One mg of Aβ+ or control protein (Aβ-) was administered to TgCRND8 mice once a week from 9 weeks up to 58 weeks. Aβ+ immunization raised both anti-Aβ antibodies and cellular immune responses. Spatial learning decline was prevented in the Aβ+ immunized group in an extended reference memory version of Morris water maze test from 21 to 57 weeks. In Tris-buffered saline (TBS), sodium dodecyl sulfate (SDS), and formic acid (FA) serial extractions, all sets of Aβ species from Aβ monomer, low to high molecular weight Aβ oligomers, and Aβ smears had different solubility in TgCRND8 brains. Aβ oligomers decreased in TBS fractions, corresponding to an increase in high molecular weight Aβ oligomers in SDS extracts and Aβ smears in FA fraction of the Aβ+ treated group. There was significant inhibition of histological Aβ burden, especially in diffuse plaques, and suppression of microglial inflammation. Processing of amyloid-β protein precursor was not different between Aβ+ and Aβ- groups. No evidence of amyloid-related inflammatory angiopathy was observed. Thus, Aβ+ oral immunization could be a promising, cheap, and long-term safe disease-modifying therapy to prevent the pathological process 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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".