Physiological amyloid-beta clearance in the periphery and its therapeutic potential for Alzheimer’s disease
Bibliographic record
Abstract
Amyloid-beta (Aβ) plays a pivotal role in the pathogenesis of Alzheimer's disease (AD). The physiological capacity of peripheral tissues and organs in clearing brain-derived Aβ and its therapeutic potential for AD remains largely unknown. Here, we measured blood Aβ levels in different locations of the circulation in humans and mice, and used a parabiosis model to investigate the effect of peripheral Aβ catabolism on AD pathogenesis. We found that blood Aβ levels in the inferior/posterior vena cava were lower than that in the superior vena cava in both humans and mice. In addition, injected (125)I labeled Aβ40 was located mostly in the liver, kidney, gastrointestinal tract, and skin but very little in the brain; suggesting that Aβ derived from the brain can be cleared in the periphery. Parabiosis before and after Aβ deposition in the brain significantly reduced brain Aβ burden without alterations in the expression of amyloid precursor protein, Aβ generating and degrading enzymes, Aβ transport receptors, and AD-type pathologies including hyperphosphorylated tau, neuroinflammation, as well as neuronal degeneration and loss in the brains of parabiotic AD mice. Our study revealed that the peripheral system is potent in clearing brain Aβ and preventing AD pathogenesis. The present work suggests that peripheral Aβ clearance is a valid therapeutic approach for AD, and implies that deficits in the Aβ clearance in the periphery might also contribute to AD pathogenesis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".