Reduction of Amyloid-β Plasma Levels by Hemodialysis: An Anti-Amyloid Treatment Strategy?
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment in hemodialysis patients is common, but the underlying pathogenesis remains unclear. Alzheimer's disease is the most common cause of dementia in the general elderly population. Histopathological hallmarks are, among others, senile plaques, which consist of amyloid-β (Aβ). OBJECTIVE: To measure plasma levels of Aβ42 and Aβ40 during hemodialysis and to examine potential associations with cognitive performance in cognitively impaired hemodialysis patients. METHODS: Plasma samples of 26 hemodialysis patients were collected shortly before, after 50% of dialysis time, and at the end of a dialysis session. Aβ42 and Aβ40 levels were measured by a high-sensitivity ELISA for human amyloid-β. Cognition was tested under standardized conditions using the Montreal Cognitive Assessment (MoCA) as proposed previously. RESULTS: Clearance rates of both peptides during one dialysis session were 22% and 35% for Aβ42 and Aβ40, respectively. Aβ42 but not Aβ40 baseline levels were significantly associated with MoCA test results (r = 0.654, p = 0.001). CONCLUSION: In cognitively impaired hemodialysis patients plasma Aβ42 levels were associated with cognitive performance and both Aβ42 and Aβ40 plasma levels could be effectively reduced by dialysis. By inducing peripheral Aβ sink, hemodialysis may be considered as an anti-amyloid treatment strategy.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".