Copper-mediated β-amyloid toxicity and its chelation therapy in Alzheimer's disease
Bibliographic record
Abstract
The link between bio-metals, Alzheimer's disease (AD), and its associated protein, amyloid-β (Aβ), is very complex and one of the most studied aspects currently. Alzheimer's disease, a progressive neurodegenerative disease, is proposed to occurs due to the misfolding and aggregation of Aβ. Dyshomeostasis of metal ions and their interaction with Aβ has largely been implicated in AD. Copper plays a crucial role in amyloid-β toxicity, and AD development potentially occurs through direct interaction with the copper-binding motif of APP and different amino acid residues of Aβ. Previous reports suggest that high levels of copper accumulation in the AD brain result in modulation of toxic Aβ peptide levels, implicating the role of copper in the pathophysiology of AD. In this review, we explore the possible mode of copper ion interaction with Aβ, which accelerates the kinetics of fibril formation and promote amyloid-β mediated cell toxicity in Alzheimer's disease and the potential use of various copper chelators in the prevention of copper-mediated Aβ toxicity. KEYWORDS: Short Twitter Statement: Authors explore copper ion interaction w/ Aβ and kinetics of fibril formation in promoting amyloid-β mediated cell toxicity in Alzheimer's disease and the potential use of copper chelators in the prevention of copper-mediated Aβ toxicity. SHORT TWITTER STATEMENT: Authors explore copper ion interaction w/Aβ and kinetics of fibril formation in promoting amyloid-β mediated cell toxicity in Alzheimer's disease and the potential use of copper chelators in the prevention of copper-mediated Aβ toxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".