Modulation of the Aβ peptide aggregation pathway by KP1019 limits Aβ-associated neurotoxicity
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that is increasing worldwide due to increased life expectancy. AD is characterized by two pathological hallmarks in the brain: amyloid-β (Aβ) plaque deposits and neurofibrillary tangles. A focus of AD research has concentrated on either inhibiting Aβ peptide aggregation that leads to plaque formation or breaking down pre-formed Aβ peptide aggregates. An alternative approach is to modulate the Aβ aggregation profile by facilitating the formation of Aβ species that are off-pathway and non-toxic. Herein, we report the re-purposing of the widely studied Ru(iii) anti-cancer complex KP1019, towards regulating the aggregation profile of the Aβ peptide. Using electron paramagnetic resonance (EPR) spectroscopy, we conclude that KP1019 binds to histidine residues, located at the N-terminus of the peptide, in a rapid and robust fashion. Native gels and transmission electron microscopy (TEM) analyses have provided insight into the species and structures that are generated by KP1019-Aβ interactions. Finally, incubation in an in vitro human neuronal cell model has demonstrated that the formation of KP1019-Aβ species rescues cell viability from Aβ-associated neurotoxicity. Modulation of the Aβ aggregation pathway via covalent interactions with small molecules is thus a promising AD therapeutic 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".