Influence of the Physiochemical Properties of Superparamagnetic Iron Oxide Nanoparticles on Amyloid β Protein Fibrillation in Solution
Bibliographic record
Abstract
Superparamagnetic iron oxide nanoparticles (SPIONs) are recognized as promising nanodiagnostic materials due to their biocompatibility, unique magnetic properties, and their application as multimodal contrast agents. As coated SPIONs have potential use in the diagnosis and treatment of various brain diseases such as Alzheimer's, a comprehensive understanding of their interactions with Aβ and other amyloidogenic proteins is essential prior to their clinical application. Here we demonstrate the effect of thickness and surface charge of the coating layer of SPIONs on the kinetics of fibrillation of Aβ in aqueous solution. A size and surface area dependent "dual" effect on Aβ fibrillation was observed. While lower concentrations of SPIONs inhibited fibrillation, higher concentrations increased the rate of Aβ fibrillation. With respect to coating charge, it is evident that the positively charged SPIONs are capable of promoting fibrillation at significantly lower particle concentrations compared with negatively charged or uncharged SPIONs. This suggests that in addition to the presence of particles, which affect the concentration of monomeric protein in solution (and thereby the nucleation time), there are also effects of binding on the protein conformation.
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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 | 0.000 | 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".