<i>In Vitro</i> Amyloid-β Binding and Inhibition of Amyloid-β Self-Association by Therapeutic Albumin
Bibliographic record
Abstract
BACKGROUND: A promising approach for treating Alzheimer's disease relies on the net efflux of the amyloid-β (Aβ) peptide from the brain to peripheral plasma, as a result of plasma Aβ clearance promoted by plasma removal and therapeutic albumin replacement. OBJECTIVE: To assess the binding of therapeutic albumin (Albutein, Grifols) to monomeric and aggregated Aβ according to methods previously tested on the interactions between Aβ and research-grade albumin. METHODS: Albumin integrity and the interactions with albumin stabilizers (octanoic acid and N-Ac-Trp) were assessed through one-dimensional (1D) 1H-NMR and saturation transfer difference (STD) NMR spectra. The interactions between monomeric Aβ1-40 and albumin were probed by 2D 1H-15 N HSQC spectra of labeled Aβ1-40. The formation of cross-β structured Aβ1-42 assemblies was monitored by ThT fluorescence. The interactions between self-assembled Aβ1-42 and albumin were probed by Trp fluorescence. RESULTS: NMR spectra indicated that both therapeutic and research-grade albumin are similarly well-folded proteins. No significant changes in either HSQC peak position or intensity were observed upon addition of albumin to 15N-labeled Aβ1-40, which rules out binding of albumin to monomeric Aβ with dissociation constant in the μM or lower range. When aggregated Aβ1-42 was added to albumin, quenching of Trp fluorescence was observed, which indicates albumin binding to Aβ1-42 aggregates. The relative potency of therapeutic albumin as an Aβ self-association inhibitor was in the same order of magnitude as research-grade albumin. CONCLUSIONS: Albutein inhibited Aβ self-association by selectively binding Aβ aggregates rather than monomers and by preventing further growth of the Aβ assemblies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".