<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 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 | 0.001 |
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".