Early diagnosis and treatment of Alzheimer’s disease by targeting toxic soluble Aβ oligomers
Bibliographic record
Abstract
Transient soluble oligomers of amyloid-β (Aβ) are toxic and accumulate early prior to insoluble plaque formation and cognitive impairment in Alzheimer’s disease (AD). Synthetic cyclic D,L-α-peptides (e.g., 1 ) self-assemble into cross β-sheet nanotubes, react with early Aβ species (1-3 mers), and inhibit Aβ aggregation and toxicity in stoichiometric concentrations, in vitro. Employing a semicarbazide as an aza-glycine residue with an extra hydrogen-bond donor to tune nanotube assembly and amyloid engagement, [azaGly 6 ]- 1 inhibited Aβ aggregation and toxicity at substoichiometric concentrations. High-resolution NMR studies revealed dynamic interactions between [azaGly 6 ]- 1 and Aβ42 residues F19 and F20, which are pivotal for early dimerization and aggregation. In an AD mouse model, brain positron emission tomography (PET) imaging using stable 64 Cu-labeled (aza)peptide tracers gave unprecedented early amyloid detection in 44-d presymptomatic animals. No tracer accumulation was detected in the cortex and hippocampus of 44-d-old 5xFAD mice; instead, intense PET signal was observed in the thalamus, from where Aβ oligomers may spread to other brain parts with disease progression. Compared with standard 11 C-labeled Pittsburgh compound-B ( 11 C-PIB), which binds specifically fibrillar Aβ plaques, 64 Cu-labeled (aza)peptide gave superior contrast and uptake in young mouse brain correlating with Aβ oligomer levels. Effectively crossing the blood–brain barrier (BBB), peptide 1 and [azaGly 6 ]- 1 reduced Aβ oligomer levels, prolonged lifespan of AD transgenic Caenorhabditis elegans , and abated memory and behavioral deficits in nematode and murine AD models. Cyclic (aza)peptides offer novel promise for early AD diagnosis and therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".