[P4–128]: SMALL INTERFERENCE PEPTIDES AS BLOCKERS OF BETA‐AMYLOID AGGREGATION
Bibliographic record
Abstract
In Alzheimer's disease (AD) pathogenesis, the conversion of the beta-amyloid (Aβ) peptide from its soluble monomeric form into various aggregated some has been recognized as one of the key step. Aβ peptides form toxic assemblies ranging in size from small oligomers (2–8 amino acids) to large fibrils. Several studies have shown that Aβ soluble oligomers play a major role in disease pathogenesis. Although it's still unclear how these amyloidogenic protein misfold and form toxic assemblies, inhibiting Aβ self-oligomerization could provide an approach to treating the underlying cause of AD. Here, we designed potential peptide-based aggregation inhibitors, using an overlapping peptides array technology, to effectively interfere with Aβ self-association. Using High Performance Liquid Chromatography (HPLC) to analyze the composition of Aβ oligomers. ThT and ANS assays, by detecting the β-sheet conformation and hydrophobic structure in the protein, also confirmed the effectiveness of the small interference peptides inhibiting Aβ self-association. We found that our small interference peptides could effectively block the small oligomers formation. ThT and ANS assays, by detecting the β-sheet conformation and hydrophobic structure in the protein, also confirmed the effectiveness of the small interference peptides inhibiting Aβ self-association. Our small interference peptides, which consist of short complimentary segments of the beta-amyloid protein, can be useful for identifying the aggregation-prone regions of the amyloid protein for drug discovery and development of potential therapeutic reagents.
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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) | 0.000 | 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) | 0.002 | 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".