Inhibition of Amyloid β-Induced Lipid Membrane Permeation and Amyloid β Aggregation by K162
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by progressive neurodegeneration associated with amyloid β (Aβ) peptide aggregation. The aggregation of Aβ monomers (AβMs) leads to the formation of Aβ oligomers (AβOs), the neurotoxic Aβ form, capable of permeating the cell membrane. Here, we investigated the effect of a fluorene-based active drug candidate, named K162, on both Aβ aggregation and AβO toxicity toward the bilayer lipid membrane (BLM). Electrochemical impedance spectroscopy (EIS), atomic force microscopy (AFM), and molecular dynamics (MD) were employed to show that K162 inhibits AβOs-induced BLM permeation, thus preserving BLM integrity. In the presence of K162, only shallow defects on the BLM surface were formed. Apparently, K162 modifies Aβ aggregation by bypassing the formation of toxic AβOs, and only nontoxic AβMs, dimers (AβDs), and fibrils (AβFs) are produced. Unlike other Aβ toxicity inhibitors, K162 preserves neurologically beneficial AβMs. This unique K162 inhibition mechanism provides an alternative AD therapeutic strategy that could be explored in the future.
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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 | 0.000 |
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".