Rapid Cytotoxicity Screening Platform for Amyloid Inhibitors Using a Membrane-Potential Sensitive Fluorescent Probe
Bibliographic record
Abstract
The growing interest in membrane interactions of amyloidogenic proteins indicates that lipid binding and the regulation of membrane potential are critical to the onset and progression of neurodegenerative diseases such as Parkinson's (PD), Alzheimer's (AD), and prion diseases. Advancing the understanding of this field requires the application of varied biophysical and biological techniques designed to probe the characteristics and underlying mechanisms of membrane-peptide interactions. Therefore, the development of a rapid cytotoxicity evaluation system using a membrane potential-sensitive bis-oxonol fluorescent dye, DiBAC4(3) is reported here. The exposure of C-terminal truncated α-synuclein 119 (α-Syn119) and amyloid-β(1-42) (Aβ(1-42)) to U2-OS cell cultures resulted in an immediate, significant, and concentration-dependent increase in fluorescence response of DiBAC4(3). This response was strongly correlated with the cytotoxicity of α-Syn119 and Aβ(1-42) as determined by conventional CC8 and ATP assays. Furthermore, the capacity of well-defined polyphenolic antioxidants (i.e., pyrroloquinoline quinone (PQQ), baicalein, (-)-epigallocatechin-3-gallate (EGCG), and myricetin) to mitigate amyloid-induced cytotoxicity was evaluated using the developed biosensing system. We envisage that this work would accelerate the development of a rapid and cost-effective high-throughput screening platform in drug discovery for AD and PD.
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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 | 0.001 | 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".