A Bifunctional Anti-Amyloid Blocks Oxidative Stress and the Accumulation of Intraneuronal Amyloid-Beta
Bibliographic record
Abstract
There is growing recognition regarding the role of intracellular amyloid beta (Aβ) in the Alzheimer's disease process, which has been linked with aberrant signaling and the disruption of protein degradation mechanisms. Most notably, intraneuronal Aβ likely underlies the oxidative stress and mitochondrial dysfunction that have been identified as key elements of disease progression. In this study, we employed fluorescence imaging to explore the ability of a bifunctional small molecule to reduce aggregates of intracellular Aβ and attenuate oxidative stress. Structurally, this small molecule is comprised of a nitroxide spin label linked to an amyloidophilic fluorene and is known as spin-labeled fluorene (SLF). The effect of the SLF on intracellular Aβ accumulation and oxidative stress was measured in MC65 cells, a human neuronal cell line with inducible expression of the amyloid precursor protein and in the N2a neuronal cell line treated with exogenous Aβ. Super-resolution microscopy imaging showed SLF decreases the accumulation of intracellular Aβ. Confocal microscopy imaging of MC65 cells treated with a reactive oxygen species (ROS)-sensitive dye demonstrated SLF significantly reduces the intracellular Aβ-induced ROS signal. In order to determine the contributions of the separate SLF moieties to these protective activities, experiments were also carried out on cells with nitroxides lacking the Aβ targeting domain or fluorene derivatives lacking the nitroxide functionality. The findings support a synergistic effect of SLF in counteracting both the conformational toxicity of both endogenous and exogenous Aβ, its promotion of ROS, and Aβ metabolism. Furthermore, these studies demonstrate an intimate link between ROS production and Aβ oligomer formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".