Effect of Varying Concentrations of Docosahexaenoic Acid on Amyloid Beta (1–42) Aggregation: An Atomic Force Microscopy Study
Bibliographic record
Abstract
Healthcare has advanced significantly, bringing with it longer life expectancies and a growing population of elders who suffer from dementia, specifically Alzheimer’s disease (AD). The amyloid beta (Aβ) peptide has been implicated in the cause of AD, where the peptides undergo a conformational change and form neurotoxic amyloid oligomers which cause neuronal cell death. While AD has no cure, preventative measures are being designed to either slow down or stop the progression of this neurodegenerative disease. One of these measures involves dietary supplements with polyunsaturated fatty acids such as docosahexaenoic acid (DHA). This omega-3 fatty acid is a key component of brain development and has been suggested to reduce the progression of cognitive decline. However, different studies have yielded different results as to whether DHA has positive, negative, or no effects on Aβ fibril formation. We believe that these discrepancies can be explained with varying concentrations of DHA. Here, we test the inhibitory effect of different concentrations of DHA on amyloid fibril formation using atomic force microscopy. Our results show that DHA has a strong inhibitory effect on Aβ1–42 fibril formation at lower concentrations (50% reduction in fibril length) than higher concentrations above its critical micelle concentration (70% increase in fibril length and three times the length of those at lower concentrations). We provide evidence that various concentrations of DHA can play a role in the inhibitory effects of amyloid fibril formation in vitro and help explain the discrepancies observed in previous studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".