Stress-mediated aggregation of disease-associated proteins in amyloid bodies
Bibliographic record
Abstract
The formation of protein aggregates is a hallmark of many neurodegenerative diseases and systemic amyloidoses. These disorders are associated with the fibrillation of a variety of proteins/peptides, which ultimately leads to cell toxicity and tissue damage. Understanding how amyloid aggregation occurs and developing compounds that impair this process is a major challenge in the health science community. Here, we demonstrate that pathogenic proteins associated with Alzheimer's disease, diabetes, AL/AA amyloidosis, and amyotrophic lateral sclerosis can aggregate within stress-inducible physiological amyloid-based structures, termed amyloid bodies (A-bodies). Using a limited collection of small molecule inhibitors, we found that diclofenac could repress amyloid aggregation of the β-amyloid (1-42) in a cellular setting, despite having no effect in the classic Thioflavin T (ThT) in vitro fibrillation assay. Mapping the mechanism of the diclofenac-mediated repression indicated that dysregulation of cyclooxygenases and the prostaglandin synthesis pathway was potentially responsible for this effect. Together, this work suggests that the A-body machinery may be linked to a subset of pathological amyloidosis, and highlights the utility of this model system in the identification of new small molecules that could treat these debilitating diseases.
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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".