Inhibition of islet amyloid polypeptide aggregation and associated cytotoxicity by nonsteroidal anti-inflammatory drugs
Bibliographic record
Abstract
Nonsteroidal anti-inflammatory drugs (NSAIDs) constitute an important pharmacotherapeutic class that, over the past decade, have expanded in application to a panoply of medical conditions. They have been tested for neurodegenerative diseases such as Alzheimer's to reduce inflammation and also in the attempt to abrogate amyloid deposition. However, the use of NSAIDs as aggregation inhibitors has not been extensively studied in pancreatic amyloid deposition. Pancreatic amyloidosis involves the misfolding of islet amyloid polypeptide (IAPP) and contributes to the progression of type-2 diabetes in humans and felines. To ascertain their antiamyloidogenic activity, several NSAIDs were tested using fluorometric thioflavin-T assays, circular dichroism, photo-induced cross-linking assays, and cell culture. Celecoxib, diclofenac, indomethacin, meloxicam, niflumic acid, nimesulide, phenylbutazone, piroxicam, sulindac, and tenoxicam reduced fibrillization at a molar ratio of 1:10. The circular dichroism spectra of diclofenac, piroxicam, and sulindac showed characteristic spectral signatures found in predominantly α-helical structures. The oligomerization of human IAPP was abrogated with diclofenac and sulindac at a molar ratio of 1:5. The cytotoxic effects of pre-incubated human IAPP on cultured INS-1 cells were noticeably reduced in the presence of diclofenac, meloxicam, phenylbutazone, sulindac, and tenoxicam at a molar ratio of 1:10. Our results demonstrate that NSAIDs can provide chemical scaffolds to generate new and promising antiamyloidogenic agents that can be used alone or as a coadjuvant therapy.
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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) | 0.000 | 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 | 0.001 |
| 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".