Effects of nitric oxide-releasing nonsteroidal anti-inflammatory drugs (NONO-NSAIDs) on melanoma cell adhesion
Bibliographic record
Abstract
A new class of nitric oxide (NO•)-releasing nonsteroidal anti-inflammatory drugs (NONO-NSAIDs) were developed in recent years and have shown promising potential as NSAID substitutes due to their gentle nature on cardiovascular and gastrointestinal systems. Since nitric oxide plays a role in regulation of cell adhesion, we assessed the potential use of NONO-NSAIDs as anti-metastasis drugs. In this regard, we compared the effects of NONO-aspirin and a novel NONO-naproxen to those exerted by their respective parent NSAIDs on avidities of human melanoma M624 cells. Both NONO-NSAIDs, but not the corresponding parent NSAIDs, reduced M624 adhesion on vascular cellular adhesion molecule-1 (VCAM-1) by 20-30% and fibronectin by 25-44% under fluid flow conditions and static conditions, respectively. Only NONO-naproxen reduced (~56%) the activity of β1 integrin, which binds to α4 integrin to form very late antigen-4 (VLA-4), the ligand of VCAM-1. These results indicate that the diazeniumdiolate (NO•)-donor moiety is critical for reducing the adhesion between VLA-4 and its ligands, while the NSAID moiety can impact the regulation mechanism of melanoma cell adhesion.
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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) | 0.004 | 0.001 |
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".