Building a better aspirin: gaseous solutions to a century‐old problem
Bibliographic record
Abstract
The gastrointestinal adverse effects of nonsteroidal anti-inflammatory drugs (NSAIDs) have been recognized since shortly after the introduction of aspirin to the marketplace over a century ago. However, the underlying pathogenesis of NSAID-induced gastropathy remains incompletely understood. Advances in understanding some of the factors that contribute to the mucosal injury have provided clues for the development of safer NSAIDs. The inhibitory effects of nitric oxide (NO) on NSAID-induced leukocyte adherence were exploited in the development of NO-releasing NSAIDs. As well as eliciting less gastrointestinal damage than conventional NSAIDs, these drugs do not elevate blood pressure and show anti-inflammatory effects, additional to those of the parent drugs. Modification of other drugs in a similar manner (i.e., NO-releasing derivatives) has similarly resulted in more effective drugs. More recently, hydrogen sulphide-releasing derivatives of NSAIDs and of other drugs, have been developed, based on the observed ability of H(2)S to reduce inflammation and pain in experimental models. H(2)S-releasing NSAIDs produce negligible gastric damage and exhibit enhanced anti-inflammatory potency as compared to the parent drugs. The NO-NSAIDs and H(2)S-releasing NSAIDs represent examples of new anti-inflammatory drugs with greatly reduced toxicity and improved therapeutic activity, both created through the concept of exploiting the beneficial effects of endogenous gaseous mediators.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".