Prostaglandins, NSAIDs, and Gastric Mucosal Protection: Why Doesn't the Stomach Digest Itself?
Bibliographic record
Abstract
Except in rare cases, the stomach can withstand exposure to highly concentrated hydrochloric acid, refluxed bile salts, alcohol, and foodstuffs with a wide range of temperatures and osmolarity. This is attributed to a number of physiological responses by the mucosal lining to potentially harmful luminal agents, and to an ability to rapidly repair damage when it does occur. Since the discovery in 1971 that prostaglandin synthesis could be blocked by aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs), there has been great interest in the contribution of prostaglandins to gastric mucosal defense. Prostaglandins modulate virtually every aspect of mucosal defense, and the importance of this contribution is evident by the increased susceptibility of the stomach to injury following ingestion of an NSAID. With chronic ingestion of these drugs, the development of ulcers in the stomach is a significant clinical concern. Research over the past two decades has helped to identify some of the key events triggered by NSAIDs that contribute to ulcer formation and/or impair ulcer healing. Recent research has also highlighted the fact that the protective functions of prostaglandins in the stomach can be carried out by other mediators, in particular the gaseous mediators nitric oxide and hydrogen sulfide. Better understanding of the mechanisms through which the stomach is able to resist injury in the presence of luminal irritants is helping to drive the development of safer anti-inflammatory drugs, and therapies to accelerate and improve the quality of ulcer healing.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".