Role of endogenous nitric oxide synthase inhibitor in gastric mucosal injury
Bibliographic record
Abstract
To explore the role of the endogenous nitric oxide synthase (NOS) inhibitor asymmetric dimethylarginine (ADMA) in gastric mucosal injury, 3 models of gastric mucosal injury induced by ethanol, indomethacin, or cold stress were used in rats. The cultured human gastric mucosal epithelial cell line GES-1 infected by Helicobacter pylori (Hp) was selected to mimic human gastric mucosal injury. Gastric mucosal ulcer index (UI), levels of ADMA and NO, and activity of dimethylarginine dimethylaminohydrolase (DDAH) were determined in the mucosal injury models; in Hp-infected or ADMA-treated GES-1 cells, levels of ADMA, NO, and TNF-alpha and activity of DDAH were measured. The results showed that UI and levels of ADMA were markedly increased and accompanied by significantly decreased DDAH activity in the mucosal injury models. Incubation of GES-1 cells with Hp increased levels of TNF-alpha and ADMA and decreased activity of DDAH. Administration of ADMA also increased levels of TNF-alpha. The results suggest that ADMA plays an important role in facilitating gastric mucosal injury, an effect which is associated with inhibiting NO synthesis and inducing inflammatory reaction.
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 | 0.000 | 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".