Accelerated gastric ulcer healing in thyroxine-treated rats: roles of gastric acid, mucus, and inflammatory response
Bibliographic record
Abstract
The roles of gastric acid, mucus, and inflammation on the pro-ulcer-healing effect of thyroid hormone were investigated. Male Wistar rats were randomly divided into four groups: control, thyroidectomised, thyroidectomised with thyroxine treatment (100 μg·kg–1·day–1), and sham-operated animals treated with thyroxine. Thirty-five days after thyroidectomy, sham surgery, or thyroxine treatment, an ulcer was experimentally induced. Healing was assessed 3, 7, and 10 days post-ulceration by measurement of the ulcer area, gastric mucus and acid secretion, and neutrophil lymphocyte ratio (NLR) as an index of inflammation. By day 10, the ulcer area had decreased in all groups. Recovery was significantly greater (P < 0.05) in thyroxine-treated rats (78.5% ± 1.6% reduction in ulcer area) than in controls (72.3% ± 1.2% reduction) or thyroidectomised rats (63.3% ± 1.9% reduction). Thyroxine-treated animals also had the highest reduction in NLR (65.0% ± 2.5%). Mucus secretion was significantly lower (P < 0.05) in thyroidectomised rats by days 3 and 7. Furthermore, by day 10, the concentration of basal acid decreased by 77.4% ± 2.6% in thyroxine-treated, 65.0% ± 0.0% in control, and 51.5% ± 3.3% in thyroidectomised rats. We conclude that thyroxine accelerates gastric ulcer healing by altering mucus and acid secretion and reducing NLR.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".