The tripeptide analog feG ameliorates severity of acute pancreatitis in a caerulein mouse model
Bibliographic record
Abstract
Acute pancreatitis (AP) is associated with significant morbidity and mortality; however, there is no specific treatment for this disease. A novel salivary tripeptide analog, feG, reduces inflammation in several different animal models of inflammation. The aims of this study were to determine whether feG reduced the severity of AP and modifies the expression of pancreatic ICAM-1 mRNA during AP in a mouse model. AP was induced in mice by hourly (x12) intraperitoneal injections of caerulein. A single dose of feG (100 microg/kg) was coadministered with caerulein either at time 0 h (prophylactic) or 3 h after AP induction (therapeutic). Plasma amylase and pancreatic MPO activities and pancreatic ICAM-1 mRNA expression (by RT-PCR) were measured. Pancreatic sections were histologically assessed for abnormal acinar cells and interstitial space. AP induction produced a sevenfold increase in plasma amylase, a tenfold increase in pancreatic MPO activity, and a threefold increase in interstitial space, and 90% of the acinar cells were abnormal. Prophylactic treatment with feG reduced the AP-induced plasma amylase activity by 45%, pancreatic MPO by 80%, the proportion of abnormal acinar cells by 30%, and interstitial space by 40%. Therapeutic treatment with feG significantly reduced the AP-induced abnormal acinar cells by 10% and the interstitial space by 20%. Pancreatic ICAM-1 mRNA expression was upregulated in AP and was reduced by 50% with prophylactic and therapeutic treatment with feG. We conclude that feG ameliorates experimental AP acting at least in part by modulating ICAM-1 expression in the pancreas.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".