Role of ICAM-1 in chronic hepatic allograft rejection in the rat
Bibliographic record
Abstract
The pathogenesis of hepatic allograft rejection remains unclear. We aimed to clarify the early role of intercellular adhesion molecule-1 (ICAM-1)-mediated cell recruitment in chronic hepatic rejection. Liver transplantation was performed from Lewis to Lewis rats (isograft controls) and from Lewis to Brown Norway rats (allograft rejection group). The allografted rats were treated with either ICAM-1 antisense oligonucleotides (10 mg. kg(-1). day(-1) x 6 days ip) or a control preparation (either ICAM-1 missense oligonucleotide or normal saline). Hepatic leukocyte recruitment in vivo was studied on day 6 by using intravital microscopy. Liver histology, biochemistry, and survival rates were also examined. Leukocyte adhesion in terminal hepatic venules was significantly increased in the rejection group compared with isograft controls. Antisense ICAM-1 in the allografted group effectively reduced leukocyte adhesion. Histology and liver chemistry were less deranged in the antisense-treated groups compared with control-treated allografted rats. In the allograft groups, survival was significantly prolonged in the antisense-treated rats (42.3 +/- 1.2 days) compared with the controls (25.2 +/- 2.7 days). These results showed that early leukocyte recruitment in the hepatic microvasculature of rejecting allografts is ICAM-1 dependent and suggest that impacting on early cell recruitment can significantly ameliorate chronic rejection.
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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) | 0.000 | 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".