Direct evidence for activated CD8+ T cell transmigration across portal vein endothelial cells in liver graft rejection
Bibliographic record
Abstract
BACKGROUND: Lymphocyte recruitment into the portal tract is crucial not only for homeostatic immune surveillance but also for many liver diseases. However, the exact route of entry for lymphocytes into portal tract is still obscure. We investigated this question using a rat hepatic allograft rejection model. METHODS: A migration route was analyzed by immunohistological methods including a recently developed scanning electron microscopy method. Transmigration-associated molecules such as selectins, integrins, and chemokines and their receptors expressed by hepatic vessels and recruited T-cells were analyzed by immunohistochemistry and flow cytometry. RESULTS: The immunoelectron microscopic analysis clearly showed CD8β(+) cells passing through the portal vein (PV) endothelia. Furthermore, the migrating pathway seemed to pass through the endothelial cell body. Local vascular cell adhesion molecule-1 (VCAM-1) expression was induced in PV endothelial cells from day 2 after liver transplantation. Although intercellular adhesion molecule-1 (ICAM-1) expression was also upregulated, it was restricted to sinusoidal endothelia. Recipient T-cells in the graft perfusate were CD25(+)CD44(+)ICAM-1(+)CXCR3(+)CCR5(-) and upregulated α4β1 or αLβ2 integrins. Immunohistochemistry showed the expression of CXCL10 in donor MHCII(high) cells in the portal tract as well as endothelial walls of PV. CONCLUSIONS: We show for the first time direct evidence of T-cell transmigration across PV endothelial cells during hepatic allograft rejection. Interactions between VCAM-1 on endothelia and α4β1 integrin on recipient effector T-cells putatively play critical roles in adhesion and transmigration through endothelia. A chemokine axis of CXCL10 and CXCR3 also may be involved.
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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) | 0.002 | 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".