Molecular mechanisms of leukocyte recruitment in postischemic liver microcirculation
Bibliographic record
Abstract
Evidence shows that leukocyte recruitment into inflamed liver sinusoids does not require selectins, with one notable exception: ischemia-reperfusion (I/R). We used intravital microscopy to directly visualize the liver microcirculation during I/R and localized endotoxemia (liver superfused with lipopolysaccharide). General anti-selectin therapy (fucoidan) or anti-adhesion therapy with an antithrombin inhibitor (hirudin) was also used. Many neutrophils rolled and adhered in postsinusoidal vessels and sequestered in the sinusoids during I/R and local endotoxin superfusion. Although fucoidan blocked rolling in both forms of inflammation, leukocyte recruitment into sinusoids was only blocked in I/R. Adhesion was also inhibited in postischemic sinusoids with a second anti-adhesive agent (hirudin). Because liver I/R inevitably induces ischemia upstream in the intestine, anti-selectin therapy may prevent intestinal injury, which could prevent downstream liver inflammation. To test this hypothesis, we completely removed the intestine and rerouted blood flow from the superior mesenteric artery to the superior mesenteric vein. I/R was induced in the liver microcirculation, and many leukocytes rolled and adhered in postsinusoidal venules and adhered in sinusoids. Although fucoidan significantly reduced the rolling in postsinusoidal vessels, adhesion persisted in the sinusoids. Our data suggest that anti-adhesion therapy is effective in liver I/R in the sinusoids and postsinusoidal venules, perhaps in part due to its beneficial effect on the intestine.
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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".