INTERACTION OF CD44 AND HYALURONAN IS THE DOMINANT MECHANISM FOR NEUTROPHIL ADHESION IN INFLAMED LIVER SINUSOIDS
Bibliographic record
Abstract
Background: Previous studies have been unable to identify adhesion molecules that mediate neutrophil recruitment within the liver sinusoids. We hypothesise that involved adhesion molecules may represent novel therapeutic targets for combating pathologic liver inflammation. Methods: Candidate adhesion molecules were identified using a novel in vivo biopanning approach (dual radiolabelled antibody technique) to quantify endothelial expression levels within the liver compared to other organs. Spinning disk intravital microscopy demonstrated the localization of adhesion molecule expression withinthe liver microvasculature. Using knockout mice, bone marrow chimeric mice, and blocking antibodies, candidate adhesion molecules were systematically investigated for a role in neutrophil recruitment in the liver sinusoids of endotoxemic mice using intravital microscopy and in vitro flow chamber assays. Results: Hyaluronan was identified as disproportionately expressed in the liver versus other organs, and hyaluronan expression was restricted to liver sinusoids. Blocking CD44-hyaluronan interactions reduced neutrophil adhesion in the sinusoids of endotoxemic mice, but had no effect on neutrophil rolling or adhesion in post-sinusoidal venules. Neutrophil but not endothelial CD44 was required for adhesionin sinusoids. Surprisingly, neutrophil CD44 avidity for hyaluronan was not increased in endotoxemia. Instead, activation of CD44-hyaluronan engagement was the result of qualitative modification of hyaluronan by a dramatic induction of serum-derived hyaluronan-associated protein (SHAP) in sinusoids in response to lipopolysaccharide. Lipopolysaccharide-induced hepatic injury was significantly reduced by inhibiting CD44-hyaluronan interactions. Therapeutic administration of anti-CD44 antibody to endotoxemic mice rapidly detached adherent neutrophils and improved sinusoidal perfusion. Conclusion: These findings reveal CD44 as a potential therapeutic target in systemic inflammatory responses involving liver.
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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".