Interaction of CD44 and hyaluronan is the dominant mechanism for neutrophil sequestration in inflamed liver sinusoids
Bibliographic record
Abstract
Adhesion molecules known to be important for neutrophil recruitment in many other organs are not involved in recruitment of neutrophils into the sinusoids of the liver. The prevailing view is that neutrophils become physically trapped in inflamed liver sinusoids. In this study, we used a biopanning approach to identify hyaluronan (HA) as disproportionately expressed in the liver versus other organs under both basal and inflammatory conditions. Spinning disk intravital microscopy revealed that constitutive HA expression was restricted to liver sinusoids. Blocking CD44-HA interactions reduced neutrophil adhesion in the sinusoids of endotoxemic mice, with no effect on rolling or adhesion in postsinusoidal venules. Neutrophil but not endothelial CD44 was required for adhesion in sinusoids, yet neutrophil CD44 avidity for HA did not increase significantly in endotoxemia. Instead, activation of CD44-HA engagement via qualitative modification of HA was demonstrated by a dramatic induction of serum-derived HA-associated protein in sinusoids in response to lipopolysaccharide (LPS). LPS-induced hepatic injury was significantly reduced by blocking CD44-HA interactions. Administration of anti-CD44 antibody 4 hours after LPS rapidly detached adherent neutrophils in sinusoids and improved sinusoidal perfusion in endotoxemic mice, revealing CD44 as a potential therapeutic target in systemic inflammatory responses involving the 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".