The adhesion molecules CD44 and hyaluronan, not physical trapping, are responsible for neutrophil sequestration in inflamed liver sinusoids (97.5)
Bibliographic record
Abstract
Abstract Adhesion molecules important for neutrophil recruitment in many organs during endotoxemia are not involved in recruitment into the sinusoids of the liver. Using a whole body dual radiolabel adhesion molecules screen, we observed that hyaluronan (HA), a CD44 ligand, is disproportionately expressed in the liver (versus other organs) under both basal and endotoxemic conditions. Using intravital microscopy, we observed that blocking CD44-HA interactions reduced neutrophil adhesion in sinusoids but had no effect on rolling or adhesion in post-sinusoidal venules (mediated by α4 and β2 integrins). Inhibition of CD44-HA interactions, but not integrins, reduced the entry of neutrophils into the liver parenchyma and significantly improved LPS-induced hepatic injury. Anti-CD44 antibody rapidly detached adhering neutrophils in sinusoids and improved sinusoidal perfusion in endotoxemic mice, revealing CD44 as a potential therapeutic target in LPS-associated liver disease. In conclusion, the identification of CD44-HA as partners for neutrophil adhesion in sinusoids, disproves the hypothesis that physical trapping is entirely responsible for sequestration of neutrophils in sinusoids. This work was funded by Alberta Heritage Foundation for Medical Research (AHFMR), and the Canadian Institutes of Health Research (CIHR).
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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".