Activation of TLR4 on endothelium alone initiates neutrophil adhesion within the liver microcirulation during endotoxemia (102.22)
Bibliographic record
Abstract
Abstract During sepsis and endotoxemia, neutrophils are recruited to the liver where they cause tissue damage and vascular dysfunction. We have previously reported that neutrophils adhere within sinusoids of the endotoxemic liver via interactions between neutrophil CD44 and endothelial hyaluronan. In this study, we aimed to characterize the cellular sentinels that detect circulating LPS via TLR4 and the pathways leading to initiation of neutrophil adhesion via CD44-hyaluronan interactions. Intravital microscopy of bone marrow chimeric mice revealed that TLR4 expression by non-bone marrow derived cells was required for neutrophil recruitment into the liver during endotoxemia (1 mg/kg LPS, i.v.). Furthermore, LPS-induced neutrophil adhesion in sinusoids was equivalent between wild-type mice and transgenic mice that express TLR4 only on endothelium (tlr4-/-Tie2tlr4, named endoTLR4), but surprisingly, sinusoidal occlusion (vascular damage) was attenuated in endoTLR4 mice. Intravital immunofluorescence imaging demonstrated that stimulation of endothelial TLR4 induced the deposition of serum-derived hyaluronan associated protein (SHAP) within liver sinsusoids, which was required for activation of endothelial hyaluronan to bind neutrophil CD44. These data reveal that endothelial cells are the key sentinels that recognize circulating LPS and initiate neutrophil adhesion in the liver during endotoxemia, but the development of liver pathology requires TLR4 activation on additional cell types.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".