Systemic inflammation increases cancer cell adhesion to hepatic sinusoids by neutrophil mediated mechanisms
Bibliographic record
Abstract
Interactions between endothelial selectins and selectin ligands expressed on tumor cells have been implicated in the binding of circulating metastatic cancer cells to the vascular endothelium during extravasation. Moreover, there is mounting evidence that inflammatory environments can accelerate the progression of metastasis by neutrophil mediated mechanisms. In this study, a physiologically relevant in vivo model of early metastasis coupled with intravital microscopy was used to visualize the trafficking of tumor cells within the liver vasculature in real time. Using GFP-labeled Lewis lung carcinoma subline H-59 cells, we show here that disrupting the interactions between endothelial selectins and tumor cell selectin ligands diminished tumor cell recruitment to the liver. Furthermore, systemic inflammation induced by intravenous injection of lipopolysaccharide significantly enhanced the metastatic potential of these lung carcinoma cells by increasing their propensity to adhere to the liver sinusoidal endothelium. Confocal microscopy revealed frequent colocalization of cancer cells with neutrophils and neutrophil depletion in vivo significantly attenuated the lipopolysaccharide-induced increase in H-59 cell adhesion. Although direct selectin-selectin ligand interactions contributed significantly to tumor cell adhesion to sinusoidal endothelial cells, we show here that in addition, interactions between adherent neutrophils within the inflamed sinusoids and circulating tumor cells may further increase tumor cell arrest in 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) | 0.002 | 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".