Immunological Regulation of Vascular Inflammation During Cancer Metastasis
Bibliographic record
Abstract
Metastasis is the predominant cause of cancer-related mortality, despite being a highly inefficient process overall. The vasculature is the gatekeeper for tumor cell seeding within the secondary tissue microenvironment-the rate limiting step of the metastatic cascade. Therefore, factors that regulate vascular physiology dramatically influence cancer outcomes. There are a myriad of physiologic circumstances that not only influence the intrinsic capacity of tumor cells to cross the endothelial barrier, but also that regulate vascular inflammation and barrier integrity to enable extravasation into the metastatic niche. These processes are highly dependent on inflammatory cues largely initiated by the innate immune compartment, that are meant to help re-establish tissue homeostasis, but instead become hijacked by cancer cells. Here, we discuss the scientific advances in understanding the interactions between innate immune cells and the endothelium, describe their influence on cancer metastasis, and evaluate potential therapeutic interventions for the alleviation of metastatic disease. By triangulating the relationship between immune cells, endothelial cells, and tumor cells, we will gain greater insight into how to impede the metastatic process by focusing on its most vulnerable phases, thereby reducing metastatic spread and cancer-related mortality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".