Understanding high endothelial venules: Lessons for cancer immunology
Bibliographic record
Abstract
High endothelial venules (HEVs) are blood vessels especially adapted for lymphocyte trafficking which are normally found in secondary lymphoid organs such as lymph nodes (LN) and Peyer's patches. It has long been known that HEVs develop in non-lymphoid organs during chronic inflammation driven by autoimmunity, infection or allografts. More recently, HEVs have been observed in solid, vascularized tumors and their presence correlated with reduced tumor size and improved patient outcome. It is proposed that newly formed HEV promote antitumor immunity by recruiting naive lymphocytes into the tumor, thus allowing the local generation of cancerous tissue-destroying lymphocytes. Understanding how HEVs develop and function are therefore important to unravel their role in human cancers. In LN, HEVs develop during embryonic and early post-natal life and are actively maintained by the LN microenvironment. Systemic blockade of lymphotoxin-β receptor leads to HEV de-differentiation, but the LN components that induce HEV differentiation have remained elusive. Recent elegant studies using gene-targeted mice have demonstrated clearly that triggering the lymphotoxin-β receptor in endothelial cells (EC) induces the differentiation of HEV and that CD11c+ dendritic cells play a crucial role in this process. It will be important to determine whether lymphotoxin-β receptor-dependent signaling in EC drives the development of HEV during tumorigenesis and which cells have HEV-inducer properties. This may reveal therapeutic approaches to promote HEV neogenesis and determine the impact of newly formed HEV on tumor immunity.
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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 | 0.001 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".