Induction of Cardiac Angiogenesis Requires Killer Cell Lectin-Like Receptor 1 and α4β7 Integrin Expression by NK Cells
Bibliographic record
Abstract
Recent findings indicate that NK cells are involved in cardiac repair following myocardial infarction. The aim of this study is to investigate the role NK cells in infarct angiogenesis and cardiac remodeling. In normal C57BL/6 mice, myelomonocytic inflammatory cells invaded infarcted heart within 24 h followed by a lymphoid/NK cell infiltrate by day 6, accompanied by substantial expression of IL-2, TNF-α, and CCL2. In contrast, NOD SCID mice had virtually no lymphoid cells infiltrating the heart and did not upregulate IL-2 levels. In vitro and in vivo, IL-2-activated NK cells promoted TNF-α-stimulated endothelial cell proliferation, enhanced angiogenesis and reduced fibrosis within the infarcted myocardium. Adoptive transfer of IL-2-activated NK cells to NOD SCID mice improved post-myocardial infarction angiogenesis. RNA silencing technology and neutralizing Abs demonstrated that this process involved α4β7 integrin/VCAM-1 and killer cell lectin-like receptor 1/N-cadherin-specific binding. In this study, we show that IL-2-activated NK cells reduce myocardial collagen deposition along with an increase in neovascularization following acute cardiac ischemia through specific interaction with endothelial cells. These data define a potential role of activated NK cells in cardiac angiogenesis and open new perspectives for the treatment of ischemic diseases.
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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) | 0.001 | 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".