Interleukin-2 Enhances Angiogenesis and Preserves Cardiac Function Following Myocardial infarction
Bibliographic record
Abstract
Abstract Abstract 2786 Previous studies demonstrated that the addition of some cytokines improved cardiac recovery in experimental models of myocardial infarction (MI). The aim of the study is to test the hypothesis that pharmacological stimulation of lymphoid cells using recombinant human interleukin (rhIL)-2 improves angiogenesis and preserves heart function after MI. A single IV injection of rhIL-2 two days following MI improved by 27.7% the left ventricular (LV) fractional shortening of immune competent (C57Bl6) mice, but had no effect on cardiac function of immune-deficient (NOD SCID IL2Rγnull) mice. Immunohistochemical analysis of C57Bl6 cross sections of heart revealed that collagen deposition was reduced by 23.1% and that capillary density was enhanced in the scar area and the border zone of the infarct respectively by 22.4% and 33.6% following rhIL-2 injection. In addition, rhIL-2 enhanced 1.6-fold the in vivo endothelial cell proliferation index and 1.8-fold the number of NK cell infiltrating the infarcted heart, but had no effect on the number of cardiac CD4 and CD8 cells. In vitro, rhIL-2 activated NK cells enhanced cardiac endothelial cell proliferation by 17.2%. Adoptive transfer of IL-2-activated NK cells enhanced cardiac angiogenesis. Here we show that a single IV injection of rhIL-2 positively impacted cardiac function by improving angiogenesis through a process involving NK cells. Disclosure: No relevant conflicts of interest to declare.
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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) | 0.001 | 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".