Genetically Modified Endothelial Progenitor Cells in the Therapy of Cardiovascular Disease and Pulmonary Hypertension
Bibliographic record
Abstract
Since their initial discovery, endothelial progenitor cells (EPCs) have held tremendous promise for cell therapy for a variety of cardiovascular diseases including pulmonary hypertension. The clinical experience to date suggests that circulating or bone marrow mononuclear cells and EPCs can induce neovascularization, and enhance cardiac repair after myocardial function, as well as improvements in the hemodynamic and functional status of patients with idiopathic pulmonary arterial hypertension. Although these results are promising, the overall magnitude of the clinical benefits seen in these trials appear to be rather modest. Indeed, strong experimental evidence points towards a reduction in mobilization and impairment in function of EPCs in preclinical models and patients with cardiac disease or with cardiovascular risk factors such as advanced age, type I and II diabetes, hypercholesterolemia, coronary artery disease, as well as other conditions such as pulmonary hypertension. Genetic engineering of EPCs ex vivo, prior to transplantation, is a promising cell-enhancement strategy for restoring the angiogenic potential of autologous, patient-derived cells. This review provides an update of the experimental studies that have used gene-modified EPC therapy to treat ischemic cardiovascular disease and pulmonary hypertension.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".