Ex Vivo Akt/HO-1 Gene Therapy to Human Endothelial Progenitor Cells Enhances Myocardial Infarction Recovery
Bibliographic record
Abstract
The aim of this study was to evaluate the overexpression of genes central to cell survival and angiogenesis to enhance the function of human late outgrowth endothelial progenitor cells (EPCs) and their utility for infarct recovery. Ischemic myocardial injury creates a hostile microenvironment, which is characterized by hypoxia, oxidative stress, and inflammation. The infarct microenvironment prevents adhesion, survival, and integration of cell transplants that promote neovascularization. EPCs are dysfunctional as a result of risk factors in cardiovascular patients. Protein kinase B (Akt) and heme-oxygenase-1 (HO-1) are intracellular proteins that play an important role in angiogenesis and cell survival. Late outgrowth EPCs transduced ex vivo with Akt and HO-1 demonstrate improved adhesion to extracellular matrix, improved migration toward human cardiomyocytes, and an improved paracrine profile under stress. Enhanced late outgrowth EPCs reduce the tumor necrosis factor-α (TNF-α) burden both in vitro and in vivo, attenuating nuclear factor-κB (NF-κB) activity and promoting cell survival. Akt and HO-1 enhance late outgrowth EPC neovascularization, resulting in improved cardiac performance and reduced negative remodeling after myocardial infarction in nude mice. Alteration of the infarct microenvironment through gene modification of human late outgrowth EPCs enhances the function and integration of transplanted cells for restoration of cardiac function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".