Angiogenic CD34 Stem Cell Therapy in Coronary Microvascular Repair—A Systematic Review
Bibliographic record
Abstract
Ischemia with non-obstructive coronary arteries (INOCA) is an increasingly recognized disease, with a prevalence of 3 to 4 million individuals, and is associated with a higher risk of morbidity, mortality, and a worse quality of life. Persistent angina in many patients with INOCA is due to coronary microvascular dysfunction (CMD), which can be difficult to diagnose and treat. A coronary flow reserve <2.5 is used to diagnose endothelial-independent CMD. Antianginal treatments are often ineffective in endothelial-independent CMD and thus novel treatment modalities are currently being studied for safety and efficacy. CD34+ cell therapy is a promising treatment option for these patients, as it has been shown to promote vascular repair and enhance angiogenesis in the microvasculature. The resulting restoration of the microcirculation improves myocardial tissue perfusion, resulting in the recovery of coronary microvascular function, as evidenced by an improvement in coronary flow reserve. A pilot study in INOCA patients with endothelial-independent CMD and persistent angina, treated with autologous intracoronary CD34+ stem cells, demonstrated a significant improvement in coronary flow reserve, angina frequency, Canadian Cardiovascular Society class, and quality of life (ESCaPE-CMD, NCT03508609). This work is being further evaluated in the ongoing FREEDOM (NCT04614467) placebo-controlled trial.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".