A Systematic Review of CD34+ Stem Cell Therapy as an Innovative and Efficient Treatment for the Management of Refractory Angina
Bibliographic record
Abstract
Despite optimal medical treatment, many individuals suffering from severe coronary artery disease are not suitable candidates for further revascularization. Therapeutic angiogenesis has attracted continuous interest to increase myocardial perfusion. Cell therapy using autologous stem cells expressing Cluster of Differentiation 34 plus (CD34+) offers a special therapeutic choice for individuals with refractory angina, seeing as CD34+ stem cells can restore microcirculation. We searched PubMed, PubMed Central (PMC), and Google Scholar to find the relevant articles to write this systematic review about the role of CD34+ stem cell therapy in the management of refractory angina. Additionally, we provided a brief explanation of CD34+ cells and their mechanism of action. Along with the positive finding of other trials, a recent open-label, single-center intracoronary CD34+ cell therapy for the treatment of coronary endothelial dysfunction in patients with angina and nonobstructive coronary arteries (IMPROvE-CED) clinical trial published in 2022 concluded improvement in coronary blood flow, a significant reduction in daily as-needed sublingual nitroglycerin use and improvement in Canadian Cardiovascular Society (CCS) angina class were observed after autologous CD34+ cell treatment. In conclusion, refractory angina management and overall prognosis may be revolutionized once this treatment is approved.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".