Human endothelial colony-forming cells in regenerative therapy: A systematic review of controlled preclinical animal studies
Bibliographic record
Abstract
Endothelial colony-forming cells (ECFCs) hold significant promise as candidates for regenerative therapy of vascular injury. Existing studies remain largely preclinical and exhibit marked design heterogeneity. A systematic review of controlled preclinical trials of human ECFCs is needed to guide future study design and to accelerate clinical translation. A systematic search of Medline and EMBASE on 1 April 2019 returned 3131 unique entries of which 66 fulfilled the inclusion criteria. Most studies used ECFCs derived from umbilical cord or adult peripheral blood. Studies used genetically modified immunodeficient mice (n = 52) and/or rats (n = 16). ECFC phenotypes were inconsistently characterized. While >90% of studies used CD31+ and CD45-, CD14- was demonstrated in 73% of studies, CD146+ in 42%, and CD10+ in 35%. Most disease models invoked ischemia. Peripheral vascular ischemia (n = 29), central nervous system ischemia (n = 14), connective tissue injury (n = 10), and cardiovascular ischemia and reperfusion injury (n = 7) were studied most commonly. Studies showed predominantly positive results; only 13 studies reported ≥1 outcome with null results, three reported only null results, and one reported harm. Quality assessment with SYRCLE revealed potential sources of bias in most studies. Preclinical ECFC studies are associated with benefit across several ischemic conditions in animal models, although combining results is limited by marked heterogeneity in study design. In particular, characterization of ECFCs varied and aspects of reporting introduced risk of bias in most studies. More studies with greater focus on standardized cell characterization and consistency of the disease model are needed.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".