Concise Review: Cell Therapy for Critical Limb Ischemia: An Integrated Review of Preclinical and Clinical Studies
Bibliographic record
Abstract
Critical limb ischemia (CLI), the most severe form of peripheral artery disease, is characterized by pain at rest and non-healing ulcers in the lower extremities. For patients with CLI, where the extent of atherosclerotic artery occlusion is too severe for surgical bypass or percutaneous interventions, limb amputation remains the only treatment option. Thus, cell-based therapy to restore perfusion and promote wound healing in patients with CLI is under intense investigation. Despite promising preclinical studies in animal models, transplantation of bone marrow (BM)-derived cell populations in patients with CLI has shown limited benefit preventing limb amputation. Early trials injected heterogenous mononuclear cells containing a low frequency of cells with pro-vascular regenerative functions. Most trials transferred autologous cells damaged by chronic disease that demonstrated poor survival in the ischemic environment and impaired function conferred by atherosclerotic or diabetic co-morbidities. Finally, recent preclinical studies suggest optimized blood vessel formation may require paracrine and/or structural contributions from multiple progenitor cell lineages, angiocrine-secretory myeloid cells derived from hematopoietic progenitor cells, tubule-forming endothelial cells generated by circulating or vessel-resident endothelial precursors, and vessel-stabilizing perivascular cells derived from mesenchymal stem cells. Understanding how stem cells co-ordinate the myriad of cells and signals required for stable revascularization remains the key to translating the potential of stem cells into curative therapies for CLI. Thus, combination delivery of multiple cell types within supportive bioengineered matricies may represent a new direction to improve cell therapy strategies for CLI. Stem Cells 2018;36:161-171.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".