Allogeneic endometrial regenerative cells: An "Off the shelf solution" for critical limb ischemia?
Bibliographic record
Abstract
Critical limb ischemia (CLI) is an advanced form of peripheral artery disease which is responsible for approximately 100,000 amputations per year in the US. Trials to date have reported clinical improvement and reduced need for amputation in CLI patients receiving autologous bone marrow or mobilized peripheral blood stem cells for stimulation of angiogenesis. While such treatments are currently entering Phase III trials, practical and scientific pitfalls will limit widespread implementation if efficacy is proven. Hurdles to be overcome include: a) reduced angiogenic potential of autologous cells in aged patients with cardiovascular risk factors; b) invasiveness/adverse effects of bone marrow extraction and G-CSF mobilization, respectively; and c) need for on-site cellular manipulation. The Endometrial Regenerative Cell (ERC) is a mesenchymal-like stem cell derived from the menstrual blood that is believed to be associated with endometrial angiogenesis. We discuss the possibility of using allogeneic ERCs as an "off the shelf" treatment for CLI based on the following properties: a) High levels of growth factors and matrix metalloprotease production; b) Ability to inhibits inflammatory responses and lack of immunogenicity; and c) Expandability to great quantities without loss of differentiation ability or karyotypic abnormalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".