Trimodal rescue of hind limb ischemia with growth factors, cells, and nanocarriers: fundamentals to clinical trials
Bibliographic record
Abstract
Peripheral artery disease is a severe medical condition commonly characterized by critical or acute limb ischemia. Gradual accumulation of thrombotic plaques in peripheral arteries of the lower limb may lead to intermittent claudication or ischemia in muscle tissue. Ischemic muscle tissue with lesions may become infected, resulting in a non-healing wound. Stable progression of the non-healing wound associated with severe ischemia might lead to functional deterioration of the limb, which, depending on the severity, can result in amputation. Immediate rescue of ischemic muscles through revascularization strategies is considered the gold standard to treat critical limb ischemia. Growth factors offer multiple levels of protection in revascularization of ischemic tissue. In this review, the basic mechanism through which growth factors exert their beneficial properties to rescue the ischemic limb is extensively discussed. Moreover, clinical trials based on growth factor and stem cell therapy to treat critical limb ischemia are considered. The clinical utility of stem cell therapy for the treatment of limb ischemia is explained and recent advances in nanocarrier technology for selective growth factor and stem cell supplementation are summarized.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".