A micro-fragmented collagen gel as a cell-assembling platform for critical limb ischemia repair
Bibliographic record
Abstract
Critical limb ischemia (CLI) is a devastating disease characterized by the progressive blockage of blood vessels. Although the paracrine effect of growth factors in stem cell therapy made it a promising angiogenic therapy for CLI, poor cell survival in the harsh ischemic microenvironment limited its efficacy. Thus, an imperative need exists for a stem-cell delivery method that enhances cell survival. Here, a collagen microgel (CMG) cell-delivery scaffold (40 × 20 μm) was fabricated via micro-fragmentation from collagen-hyaluronic acid polyionic complex to improve transplantation efficiency. Culturing human adipose-derived stem cells (hASCs) with CMG enabled integrin receptors to interact with CMG to form injectable 3-dimensional constructs (CMG-hASCs) with a microporous microarchitecture and enhanced mass transfer. CMG-hASCs exhibited higher cell survival (p < 0.0001) and angiogenic potential in tube formation and aortic ring angiogenesis assays than cell aggregates. Injection of CMG-hASCs intramuscularly into CLI mice increased blood perfusion and limb salvage ratios by 40 % and 60 %, respectively, compared to cell aggregate-treated mice. Further immunofluorescent analysis revealed that transplanted CMG-hASCs have greater muscle regenerative and angiogenic potential, with enhanced cell survival than cell aggregates (p < 0.05). Collectively, we propose CMG as a cell-assembling platform and CMG-hASCs as promising therapeutics to treat CLI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".