Collagen-Based Matrices Improve the Delivery of Transplanted Circulating Progenitor Cells
Bibliographic record
Abstract
BACKGROUND: Collagen delivery matrices have been reported to improve the results of cell therapy, but knowledge of their mechanisms of action is limited. To evaluate whether a collagen matrix improves early engraftment posttransplantation, 2-[(18)F]fluoro-2-deoxy-d-glucose ((18)F-FDG) was used to label transplanted circulating progenitor cells (CPCs) and track them in vivo with positron-emission tomography. METHODS AND RESULTS: Efficiency of (18)F-FDG cell labeling was CPC-concentration dependent (r=0.61, P<0.001) but not (18)F-FDG-dose dependent. Labeled human CPCs (2x10(6)) were injected with or without a collagen-based matrix in the ischemic hind limb of rats (n=12 per group) 2 weeks after femoral artery ligation. Imaging of labeled cells, acquired by small animal positron-emission tomography at 150 minutes postinjection, revealed greater CPC retention in the ischemic hind limb and less nonspecific leakage to other tissues (retention ratio, 0.44+/-0.08) when CPCs were delivered within the matrix, compared with cells injected alone (0.22+/-0.13, P=0.040) and with (18)F-FDG injected with or without the matrix (0.10+/-0.05 and 0.11+/-0.05, respectively, P<0.005). Tissue radionuclide biodistribution was performed after completion of positron-emission tomography imaging. When (18)F-FDG-labeled cells were injected with the collagen matrix, accumulation was significantly increased (by 69.6%, P=0.021) in the target ischemic hind limb muscle and significantly reduced (by 14.8% to 31.4%, P<0.05) in nonspecific tissues, compared with cells injected alone. Histology confirmed the increased retention in target tissue associated with the matrix. CONCLUSIONS: Early posttransplantation, a collagen matrix enhances progenitor cell retention and limits distribution to nonspecific tissues, as measured by the use of (18)F-FDG labeled cells and positron-emission tomography imaging and confirmed by biodistribution and histology.
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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) | 0.001 | 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".