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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 teacher head, 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".