Tissue-Engineered Injectable Collagen-Based Matrices for Improved Cell Delivery and Vascularization of Ischemic Tissue Using CD133 <sup>+</sup> Progenitors Expanded From the Peripheral Blood
Bibliographic record
Abstract
BACKGROUND: The use of stem and/or progenitor cells to achieve potent vasculogenesis in humans has been hindered by low cell numbers, implant capacity, and survival. This study investigated the expansion of CD133+ cells and the use of an injectable collagen-based tissue engineered matrix to support cell delivery and implantation within target ischemic tissue. METHODS AND RESULTS: Adult human CD133+ progenitor cells from the peripheral blood were generated and expanded by successive removal and culture of CD133- cell fractions, and delivered within an injectable collagen-based matrix into the ischemic hindlimb of athymic rats. Controls received injections of phosphate-buffered saline, matrix, or CD133+ cells alone. Immunohistochemistry of hindlimb muscle 2 weeks after treatment revealed that the number of CD133+ cells retained within the target site was >2-fold greater when delivered by matrix than when delivered alone (P<0.01). The transplanted CD133+ cells incorporated into vascular structures, and the matrix itself also was vascularized. Rats that received matrix and CD133+ cells demonstrated greater intramuscular arteriole and capillary density than other treatment groups (P<0.05 and P<0.01, respectively). CONCLUSIONS: Compared with other experimental approaches, treatment of ischemic muscle tissue with generated CD133+ progenitor cells delivered in an injectable collagen-based matrix significantly improved the restoration of a vascular network. This work demonstrates a novel approach for the expansion and delivery of blood CD133+ cells with resultant improvement of their implantation and vasculogenic capacity.
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 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".