Vascularization and Improved <i>In Vivo</i> Survival of VEGF-Secreting Cells Microencapsulated in HEMA-MMA
Bibliographic record
Abstract
Vascularization caused by encapsulated cells engineered to secrete vascular endothelial growth factor (VEGF) improved the in vivo survival of the encapsulated cells in a syngeneic mouse Matrigel plug model. Murine fibroblast cells (L929) were engineered to secrete recombinant human vascular endothelial growth factor (rhVEGF(165)). Transfected and nontransfected L929 cells were microencapsulated in a 75:25 hydroxyethyl methacrylate-methyl methacrylate (HEMA-MMA) copolymer. Capsules containing transfected cells induced vascularization in vivo at 1 and 3 weeks postimplantation. In histological sections, a significant positive correlation was seen between the number of capsules and blood vessel density for VEGF-secreting cell capsule implants. New vessels, many positively stained for smooth muscle cells and pericytes, were seen surrounding these VEGF-secreting cell capsule explants. Few vessels were seen in nontransfected L929 capsule implants. The viability of transfected and nontransfected encapsulated cells was assessed on explantation. Although the viability of all encapsulated cells decreased at both 1 and 3 weeks, encapsulated VEGF-secreting cells retained more of the viability than did encapsulated nontransfected control cells. Genetically modified cells promoted vascularization in this context and appeared to enhance the viability of the encapsulated cells, although the extent of the functional benefit was less than expected. Additional effort is required to enhance the benefit, to quantify it, and to understand further the host response to HEMA-MMA microencapsulated cells and tissue constructs, more generally.
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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".