Effect of mouse VEGF<sub>164</sub> on the viability of hydroxyethyl methacrylate–methyl methacrylate‐microencapsulated cells <i>in vivo</i>: Bioluminescence imaging
Bibliographic record
Abstract
Bioluminescent imaging was used to track the viability of luciferase transfected L929 cells in poly(hydroxyethyl methacrylate-co-methyl methacrylate) (HEMA-MMA) microcapsules. Bioluminescence, as determined by Xenogen imaging after addition of luciferin to microcapsules in vitro, increased with time, consistent with an increase in cell number. Capsules were suspended in Matrigel and injected subcutaneously. The bioluminesence in vivo increased over the first 3 weeks and then decreased, both with and without the delivery of mVEGF(164) (1.2 ng/24 h/200 microcapsules in vitro); VEGF delivery was from microencapsulated doubly transfected cells (both luciferase and mVEGF(164)). VEGF delivery was sufficient to generate a greater number of vascular structures, but this did not result in the expected increase in microencapsulated cell viability. Interestingly, the number of vessels at day 28 was less than at day 21, consistent with what would be an expected reduction in VEGF secretion when cell viability is lost. The results presented here do not support the hypothesis that transfection of microencapsulated cells with VEGF is sufficient to correct the oxygen transport limitation, at least with this type of tissue engineering construct. On the other hand, bioluminescent imaging proved to be a useful method of monitoring microencapsulated cell viability over many weeks in vivo.
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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) | 0.001 | 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 | 0.001 |
| 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".